8 Chapter 8. Classification: Assigning Resources to Categories

Robert J. Glushko

Jess Hemerly

Vivien Petras

Michael Manoochehri

Longhao Wang

Jordan Shedlock

Daniel Griffin

Table of Contents

8.1. Introduction

8.1.1. Classification vs. Categorization

8.1.2. Classification vs. Tagging

8.1.3. Classification vs. Physical Arrangement

8.1.4. Classification Schemes

8.1.5. Classification and Standardization

8.2. Understanding Classification

8.2.1. Classification Is Purposeful

8.2.2. Classification Is Principled

8.2.3. Classification Is Biased

8.3. Bibliographic Classification

8.3.1. The Dewey Decimal Classification

8.3.2. The Library of Congress Classification

8.3.3. The BISAC Classification

8.4. Faceted Classification

8.4.1. Foundations for Faceted Classification

8.4.2. Faceted Classification in Description

8.4.3. A Classification for Facets

8.4.4. Designing a Faceted Classification System

8.5. Classification by Activity Structure

8.6. Computational Classification

8.7. Key Points in Chapter Eight


In Chapter 6, Describing Relationships and Structures we discussed different types of semantic relationships and contrasted abstract relationships between categories that define a semantic hierarchy like

        Meat  is-a  Food

with concrete relationships involving specific people like members of the Simpson family:

        Homer Simpson  is-a  Husband

When we make an assertion that a particular instance like Homer Simpson is a member of class, we are classifying the instance.

Classification, the systematic assignment of resources to intentional categories, is the focus of this chapter. In

Chapter 7, Categorization: Describing Resource Classes and Types, we described categories created by people as cognitive and linguistic models for applying prior knowledge and we discussed a set of principles for creating categories and category systems. We explained how cultural categories serve as the foundations upon which individual and institutional categories are based. Institutional categories are most often created in abstract and information-intensive domains where unambiguous and precise categories enable classification to be purposeful and principled. Computational categories inherited by supervised learning techniques are usually as interpretable as those created by people, but categories created by unsupervised machine learning techniques are statistical patterns that might or might not be interpretable.

A system of categories and its attendant rules or access methods is typically called a classification scheme or just the classifications. A system of categories captures the distinctions and relationships among its resources that are most important in a domain and for a particular context of use, creating a reference model or conceptual roadmap for its users. This classification creates the structure and support for the interactions that human or computational agents perform. For example, research libraries and bookstores do not use the same classifications to organize books, but the categories they each use are appropriate for their contrasting types of collections and the different kinds of browsing and searching activities that take place in each context. Likewise, the scientific classifications for animals used by biologists contrast with those used in pet stores because the latter have no need for the precise differentiation enabled by the former.

Navigating This Chapter

Most of the chapter is a survey of topics that span the broad range of how classifications are used in organizing systems. These include enumerative classification (the section called “Bibliographic Classification”), faceted classification (the section called “Faceted Classification”), activity-based classification (the section called “Classification by Activity Structure”), and computational classification (the section called “Computational Classification”). Because classification and standardization are closely related, we also analyze standards and standards making as they apply to organizing systems. Throughout, we observe how personal, institutional, cultural, linguistic, political, religious, and even artistic biases can affect otherwise principled and purposeful classification schemes. We finish the chapter with the section called “Key Points in Chapter Eight”.

Classification vs. Categorization

Classification requires a system of categories, so not everyone distinguishes classification from categorization. Batley, for example, says classification is “imposing some sort of structure on our understanding of our environment,” a vague definition that applies equally well to categorization.[453]

In the discipline of organizing, the definition of classification is narrower and more formal. The contrasts among cultural, individual, and institutional categories in the section called “The What and Why of Categories” yield a precise definition of classification: The systematic assignment of resources to a system of intentional categories, often institutional ones. This definition highlights the intentionality behind the system of categories, the systematic processes for using them, and implies the greater requirements for governance and maintenance that are absent for cultural categories and most individual ones.

Classification vs. Tagging

Precise and reliable classification is possible when the shared properties of a collection of resources are used in a principled and systematic manner. This method of classification is essential to satisfy institutional and commercial purposes. However, this degree of rigor might be excessive for personal classifications and for classifications of resources in social or informal contexts.

Instead, a weaker approach to organizing resources is to use any property of a resource and any vocabulary to describe it, regardless of how well it differentiates it from other resources to create a system of categories. This method of organizing resources is most often called tagging (the section called “Tagging of Web-based Resources”), but it has also been called social classification.[454]

Tagging is often used in personal organizing systems, but is social when it serves goals to convey information, develop a community, or manage reputation. Regardless of its name, however, tagging is popular for organizing and rating photos, websites, email messages, or other web-based resources or web-based descriptions of physical resources like stores and restaurants.

The distinction between classification and tagging was blurred when Thomas Vander Wal coined the term “folksonomy” combining “folk” and “taxonomy” (which is a classification; see the section called “Inclusion”) to describe the collection of tags for a particular web site or application.[455] Folksonomies are often displayed in the form of a tag cloud, where the frequency with which the tag is used throughout the site determines the size of the text in the tag cloud. The tag cloud emerges through the bottom-up aggregation of user tags and is a statistical construct, rather than a semantic one.[456]

Tagging seems insufficiently principled to be considered classification. Tagging a photo as “red” or “car” is an act of resource description, not classification, because the other tags that would serve as the alternative classifications are unspecified. Furthermore, when tagging principles are followed at all, they are likely to be idiosyncratic ones that were not pre-determined or arrived at through an analysis of goals and requirements.

Noticeably, some uses of tags treat them as category labels, turning tagging into classification. Many websites and resources encourage users to assign “Like” or “+1” tags to them, and because these tags are pre-defined, they are category choices in an implied classification system; for example, we can consider “Like” as an alternative to a “Not liked enough” category.

When users or communities establish sets of principles to govern their tagging practices, tagging is even more like classification. Such a tagging system can be called a tagsonomy, a neologism we have invented to describe more systematic tagging. For example, a tagsonomy could predetermine tags as categories to be assigned to particular contents of a blog post, or specify the level of abstraction and granularity for assigning tags without predetermining them (the section called “Category Design Issues and Implications”). Some people use multiple user accounts for the same application to establish distinct personas or contexts (e.g., personal vs. business photo collections) as a way to make their tagsonomies more distinct.

Making these decisions about tagging content and form and applying them in the tagging process transforms an ad hoc set of tags into a principled tagsonomy. When tagging is introduced in a business setting, more pragmatic purposes and more systematic taggingfor example, by using tags from lists of departments or productsalso tends to create tagsonomic classification.[457]

“Tagging documents by computer,” or multi-label classification, is a glib way to describe topic modeling, an unsupervised learning technique for organizing and summarizing collections of unstructured documents by discovering patterns or clusters in the words they contain. The basic intuition behind topic modeling is that the words in a document are probabilistic indications of what the document is about; a document that contains words like “election, government, and candidate” is probably about the “politics” topic, while words like “adore, wedding, and marriage” are good indications of a “love” topic. Topic models are not quite tagging because the words they identify to describe documents are not atomic tags or labels explicitly assigned to individual documents. Instead, topics are more like themes that different documents are more or less likely to contain.

Topic models have been used to implement user interfaces for browsing large document collections because they let a user explore using themes instead of specific search terms. In digital humanities, topic models have been used to discover changes in “what’s written about” by some author or resource (like a newspaper) over time. Web commerce companies use topic models to organize books or products for their recommendation engines.[458]

Classification vs. Physical Arrangement

We have often stressed the principle in the discipline of organizing that logical issues must be separated from implementation issues. (See the section called “The Concept of “Organizing Principle”, the section called “Designing the Description Form”, and the section called “The Implementation Perspective ”) With classification we separate the conceptual act of assigning a resource to a category from the subsequent but often incidental act of putting it in some physical or digital storage location. This focus on the logical essence of classification is elegantly expressed in a definition by Gruenberg: Classification is “a higher order thinking skill requiring the fusion of the naturalist’s eye for relationships… with the logician’s desire for structured order… the mathematician’s compulsion to achieve consistent, predictable results… and the linguist’s interest in explicit and tacit expressions of meaning.”[459]

Taking a conceptual or cognitive perspective on classification contrasts with much conventional usage in library science, where classification is mostly associated with arranging tangible items on shelves, emphasizing the “parking” function that realizes the “marking” function of identifying the category to which the resource belongs.[460]

From a library science or collection curation perspective, it seems undeniable that when the resources being classified are physical or tangible things such as books, paintings, animals, or cooking pots, the end result of the classification activity is that some resource has been placed in some physical location. Moreover, the placement of physical resources can be influenced by the physical context in which they are organized. Once placed, the physical context often embodies some aspects of the organization when similar or related resources are arranged in nearby locations. In libraries and bookstores, this adjacency facilitates the serendipitous discovery of resources, as anyone well knows who has found an interesting book by browsing the shelves.

It might seem natural to identify storage locations with the classes used by the classification system. Just as we might think of a location in the zoo as the “lion habitat,” we can put a “QC” sign on a particular row of shelves in a library where books about physics are arranged.

However, once we broaden the scope of organizing to include digital resources, it is clear that we rely on their logical classifications when we interact with them, not whether they reside on a computer in Berkeley or Bangalore. It is better to emphasize that a classification system is foremost a specification for the logical arrangement of resources because there are usually many possible and often arbitrary mappings of logical references to physical locations.

Classification Schemes

A classification scheme is a realization of one or more organizing principles. Physical resources are often classified according to their tangible or perceivable properties. As we discussed in the section called “Single Properties” and the section called “Multiple Properties”, when properties take on only a small set of discrete values, a classification system naturally emerges in which each category is defined by one property value or some particular combination of property values. Classification schemes in which all possible categories to which resources can be assigned are defined explicitly are enumerative. For example, the enumerative classification for a personal collection of music recorded on physical media might have categories for CDs, DVDs, vinyl albums, 8-track cartridges, reel-to-reel tape, and tape cassettes; every music resource fits into one and only one of these categories.

When multiple resource properties are considered in a fixed sequence, each property creates another level in the system of categories and the classification scheme is hierarchical or taxonomic. (See the section called “Inclusion”.)

For information resources, their aboutness is usually more important than their physical properties. For example, a professor planning a new course might organize candidate articles for the syllabus in a fixed set of categories, one for each potential lecture topic. But it is more challenging to enumerate all the subjects or topics that a larger collection of resources might be about. The Library of Congress Classification(LCC) is a hierarchical and enumerative scheme with a very detailed set of subject categories because books can be about almost anything. We discuss the LCC more in the section called “Bibliographic Classification”.

In addition to or instead of their aboutness, information resources are sometimes organized using intrinsic properties like author names or creation dates. Our professor might primarily organize his collection of articles by author name, and when he plans a new course, he might put those he selects for the syllabus into a classification system with one category for every scheduled lecture.

Because names and dates can take on a great many values, an organizing principle like alphabetical or chronological ordering is unlikely to enumerate in advance an explicit category for each possible value. Instead, we can consider these organizing principles as creating an implicit or latent classification system in which the categories are generated only as needed. For example, the Q category only exists in an alphabetical scheme if there is a resource whose name starts with Q.

Many resource domains have multiple properties that might be used to define a classification scheme. For example, wine can be classified by type of grape (varietal), color, flavor, price, winemaker, region of origin (appellation), blending style, and other properties. Furthermore, people differ in their knowledge or preferences about these properties; some people choose wine based on its price and varietal, while others studiously compare winemakers and appellations. Each order of considering the properties creates a different hierarchical classification, and using all of them would create a very deep and unwieldy system. Moreover, many different hierarchies might be required to satisfy divergent preferences. An alternative classification scheme for domains like these is faceted classification, a type of classification system that takes a set of resource properties and then generates only those categories for combinations that actually occur.

The most common types of facets are enumerative (mutually exclusive); Boolean (yes or no); hierarchical or taxonomic (logical containment); and spectrum (a range of numerical values). We discuss faceted classification in detail (in the section called “Faceted Classification”) because it is very frequently used in online classifications. Faceted schemes enable easier search and browsing of large resource collections like those for retail sites and museums than hierarchical enumerative schemes. In library science a classification system that builds categories by combination of facets is sometimes also called analytico-synthetic.

The Dewey Decimal Classification(DDC) is a highly enumerative classification system that also uses faceted properties; we will discuss it more in the section called “Bibliographic Classification”.

Classification and Standardization

Classifications impose order on resources. Standards do the same by making distinctions, either implicitly or explicitly, between “standard” and “nonstandard” ways of creating, organizing, and using resources. Classification and standardization are not identical, but they are closely related. Some classifications become standards, and some standards define new classifications. Institutional categories (the section called “Institutional Categories”) are of two broad types.

Institutional Taxonomies

Institutional taxonomies are classifications designed to make it more likely that people or computational agents will organize and interact with resources in the same way. Among the thousands of standards published by the International Organization for Standardization(ISO) are many institutional taxonomies that govern the classification of resources and products in agriculture, aviation, construction, energy, healthcare, information technology, transportation, and almost every industry sector.[461]

Institutional taxonomies are especially important in libraries and knowledge management. The Dewey Decimal Classification(DDC) and Library of Congress Classification(LCC) enable different libraries to arrange books in the same categories, and the Diagnostic and Statistical Manual of Mental Disorders(DSM) in clinical psychology enables different doctors to assign patients to the same diagnostic and insurance categories.[462]

Institutional Semantics

Systems of institutional semantics offer precisely defined abstractions or information components (the section called “Identity and Information Components”) needed to ensure that information can be efficiently exchanged and used. Organizing systems that use different information models often cannot share and combine information without tedious negotiation and excessive rework.

Automating transactions with suppliers and customers in a supply chain requires that all the parties use the same data format or formats that can be transformed to be interoperable. Retrofitting or replacing these applications to enable efficient interoperability is often possible, and it is usually desirable for the firm to develop or adopt enterprise standards for information exchange models rather than pay the recurring transaction costs to integrate or transform incompatible formats.

Standard semantics are especially important in industries or markets that have significant network effects where the value of a product depends on the number of interoperable or compatible productsthese include much of the information and service economies.

An example of a system of institutional semantics is the Universal Business Language(UBL) a library of about 2000 semantic “building blocks” for common concepts like “Address,” “Item,” “Payment,” and “Party” along with nearly 100 document types assembled from the standard components. UBL is widely used to facilitate the automated exchange of transactional documents in procurement, logistics, inventory management, collaborative planning and forecasting, and payment.[463]

Specifications vs. Standards

Implementing an organizing system of significant scope and complexity in a robust and maintainable fashion requires precise descriptions of the resources it contains, their formats, the classes, relations, structures and collections in which they participate, and the processes that ensure their efficient and effective use. Rigorous descriptions like these are often called “specifications” and there are well-established practices for developing good ones.

There is a subtle but critical distinction between “specifications” and “standards.” Any person, firm, or ad hoc group of people or firms can create a specification and then use it or attempt to get others to use it.[464] In contrast, a standard is a published specification that is developed and maintained by consensus of all the relevant stakeholders in some domain by following a defined and transparent process, usually under the auspices of a recognized standards organization.[465] In addition, implementations of standards often are subject to conformance tests that establish the completeness and accuracy of the implementation. This means that users can decide either to implement the specification themselves or choose from other conforming implementations.

The additional rigor and transparency when specifications are developed and maintained through a standards process often makes them fairer and gives them more legitimacy. Governments often require or recommend these de jure standards, especially those that are “open” or “royalty free” because they are typically supported by multiple vendors, minimizing the cost of adoption and maximizing their longevity.

For example, work on UBL has gone on for over a decade in a technical committee under the auspices of a standards development consortium called the Organization for the Advancement of Structured Information Standards(OASIS), which has developed scores of standards for web services and information-intensive industries.

Despite these important distinctions between “specifications” and “standards,” however, in conventional usage “standard” is often simply a synonym for “dominant or widely-adopted specification.” These de facto standards, in contrast with the de jure standards created by standards organizations, are typically created by the dominant firm or firms in an industry, by a new firm that is first to use a new technology or innovative method, or by a non-profit entity like a foundation that focuses on a particular domain.[466]

De facto standards and ad hoc standards often co-exist and compete in “standards wars,” especially in information-intensive domains and industries with rapid innovation. Standards “wars” tend to occur when different firms or groups of firms develop two or more standards that tend to address the same needs. Not surprisingly, the competing standards are often incompatible on purpose. At first this lets each standard attract customers with features not enabled by the other, but it ends up locking them in by imposing switching costs. Current examples include Google vs. Apple on mobile phones and Kindle versus Apple on ebook readers.[467]

For example, the Dewey Decimal Classification(DDC) is the world’s most widely used library classification system, and most people treat it as a standard. In fact, the DDC is proprietary and it is maintained and licensed for use by the Online Computer Library Center(OCLC). Similarly, the DSM is maintained and published by the American Psychiatric Association(APA) and it earns the APA many millions of dollars a year.

In contrast, de jure standards include the Library of Congress Classification(LCC), developed under the auspices of the US government, the familiar MARC record format used in online library catalogs (ISO 2709), and its American counterpart ANSI Z39.2.[468]

As a result, even though it would be technically correct to argue that “while all standards are specifications, not all specifications are standards,” this distinction is hard to maintain in practice.

Mandated Classifications

Standards are often imposed by governments to protect the interests of their citizens by coordinating or facilitating activities that might otherwise not be possible or safe. Some of them primarily concern public or product safety and are only tangentially relevant to systems for organizing information. Others are highly relevant, especially those that specify the formats and content of information exchange; many European governments require firms doing business with the government to adopt UBL.[469]

Other government standards that are important in organizing systems are those that express requirements for classification and retention of auditing information for financial activities, such as the Sarbanes-Oxley Act, or for non-retention of personal information, such as HIPAA and FERPA.[470]

Understanding Classification

Classifications arrange resources to support discovery, selection, combination, integration, analysis, and other purposeful activity in every organizing system. A classification of diseases facilitates diagnosis and development of medical procedures, as well as accounting and billing. In addition, classifications facilitate understanding of a domain by highlighting the important resources and relationships in it, supporting the training of people who work in the domain and their acquisition of specialized skills for it.

We consider classification to be systematic when it follows principles that govern the structure of categories and their relationships. However, being systematic and principled does not necessarily ensure that a classification will be unbiased or satisfy all users’ requirements. For example, the zoning, environmental, economic development, and political district classifications that overlay different parts of a city determine the present and future allocation of services and resources, and over time influence whether the city thrives or decays. These classifications reflect tradeoffs and negotiations among numerous participants, including businesses, lobbyists, incumbent politicians, donors to political parties, real estate developers, and others with strong self-interests.

Classification Is Purposeful

Categories often arise naturally, but by definition classifications do not because they are systems of categories that have been intentionally designed for some purpose. Every classification brings together resources that go together, and in doing so differentiates among them. However, bringing resources together would be pointless without reasons for finding, accessing, and interacting with them later.

Classifications Are Reference Models

A classification creates a semantic or conceptual roadmap to a domain by highlighting the properties and relationships that distinguish the resources in it. This reference model facilitates learning, comprehension, and use of organizing systems within the domain. Standard classifications like those used in libraries enable people to rely on one system that they can use to locate resources in many libraries. Standard business, job, and product classifications enable the reliable collection, analysis, and interchange of economic data and resources.

Another important use of standard classifications created by people is as a “gold standard” for comparison with unsupervised computational classifications carried out on the same collection of resources or in the same domain. Presumably no unsupervised classifier could exactly reproduce the classifications created by careful experts.

Classifications Support Interactions

A classification creates structure in the organizing system that increases the variety and capability of the interactions it can support. With physical resources, classification increases useful co-location; in kitchens, for example, keeping resources that are used together near each other (e.g., baking ingredients) makes cooking and cleanup more efficient (see “activity-based” classification in the section called “Classification by Activity Structure”).

Classification makes systems more usable when it is manifested in the arrangement of resource descriptions or controls in user interface components like list boxes, tabs, buttons, function menus, and structured lists of search results.[471]

A typical mapping between the logic of a classification scheme and a user interface is illustrated in Figure 8.1, “Classification and Interactions.”

Figure 8.1. Classification and Interactions.


Good user interface design creates a clear mapping between the logic of a classification scheme and the selection methods and arrangements presented to users. Categories that are mutually exclusive imply different tabs or other visualizations that imply a single selection, for example.


How a business classifies its product or service strongly influences whether a customer can find it; this is the essential task of marketing. The business of “search engine optimization” exists to help a firm with a web presence choose the categories and descriptive terms that will improve its ranking in search results and attract the number of types of customer it desires.[472] How a customer interacts with a supplier is influenced by how the supplier classifies its offerings in its shopping aisles or catalogs; the “science of shopping” uses creative classifications and co-location of goods to shape browsing behavior and encourage impulse buying.[473] In business-to-business contexts, standard classifications for business processes and their application interfaces enable firms to more easily build and maintain supply chains and distribution networks that interconnect many business partners.[474]

Classification In A Novel User Interface


The meat from animals used as food is classified into numerous “cuts” based on its origin. In the US, these classifications are standardized by the Department of Agriculture to ensure that meat is labeled correctly. The most natural way to convey the classification system is to label the parts of the animal in a diagram, because this binds each logical category to the “user interface.”

(Photo by R. Glushko. Taken in 2011 at the Union Square Greenmarket in New York City.)

Classification Is Principled

the section called “Principles for Creating Categories” explained principles for creating categories, including enumeration, single properties, multiple properties and hierarchy, probabilistic co-occurrence of properties, theory and goal-based categorization. It logically follows that the principles considered in designing categories are embodied in classifications that use those categories. However, when we say, “classification is principled,” we are going further to say that the processes of assigning resources to categories and maintaining the classification scheme over time must also follow principles.

The design and use of a classification system involves many choices about its purposes, scope, scale, intended lifetime, extensibility, and other considerations. Principled classification means that once those design choices are made they should be systematically and consistently followed.

Principled does not necessarily equate to “good,” because many of the choices can be arbitrary and others may involve tradeoffs that depend on the nature of the resources, the purposes of the classification, the amount of effort available, the complexity of the domain, and the capabilities of the people doing the classification and of the people using it (see the section called “Category Design Issues and Implications”). Every classification system is biased in one way or another (see the section called “Bibliographic Classification”).

Consider the classifications of resources in a highly-organized kitchen. (See the section called “Organizing a Kitchen”). Tableware, dishes, pots and pans, spices and food provisions, and other resources have dedicated locations determined by a set of intersecting requirements and organizing principles. There is no written specification, and other people organize their kitchens differently.

On the other hand, complex institutional classification systems like those used in libraries or government agencies are implemented with detailed specifications, methods, protocols, and guidelines. The people who apply these methods in the field have studied the protocols in school or they have received extensive on-the-job training to ensure that they apply them correctly, consistently, and in accordance with the specifications and guidelines.

Principles Embodied in the Classification Scheme

Some of the most important principles that lead us to say that classification is principled are those that guide the design of the classification scheme in the first place. These principles are fundamental in the discipline of library science but they apply more broadly to other domains.

The warrant principle concerns the justification for the choice of categories and the names given to them. The principle of literary warrant holds that a classification must be based only on the specific resources that are being classified. In the library context, this ad hoc principle that builds a classification from a particular collection principle is often posed in opposition to a more philosophical or epistemological perspective, first articulated by Francis Bacon in the seventeenth century, that a classification should be universal and must handle all knowledge and all possible resources.[475] The principle of scientific warrant argues that only the categories recognized by the scientists or experts in a domain should be used in a classification system, and it is often opposed by the principle of use or user warrant, which chooses categories and descriptive terms according to their frequency of use by everyone, not just experts.[476] With classifications of physical resources like those in a kitchen, we see object warrant, where similar objects are put together, but more frequently the justifying principle will be one of use warrant, where resources are organized based on how they are used.

Starbucks Coffee Sizes: “Anti-User” Warrant?

The Starbucks coffee chain seemingly goes out of its way to confuse its customers by calling the smallest (twelve ounces) of its three coffee sizes the “tall” size, calling its sixteen-ounce size a grande,” and calling its largest a venti,” which is Italian for twenty (ounces). Outside of Starbucks, something that is “tall” is never also considered “small.” Ironically, despite having more than five thousand coffeehouses in over fifty countries, Starbucks has none in Italy where venti would be in the local language.

A second principle embodied in a classification scheme concerns the breadth and depth of the category hierarchy. We discussed this in the section called “Category Design Issues and Implications” but in the context of classification this principle has additional implications and is framed as the extent to which the scheme is enumerative (the section called “Classification vs. Physical Arrangement”). The decision to classify broadly or precisely depends largely on the variety or heterogeneity of the resources that the system of categories has been designed to organize. Because of the diversity of resources for a sale in a department store, a broad classification is necessary to accommodate everything in the store. Kitchen goods will be grouped together in a few aisles on a single floor. But a specialty kitchen store or a wholesale kitchen supply store for restaurants would classify much more precisely because of the restricted resource domain and the greater expertise of those who want to buy things there. An entire section might be dedicated just to knives, organized by knife type, manufacturer, quality of steel, and other categories that are not used in the kitchen section of the department store.[477]

The precision or enumerativeness of a classification scheme increases the similarity of resources that are assigned to the same category and sharpens the distinctions between resources in different categories. However, when different classifications must be combined, mismatches in their precision or granularity can create challenges (see the section called “Reorganizing Resources for Interactions”).

Principles for Assigning Resources to Categories

The uniqueness principle means the categories in a classification scheme are mutually exclusive. Thus, when a logical concept is assigned to a particular category, it cannot simultaneously be assigned to another category. Resources, however, can be assigned to several categories if they embody several concepts represented by those different categories. This can present a challenge when a physical storage solution is based on storing resources according to its assigned category in a logical classification system. This is not a serious problem for resource types like technical equipment or tools, for which the properties used to classify them are highly salient, and that have very narrow and predictable contexts of use. It is also not a problem for highly-specialized information resources like scientific research reports or government economic data, which might end up in only one specialized class. However, many resources are inherently more difficult to classify because they have less salient properties or because they have many more possible uses.

We face this kind of problem all the time. For example, should we store a pair of scissors in the kitchen or in the office? One solution is to buy a second pair of scissors so that scissors can be kept in both locations where they are typically used, but this is not practical for many types of resources and this principle would be difficult to apply in a systematic manner.

Many books are about multiple subjects. A self-help book about coping with change in a business setting might reasonably be classified as either about applied psychology or about business. It is not helpful that book titles are often poor clues to their content; Who Moved My Cheese? is in fact a self-help book about coping with change in a business setting. Its Library of Congress Classification is BF 637, “Applied Psychology,” and at UC Berkeley it is kept in the business school library.

The general solution to satisfying the uniqueness principle in library classifications when resources do not clearly fit in a single category is to invent and follow a detailed set of often arbitrary rules. Usually, the primary subject of the book is used for assigning a category, which will then determine the book’s place on a shelf.

However, another rule might state that if a book treats two subjects equally, the subject that is covered first determines the classification. For some classifications a “table of preference” can trump other rules at the last minute. Not surprisingly, the rules for categorizing books take a long time to learn and are not always easy to apply.[478]

Principles for Maintaining the Classification over Time

Most personal classifications are created in response to a specific situation to solve an emerging organizational challenge. As a consequence, personal classification systems change in an ad hoc or opportunistic manner during their limited lifetimes. For example, the classification schemes in your kitchen or closet are deconstructed and disappear when you move and take your possessions to a different house or apartment. Your efforts to re-implement the classifications will be influenced by the configuration of shelves and cabinets in your new residence, so they will not be exactly the same.

In contrast, the institutional classification schemes for many library resources, culturally or scientifically-important artifacts, and much of the information created or collected by businesses, governments and researchers might have useful lives of decades or centuries. Classification systems like these can only be changed incrementally to avoid disruption of the work flows of the organization. We described maintaining resources as an activity in all organizing systems (the section called “Maintaining Resources”) and the issues of persistence, effectivity, authenticity, and provenance that emerge with resources over time (the section called “Resources over Time”). Much of this previous discussion applies in a straightforward manner to maintaining classifications over time.

However, some additional issues arise with classifications over time. The warrant principle (the section called “Principles Embodied in the Classification Scheme”) implicitly treats the justification for designing and naming categories as a one-time decision. This is reasonable if you are organizing a collection of bibliographic resources or common types of physical resources like printed books, clothing or butterflies. However, in domains where the resources are active, change their state or implementation, or otherwise have a probabilistic character it might be necessary to revisit warrant and the decisions based on it from time to time. Put another way, if the world that you are sampling from or describing has some randomness or change in it, the categories and descriptions you imposed on it probably need to change as well. It often happens that the meaning of an underlying category can change, along with its relative and absolute importance with respect to the other categories in the classification system. Categories sometimes change slowly, but they can also change quickly and radically as a result of technological, process, or geopolitical innovation or events. Entirely new types of resources and bodies of knowledge can appear in a short time. Consider what the categories of “travel,” “entertainment,” “computing,” and “communication” mean today compared to just a decade or two ago.

Changes in the meaning of the categories in a classification threaten its integrity, the principle that categories should not move within the structure of the classification system.[479] One way to maintain integrity while adapting to the dynamic and changing nature of knowledge is to define a new version of a classification system while allowing earlier ones to persist, which preserves resource assignments in the previous version of the classification system while allowing it to change in the new one. If we adopt a logical perspective on classification (the section called “Classification vs. Tagging”) that dissociates the conceptual assignment of resources to categories from their physical arrangement, there is no reason why a resource cannot have contrasting category assignments in different versions of a classification.

However, the conventional library with collections of physical resources cannot easily abandon its requirement to use a classification to arrange books on shelves in specific places so they can be located, checked out, and returned to the same location.

This constraint does not preclude the versioning of library classifications, but it increases the inertia and limits the degree of change when revisions are made because of the cost and coordination considerations of rearranging books in all the world’s libraries.

A related principle about maintaining classifications over time is flexibility, the degree to which the classification can accommodate new categories. Computer scientists typically describe this principle as extensibility, and library scientists sometimes describe it as hospitality. In any case the concern is the same and we are all familiar with it. When you buy a bookshelf, clothes wardrobe, file cabinet, or computer, it makes sense to buy one that has some extra space to accommodate the books, clothes, or files you will acquire over some future time frame. As with other choices that need to be made about organizing systems, how much extra space and “organizing room” you will acquire involves numerous tradeoffs.

Classification schemes can increase their flexibility by creating extra “logical space” when they are defined. Library classifications accomplish this by using naming or numbering schemes for classification that can be extended easily to create new subcategories.[480] Classification schemes in information systems can also anticipate the evolution of document or database schemas.[481]

Classification Is Biased

The discipline of organizing is fundamentally about choices of properties and principles for describing and arranging resources. We discussed choices about describing resources in the section called “The Process of Describing Resources”, choices for creating resource categories in the section called “Principles for Creating Categories”, and choices for creating classifications in this chapter. The choices made reflect the purposes, experiences, professions, politics, values, and other characteristics and preferences of the people making them. As a result, every system of classification is biased because it takes a point of view that is a composite of all of these influences.

Statistical Bias and Variance

Statistical bias is the systematic error in measurements introduced by miscalibration of the measurement instrument, by ineffective measurement techniques, an algorithm that makes incorrect assumptions, or some environment interference, all of which distort the measured value in a predictable way. Measurement bias contrasts with the variability or variance of a measurement, the amount of dispersion around an average or expected value, most often due to random factors. Some variance arises because the property being measured is not the same for all instances, as we would expect for measurements of the weight of a random sample of people, or in the set of tags or topics assigned to a random sample of news articles by people or algorithms. By analyzing a large enough set of instances it is possible to determine the most likely values of the property and also to estimate the amount of random error.

High variance in the measurements for a sample of resources when we expect all of them to have more similar values can be a quality problem. High bias, on the other hand, might be less of a quality problem, because systematic sources of inaccuracy might be easier to correct.

But first we need to point out that there are at least two quite different senses of “bias” that people reading this book are likely to encounter. The colloquial sense of bias we discuss in this section reflects value-based decisions in organizing systems that implicitly or explicitly favor some interactions or users over others. In contrast, statistical bias is systematic error or distortion in a measurement. (See the sidebar, Statistical Bias and Variance.)

Bias and Variance on Dartboards

Precise and accurate dart throws demonstrate low bias and low variance (lower left in the figure). Precise but inaccurate darts reflect high bias and low variance (upper left). Imprecise but accurate ones have low bias but high variance (lower right). Finally, a lack of accuracy and precision shows both high bias and high variance (upper right).


The claim that classification is biased might seem surprising, because many classification systems are formal and institutional, created by governments or firms participating in standards organizations. We expect these classifications to be impartial and objective. However, consider the classification of people as “employed” or “unemployed.” Many people think that any employable person who is not currently employed would be counted as unemployed. But the US government’s Department of Labor only counts someone as unemployed if they have actively looked for work in the past month, effectively removing anyone who has given up on finding work from the unemployed category by assigning them to a “discouraged worker” category. In 2012 this classification scheme allowed the government to report that unemployment was about 8% and falling, when in fact it was closer to 20% and rising. The political implications of this classification are substantial.[482]

California Election Map: A “Blue” State with “Red” Counties


Classification bias is often intentionally or unintentionally shown in data visualizations, including choropleth maps, in which map regions are colored, patterned, or otherwise distinguished according to a statistical variable being displayed on the map. Choropleths are commonly used to display election results, with the districts or states won by each candidate shown in different colors; in the United States, the convention is to show those won by Democratic Party candidates in blue, and those won by Republicans in Red. These election choropleths are often misleading because coloring an entire state in the winner’s colors ignores population density and the regional concentrations of votes that differ from the majority. California voters are reliably “blue” as a whole, but as you can see in the nearby figure with election results divided by county, this majority is amassed in the large cities along the coast, and inland and rural counties are more reliably “red” in their voting.

A more subtle way in which choropleths encode bias reflects the decisions made to organize the data into the categories that are represented by different colors or patterns. Choropleth categories might present data divided into equal range intervals, into sets with the same number of observations, or into categories that reflect clusters or natural breaks in the observed data. Small changes in the data ranges or proportions that are then assigned to each category can communicate entirely different stories with the same data. To learn “how to lie with maps” or how to prevent being lied to, refer to the classic book with that title by Mark Monmonier.[483]

Friedman and Nissenbaum’s Bias In Computer Systems offers a framework for conceptualizing the various types of bias that may be present in technical systems. Friedman and Nissenbaum define bias as “a system that systematically and unfairly discriminates against individuals or groups of individuals in favor of others” Their taxonomy includes pre-existing, technical, and emergent bias.[484]

Pre-existing bias is the type people are most familiar with: it occurs when an organizing system’s design embodies personal or societal biases that exist at the time of its creation, either intentionally or inadvertently, and sometimes despite one’s best intentions to prevent it.

Technical bias arises from limitations and constraints of technical systems that result in unfairness when the system is applied to the real world. Automated decision-making is especially ripe for this sort of bias: alphabetical ordering, processes that rely on pseudo-random number generation, and other automated ways of sorting or grouping resources may systematically create different opportunities for different user groups (e.g., people or companies whose names begin with “A”).

Emergent bias is related to the interplay between actual users and a technical system. Problems of this type arise when, due to the designer’s incomplete understanding of the user population, or a change in that population over time, there is a mismatch between users and the system. User interfaces are especially susceptible to this form of bias, given their need to reflect the habits and capacities of intended users. Unfairness can emerge when an unexpected user group uses the system, or as new societal knowledge arises that the system is not able to incorporate or respond to.

Both pre-existing and emergent bias may be difficult to assess accurately; the former may be difficult for the biased to see or admit to, and the latter, arising due to unanticipated circumstances after implementation, is hard to predict.

Bowker and Star have written extensively about biases in classification systems but acknowledge that many people do not see them:


Information scientists work every day on the design, delegation and choice of classification systems and standards, yet few see them as artifacts embodying moral and aesthetic choices that in turn craft people’s identities, aspirations and dignity.[485]



[(Bowker and Star 2000)]

Bowker and Star describe many examples where seemingly neutral and benign classifications implement controversial assumptions. A striking example is found in the ethnic classifications of the United States Census and the categories to which US residents are required to assign themselves. These categories have changed nearly every decade since the first census in 1790 and strongly reflect political goals, prevailing cultural sensitivities or lack thereof, and non-scientific considerations. Some recent changes included a “multi-racial” category, which some people viewed as empowering, but which was attacked by African-American and Hispanic civil rights groups as diluting their power.[486]

A more positive way to think about bias in classification is that the choices made in an organizing system about resource selection, description, and arrangement come together to convey the values of the organizers. This makes a classification a rhetorical or communicative vehicle for establishing credibility and trust with those who interact with the resources in the classification. Seen in this light, an objective or neutral classification is not only unrealistic as a goal; it may also consume valuable time and energy when instead it might be more desirable to seize the opportunity to interpret the resources in a creative way to communicate a particular message to a particular user group. Melanie Feinberg makes the point that “fair trade” or “green” supermarkets differentiate themselves by a relatively small proportion of the goods they offer compared with ordinary stores, but these particular items signal the values that their customers care most about.[487]

Bias is clearly evident in the most widely used bibliographic classifications, the Library of Congress and the Dewey Decimal, which we discuss next.

Bibliographic Classification

Much of our thinking about classification comes from the bibliographic domain. Libraries and the classification systems for the resources they contain have been evolving for millennia, shaped by the intellectual, social, and technological conditions of the societies that created them. As early as the third millennium BCE, there were enough written documents—papyrus scrolls or clay tablets—that the need arose to organize them. Some of the first attempts, by Mesopotamian scribes, were simple lists of documents in no particular order. The ancient Greeks, Romans, and Chinese created more principled systems, both sorting works by features such as language and alphabetical order, and placing them into semantically significant categories such as topic or genre. Medieval European libraries were tightly focused on Christian theology, but as secular books and readers proliferated thanks to new technologies and increased literacy, bibliographic classifications grew broader and more complex to accommodate them. Modern classification systems are highly nuanced systems designed to encompass all knowledge; however, they retain some of the same features and biases of their forebears.[488]

We will briefly describe the most important systems for bibliographic classification, especially the Dewey Decimal Classification(DDC) and Library of Congress Classification(LCC) systems. However, there are several important ways in which bibliographic classification is distinctive and we will discuss those first:

Scale, Complexity, and Degree of Standardization:

Department stores and supermarkets typically offer tens of thousands of different items (as measured by the number of “stock keeping units” or SKUs), and popular online commerce sites like Amazon.com and eBay are of similar scale. However, the standard product classification system for supermarkets has only about 300 categories.[489] The classifications for online stores are typically deeper than those for physical stores, but they are highly idiosyncratic and non-standard. In contrast, scores of university libraries have five million or more distinct items in their collections, and they almost all use the same standard bibliographic classification system that has about 300,000 distinct categories.[490]

Legacy of Physical Arrangement, User Access, and Re-Shelving:

A corollary to the previous one that distinguishes bibliographic classification systems is that they have long been shaped and continue to be shaped by the legacy of physical arrangement, user access to the storage locations, and re-shelving that they support. These requirements constrain the evolution and extensibility of bibliographic classifications, making them less able to keep pace with changing concepts and new bodies of knowledge. Amazon classifies the products it sells in huge warehouses, but its customers do not have to pick out their purchases there, and most goods never return to the warehouse. Amazon can add new product categories and manage the resources in warehouses far more easily than libraries can.

With digital libraries, constraints of scale and physical arrangement are substantially eliminated, because the storage location is hidden from the user and the resources do not need to be returned and re-shelved. However, when users can search the entire content of the library, as they have learned to expect from the web, they are less likely to use the bibliographic classification systems that have painstakingly been applied to the library’s resources.

The Dewey Decimal Classification

The Dewey Decimal Classification(DDC) is the world’s most widely used bibliographic system, applied to books in over 200,000 libraries in 135 countries. It is a proprietary and de facto standard, and it must be licensed for use from the Online Computer Library Center(OCLC).[491]

In 1876, Melvil Dewey invented the DDC when he was hired to manage the Amherst College library immediately after graduating. Dewey was inspired by Bacon’s attempt to create a universal classification for all knowledge and considered the DDC as a numerical overlay on Bacon with 10 main classes, each divided into 10 more, and so on. Despite his explicit rejection of literary warrant, however, Dewey’s classification was strongly influenced by the existing Amherst collection, which reflected Amherst’s focus on the time on the “education of indigent young men of piety and talents for the Christian ministry.”[492]

The resulting nineteenth-century Western bias in the DDC’s classification of religion seems almost startling today, where it persists in the 23rd revision (see Figure 8.2, ““Religion” in Dewey Decimal Classification.”). “Religion” is one of the 10 main classes, the 200 class, with nine subclasses, Six of these nine subclasses are topics with “Christian” in the name; one class is for the Bible alone; and another section is entitled “Natural theology.” Everything else related to the world’s many religions is lumped under 290, “Other religions.”

Figure 8.2. “Religion” in Dewey Decimal Classification.



200 Religion

210 Natural Theology

220 Bible

230 Christian theology

240 Christian moral and devotional theology

250 Christian orders and local church

260 Christian social theology

270 Christian church history

280 Christian sects and denominations

290 Other religions


The notational simplicity of a decimal system makes the DDC easy to use and easy to subdivide existing categories, So-called subdivision tables allow facets for language, geography or format to be added to many classes, making the classification more specific. But the overall system is not very hospitable to new areas of knowledge.

The Library of Congress Classification

The US Library of Congress is the largest library in the world today, but it got off to a bad start after being established in 1800. In 1814, during the War of 1812, British troops burned down the US Capitol building where the library was located and the 3000 books in the collection went up in flames.[493] The library was restarted a year later when Congress purchased the personal library of former president Thomas Jefferson, which was over twice the size of the collection that the British burned. Jefferson was a deeply intellectual person, and unlike the narrow historical and legal collection of the original library, Jefferson’s library reflected his “comprehensive interests in philosophy, history, geography, science, and literature, as well as political and legal treatises.”[494]

Restarting the Library of Congress around Jefferson’s personal collection and classification had an interesting implication. When Herbert Putnam formally created the Library of Congress Classification (LCC) in 1897, he meant it not as a way to organize all the world’s knowledge, but to provide a practical way to organize and later locate items within the Library of Congress’s collection. However, despite Putnam’s commitment to literary warrant, the breadth of Jefferson’s collection made the LCC more intellectually ambitious than it might otherwise had been, and probably contributed to its dominant adoption in university libraries.

The LCC has 21 top-level categories, identified by letters instead of using numbers like the DDC (see Figure 8.3, “Top Level Categories in the Library of Congress Classification.”). Each top-level category is divided into about 10-20 subclasses, each of which is further subdivided. The complete LCC and supporting information takes up 41 printed volumes.

Figure 8.3. Top Level Categories in the Library of Congress Classification.


























Bias is apparent in the LCC as it is in the DDC, but is somewhat more subtle. A library for the US emphasizes its own history. “Naval science” was vastly more important in the 1800s when it was given its own top level category, separated from other resources about “Military science” (which had a subclass for “Cavalry”).[495]

The LCC is highly enumerative, and along with the uniqueness principle, this creates distortions over time and sometimes requires contortions to incorporate new disciplines. For example, it might seem odd today that a discipline as broad and important as computer science does not have its own second level category under the Q category of science, but because computer science was first taught in math departments, the LCC has it as the QA76 subclass of mathematics, which is QA.[496]

The BISAC Classification

A very different approach to bibliographic classification is represented in the Book Industry Standards Advisory Committee classification(BISAC). BISAC is developed by the Book Industry Study Group(BISG), a non-profit industry association that “develops, maintains, and promotes standards and best practices that enable the book industry to conduct business more efficiently.” The BISAC classification system is used by many of the major businesses within the North American book industry, including Amazon, Baker & Taylor, Barnes & Noble, Bookscan, Booksense, Bowker, Indigo, Ingram and most major publishers.[497]

The BISAC classifications are used by publishers to suggest to booksellers how a book should be classified in physical and online bookstores. Because of its commercial and consumer focus, BISAC follows a principle of use warrant, and its categories are biased toward common language usage and popular culture. Some top-level BISAC categories, including Law, Medicine, Music, and Philosophy, are also top-level categories in the LCC. However, BISAC also has top-level categories for Comics & Graphic Novels. Cooking, Pets, and True Crime.

The differences between BISAC and the LCC are understandable because they are used for completely different purposes and generally have little need to come into contact. This changed in 2004, when Google began its ambitious project to digitize the majority of the world’s books. (See the sidebar, What Is a Library?). To the dismay of many people in the library and academic community, Google initially classified books using BISAC rather than the LCC.[498]

In addition, some new public libraries have adopted BISAC rather than the DDC because they feel the former makes the library friendlier to its users. Some librarians believe that their online catalogs need to be more like web search engines, so a less precise classification that uses more familiar category terms seems like a good choice.[499]

Faceted Classification

We have noted several times that strictly enumerative classifications constrain how resources are assigned to categories and how the classification can evolve over time. Faceted classifications are an alternative that overcome some of these limitations. In a faceted classification system, each resource is described using properties from multiple facets, but a person searching for resources does not need to consider all of the properties (and consequently the facets) and does not need to consider them in a fixed order, which an enumerative hierarchical classification requires.

Faceted classifications are especially useful in web user interfaces for online shopping or for browsing a large and heterogeneous museum collection. The process of considering facets in any order and ignoring those that are not relevant implies a dynamic organizational structure that makes selection both flexible and efficient. We can best illustrate these advantages with a shopping example in a domain that we are familiar with from the section called “Multiple Properties”.

If a department store offers shirts in various styles, colors, sizes, brands, and prices, shoppers might want to search and sort through them using properties from these facets in any order. However, in a physical store, this is not possible because the shirts must be arranged in actual locations in the store, with dress shirts in one area, work shirts in another, and so on.

Assume that the shirt store has shirts in four styles: dress shirts, work shirts, party shirts, and athletic shirts. The dress shirts come in white and blue, the work shirts in white and brown, and the party and athletic shirts come in white, blue, brown, and red. White dress shirts come in large and medium sizes.

Suppose we are looking for a white dress shirt in a large size. We can think of this desired shirt in two equivalent ways, either as a member of a category of “large white dress shirts” or a shirt with “dress,” “white,” and “large” values on style, color, and size facets. Because of the way the shirts are arranged in the physical store, our search process has to follow a hierarchical structure of categories. We go to the dress shirt section, find white shirts, and then look for a large one. This process corresponds to the hierarchy shown in Figure 8.4, “Enumerative Classification with Style Facet Followed by Color Facet.”

Figure 8.4. Enumerative Classification with Style Facet Followed by Color Facet.


In an enumerative classification system the order of the facets determines the classification hierarchy. For example, a store might classify shirts first using a style facet, next with a color facet, and finally with a size facet. This ordering could result in two piles of dress shirts, one blue and one white, in which each pile contains shirts of large and medium sizes.


Although unlikely, a store might choose to organize its shirts by color. In our search for a “white dress shirt in a large size,” if we consider the color first, because shirts come in four colors, there are four color categories to choose from. When we choose the white shirts, there is no category for work shirts because there are no work shirts that come in white. We then choose the dress shirts, and then finally find the large one. (Figure 8.5, “Enumerative Classification with Color Facet Followed by Style Facet.”)

This department store example shows that for a physical organization, one property facet guides the localization of resources; all other facets are subordinated under the primary organizing property. In hierarchical enumerative classifications, this means that the primary organizing facet determines the primary form of access. The shirts are either organized by style and then color, or by color then style, which enforces an inflexible query strategy (style first or color first).

Figure 8.5. Enumerative Classification with Color Facet Followed by Style Facet.


An alternative ordering of the same shirt facets changes the classification hierarchy. If the first facet considered is color, style is next, and finally size, this ordering could result in two piles of white shirts, one for dress shirts and one for athletic shirts, in which each pile contains shirts of large and medium sizes.


In an online store, however, descriptions of the shirts are being searched and sorted instead of the real shirts, and different organizations are possible. When the shirts are described using a faceted classification system, we treat all facets independently (i.e., they can all be the primary facet).

We can enumerate all the properties needed to assign resources appropriately, but we create the categories (i.e., union of properties from different facets) only as needed to sort resources with a particular combination of properties.

An additional aspect of the flexibility of faceted classification is that a facet can be left out of a resource description if it is not needed or appropriate. For example, because party shirts are often multi-colored with exotic patterns, it is not that useful to describe their color. Likewise, certain types of athletic shirts might be very loose-fitting, and as a result not be given a size description, but their color is important because it is tied to a particular team. Figure 8.6, “Faceted Classification.” shows how these two resource types can be classified with the faceted Shirt classification. Resource 1 describes a party shirt in medium; resource 2 describes an athletic shirt in blue without information about size.

Figure 8.6. Faceted Classification.


In a pure faceted classification, not every facet needs to apply to every resource, and there is no requirement for a predetermined order in which the facets are considered.


A faceted classification scheme like that shown in Figure 8.6, “Faceted Classification.” eliminates the requirement for predetermining a combination and ordering of facets like those in Figure 8.4, “Enumerative Classification with Style Facet Followed by Color Facet.” and Figure 8.5, “Enumerative Classification with Color Facet Followed by Style Facet.” Instead, imagine a shirt store where you decide when you begin shopping which facets are important to you (“show me all the medium party shirts,” “show me the blue athletic shirts”) instead of having to adhere to whatever predetermined (pre-combined) enumerative classification the store invented. In a digital organizing system, faceted classification enables highly flexible access because prioritizing different facets can dynamically reorganize how the collection is presented.

Foundations for Faceted Classification

In library and information science texts it is common to credit the idea of faceted classification to S.R. Ranganathan, a Hindu mathematician working as a librarian. Ranganathan had an almost mystical motivation to classify everything in the universe with a single classification system and notation, considering it his dharma (the closest translation in English would be “fundamental duty” or “destiny”). Facing the limitations of Dewey’s system, where an item’s essence had to first be identified and then the item assigned to a category based on that essence, Ranganathan believed that all bibliographic resources could be organized around a more abstract variety of aspects.

In 1933 Ranganathan proposed that a set of five facets applied to all knowledge:


The type of thing.


The constituent material of the thing.


The action or activity of the thing.


Where the thing occurs.


When the thing occurs.

This classification system is known as colon classification (or PMEST) because the notation used for resource identifiers uses a colon to separate the values on each facet. These values come from tables of categories and subcategories, making the call number very compact. Colon classification is most commonly used in libraries in India.[500]

For example, a book on “research in the cure of tuberculosis of lungs by x-ray conducted in India in 1950” has a Personality facet value of Medicine, a Matter facet value of Lungs with tuberculosis, an Energy facet value of Treatment using X-rays, a Space facet value of India, and a Time facet value of 1950. When the alphanumeric codes for these values are looked up in the classification tables, the composed call number is L,45;421:6;253:f.44’N5.[501]

Ranganathan deserves credit for implementing the first faceted classification system, but people other than librarians generally credit the idea to Nicolas de Condorcet, a French mathematician and philosopher. About 140 years before Ranganathan, Condorcet was concerned that “systems of classification that imposed a given interpretation upon Nature… represented an insufferable obstacle to… scientific advance.” Condorcet thus proposed a flexible classification scheme for “arranging a large number of subjects in a system so that we may straightway grasp their relations, quickly perceive their combinations, and readily form new combinations.”[502]

Condorcet’s system was based on five major facet categories, divided into 10 terms each, yielding 10^5 or 100,000 combinations:


domains of study.


for studying objects and describing the knowledge gained.

Points of view

for studying objects.

Uses and utility

of knowledge.


in which knowledge can be acquired.[503]

Condorcet and Ranganathan proposed different facets, but both hoped that their five top-level facets would be sufficient for a universal classification system. People have generally rejected the idea of universal facets, but Ranganathan’s proposals continue to influence the development of the Library of Congress Subject Headings (LCSH).[504]

Faceted classification is most commonly used in narrow domains, each with its own specific facets. This makes intuitive sense because even if resources can be distinguished with a general classification, doing so requires lengthy notations, and it is much harder to add to a general classification than to a classification created specifically for a single subject area. We could probably describe shirts using the PMEST facets, but style, color, and size seem more natural.

Faceted Classification in Description

Elaine Svenonius defines facets as “groupings of terms obtained by the first division of a subject discipline into homogeneous or semantically cohesive categories.”[505] The relationships between these facets results in a controlled vocabulary (the section called “Identity, Identifiers, and Names”) governing the resources we are organizing. From this controlled vocabulary we can generate many descriptions that are complex but formally structured, enabling us to describe things for which terms do not yet exist.

Getty’s Art & Architecture Thesaurus(AAT) is a robust and widely used controlled vocabulary consisting of generic terms to describe artifacts, objects, places and concepts in the domains of “art, architecture, and material culture.”[506]

AAT is a thesaurus with a faceted hierarchical structure. The AAT’s facets are “conceptually organized in a scheme that proceeds from abstract concepts to concrete, physical artifacts:”

Associated Concepts

Concepts, philosophical and critical theory, and phenomena, such as “love” and “nihilism.”

Physical Attributes

Material characteristics that can be measured and perceived, like “height” and “flexibility.”

Styles and Periods

Artistic and architectural eras and stylistic groupings, such as “Renaissance” and “Dada.”


Basically, people and the various groups and organizations with which they identify, whether based on physical, mental, socio-economic, or political characteristicse.g., “stonemasons” or “socialists.”


Actions, processes, and occurrences, such as “body painting” and “drawing.” These are different from the “Objects” facet, which may also contain “body painting,” in terms of the actual work itself, not the creation process.


Concerned with the actual substance of which a work is made, like “metal” or “bleach.” “Materials” differ from “Physical Attributes” in that the latter is more abstract than the former.


The largest facet, objects contains the actual works, like “sandcastles” and “screen prints.”

Within each facet is a strict hierarchical structure drilling down from broad term to very specific instance.

Figure 8.7. “Patent Leather” in the Art & Architecture Thesaurus.


The Art and Architecture Thesaurus has a faceted hierarchical structure. For example, the materials facet distinguishes the material of “patent leather” according to the process applied to processed animal material, which is a type of biological material, and so on.


Figure 8.7, ““Patent Leather” in the Art & Architecture Thesaurus.” shows how a particular instance may be described on a number of dimensions for the purpose of organizing the item and retrieving information about it. And by using a standard

controlled vocabulary, catalogers and indexers make it easier for users to understand and adapt to the way things are organized for the purpose of finding them.[507]

A Classification for Facets

There are four major types of facets.

Enumerative facets

Have mutually exclusive possible values. In our online shirt store, “Style” is an enumerative facet whose values are “dress,” “work,” “party,” and “athletic.”

Boolean facets

Take on one of two values, yes (true) or no (false) along some dimension or property. On a sportswear website, “Waterproof” would be a Boolean facet because an item of clothing is either waterproof or it is not.

Hierarchical facets

Organize resources by logical inclusion (the section called “Inclusion”). At Williams-Sonoma’s website, the top-level facet includes “Cookware,” “Cooks’ Tools,” and “Cutlery.” At wine.com the “Region” facet has values for “US,” “Old World,” and “New World,” each of which is further divided geographically. [508] Also see taxonomic facets.

Spectrum facets

Assume a range of numerical values with a defined minimum and maximum. Price and date are common spectrum facets. The ranges are often modeled as mutually exclusive regions (potential price facet values might include “$0—$49,” “$50—$99,” and “$100—$149”).

Designing a Faceted Classification System

It is important to be systematic and principled when designing a faceted classification. In some respects the process and design concerns overlap with those for describing resources, and much of the advice in the section called “The Process of Describing Resources” is relevant here.[509]

Design Process for Faceted Classification

We advocate a five step process for designing a faceted classification system.

Define the purposes of the classification (the section called “Determining the Purposes”, the section called “Classification Is Purposeful”) and specify the collection of concepts or resources to be classified.

For each facet, determine its logical type (the section called “A Classification for Facets”) and possible values. Specify the order of the values for each facet so that they make sense to users; useful orderings are alphabetical, chronological, procedural, size, most popular to least popular, simple to complex, and geographical or topological.

Analyze and describe a representative sample of resource instances to identify properties or dimensions as candidate facets (See the section called “Identifying Properties”).

Examine the relationships between the facets to create sub-facets if necessary. Determine how the facets will be combined to generate the classifications.

Test the classification on new instances, and revise the facets, facet values, and facet grammar as needed.

Design Principles and Pragmatics

Here is some more specific advice about selecting and designing facets and facet values:


Facets should be independent dimensions, so a resource can have values of all of them while only having one value on each of them. In an online kitchen store, one facet might be “Product” and another might be “Brand.” A particular item might be classified as a “Saucepan” in the “Product” facet and as Calphalon in the “Brand” one. Other saucepans might have other brands, and other Calphalon products might not be saucepans, because Product and Brand are orthogonal.

Semantic Balance

Top-level facets should be the properties that best differentiate the resources in the classification domain. The values should be of equal semantic scope so that resources are distributed among the subcategories. Subfacets of “Cookware” like “Sauciers and Saucepans” and “Roasters and Brasiers” are semantically balanced as they are both named and grouped by cooking activity.[510]


The values of a facet should be able of classifying all instances within the intended scope.


Facet values must accommodate potential additions to the set of instances. Including an “Other” value is an easy way to ensure that a facet is flexible and hospitable to new instances, but it not desirable if all new instances will be assigned that value.


Although every classification has an explicit or implicit bias (the section called “Classification Is Biased”), facets and facet values should be as unambiguous and concrete as possible to enable reliable classification of instances.


To make a faceted classification as useful by as many people as possible, the terms used for facets and facet values should not be idiosyncratic, metaphorical, or require special knowledge to interpret.[511]

As we will see in the section called “Computational Classification”, classification can sometimes be done by computers rather than by people. Computer algorithms can analyze resource properties and descriptions to identify dimensions on which resources differ and the most frequent descriptive terms, which can then be used to design a faceted classification scheme. Resources can then be assigned to the appropriate categories, either without human intervention or in collaboration with a human who trains the algorithm with classified instances.

Classification by Activity Structure

Institutional classification systems are often strongly hierarchical and taxonomic because their many users come to them for diverse purposes, making a context-free or semantic organization the most appropriate. However, in narrow domains that offer a more limited variety of uses it can be much more effective to classify resources according to the tasks or activities they support. A task or activity-based classification system is called a taskonomy, a term invented by anthropologists Janet Dougherty and Charles Keller after their ethnographic study of how blacksmiths organized their tools. Instead of keeping things together according to their semantic relationships in what Donald Norman called “hardware store organization,” the blacksmiths arranged tools in locations where they were used “fire tools,” “stump tools,” “drill press rack tools,” and so on.[512]

Personal organizing systems are often taskonomic. Think about the way you cook when you are following a recipe. Do you first retrieve all the ingredients from their storage places, and arrange them in activity-based groups in the preparation area?[513]

Looking at the relationship between tasks and tools in this way can help a cook determine the best way to organize tools in a kitchen. Cutting items would necessarily be kept together near a prep area; having to run across the kitchen to another area where a poultry knife is kept with, say, chicken broth would be detrimental to the cook’s workflow. It would make far more sense to have all of the items for the task of cutting in a single area.

The intentional arrangement of tools in a working kitchen might look something like Table 8.1:

Table 8.1. A cook’s taskonomy




Poultry knife

Oven mitts

Pots and pans

Paring knife

Baking sheets

Wooden spoons

Vegetable knife

Aluminum foil


Cutting board

Parchment paper



Roasting pan



Stop and Think: Office Taskonomy

Think about your personal office space. It may be an interesting hybrid space—it probably contains documents that could be classified in a hierarchical system, but it is also a work space that could lend itself to “taskonomy” organization. Which does it more closely resemble? How have any conflicts between hierarchy and “taskonomy” been resolved?

Computational Classification

Because of its importance, ubiquity, and ease of processing by computers, it should not be surprising that a great many computational classification problems involve text. Some of these problems are relatively simple, like identifying the language in which a text is written, which is solved by comparing the probability of one, two, and three character-long contiguous strings in the text against their probabilities in different languages. For example, in English the most likely strings are “the”, “and”, “to”, “of”, “a”, “in”, and so on. But if the most likely strings are “der”, “die”, “und”, and “den” the text is German and if they are “de”, “la”, “que”, “el”, and “en” the text is Spanish.

More challenging text classification problems arise when more features are required to describe each instance being classified and where the features are less predictable. The unknown author of a document can sometimes be identified by analyzing other documents known to be written by him to identify a set of features like word frequency, phrase structure, and sentence length that create a “writeprint” analogous to a fingerprint that uniquely identifies him. This kind of analysis was used in 2013 to determine that Harry Potter author J. K. Rowling had written a crime fiction novel entitled The Cuckoo’s Calling under the pseudonym Robert Galbraith.[514]

Another challenging text classification problem is sentiment analysis, determining whether a text has a positive or negative opinion about some topic. Much academic and commercial research has been conducted to understand the sentiment of Twitter tweets, Facebook posts, email sent to customer support applications, and other similar contexts. Sentiment analysis is hard because messages are often short so there is not much to analyze, and because and because sarcasm, slang, clichés, and cultural norms obscure the content needed to make the classification.

A crucial consideration whenever supervised learning is used to train a classifier is ensuring that the training set is appropriate. If we were training a classifier to detect spam messages using email from the year 2000, the topics of the emails, the words they contain, and perhaps even the language they are written in would be substantially different than messages from this year. Up to date training data is especially important for the classification algorithms used by Twitter, Facebook, YouTube, and similar social sites that classify and recommend content based on popularity trends.

When the relevant training data is constantly changing and there is a great deal of it, there is a risk that by the time a model can learn to classify correctly it is already out of date. This challenge has led to the development of streaming algorithms that operate on data as it comes in, using it as a live data source rather than as a static training set. Streaming algorithms are essential for tackling datasets that are too large to store or for models that must operate under intense time pressure. Streaming approaches complement rather than replace those that work with historical datasets because they make different tradeoffs between accuracy and speed. The streaming system might provide real-time alerting and recommendations, while historical analyses are made on the batch-oriented system that works with the entire data collection.[515]

Stop and Think: Sentiment Analysis

Sometimes, a text message might seem complimentary, but really is not. Is the customer happy if he tweets “Nice job, United. You only lost one of my bags this time.” Think of some other short messages where sarcasm or slang makes sentiment analysis difficult. How would you write a product or service review that is unambiguously positive, negative, or neutral? How would you write a review whose sentiment is difficult to determine?

How a computational classifier “learns” depends on the specific machine learning algorithm. Decision trees, Naive Bayes, support vector machines, and neural net approaches were briefly described in the section called “Implementing Categories”.

Key Points in Chapter Eight

8.7.1. What is classification?

8.7.2. What is a classification system?

8.7.3. How does classification affect the potential interactions in an organizing system?

8.7.4. Why are classifications always biased in some way?

8.7.5. According to Friedman and Nissenbaum, what are the three types of bias in technical systems?

8.7.6. What are enumerative classification schemes?

8.7.7. What is a taxonomic classification scheme?

8.7.8. What is the relationship between classification and standardization?

8.7.9. What is a standard?

8.7.10. Why are standard semantics important?

8.7.11. What is literary warrant?

8.7.12. What is the uniqueness principle?

8.7.13. How is the uniqueness principle followed when resources do not clearly fit in a single category?

8.7.14. What motivates category change?

8.7.15. What is the relationship among flexibility, extensibility and hospitality in a classification system?

8.7.16. What distinguishes bibliographic classification?

8.7.17. What distinguishes faceted classification systems?

8.7.18. What is orthogonality in a faceted classification system?

8.7.19. What is semantic balance in a faceted classification system?

8.7.20. What is scalability in a faceted classification system?

8.7.21. What is the relationship between classification and tagging?

8.7.22. What is a taskonomy?

8.7.23. What is the relationship between supervised learning and classification?


What is classification?


Classification is the systematic assignment of resources to a system of intentional categories, often institutional ones.

(See the section called “Introduction”)


What is a classification system?


A classification system is foremost a specification for the logical arrangement of resources because there are usually many possible and often arbitrary mappings of logical locations to physical ones.

(See the section called “Classification vs. Physical Arrangement”)


How does classification affect the potential interactions in an organizing system?


A classification creates structure in the organizing system that increases the variety and capability of the interactions it can support.

(See the section called “Classifications Support Interactions”)


Why are classifications always biased in some way?


Classifications are always biased by the purposes, experiences, professions, politics, values, and other characteristics and preferences of the people making them.

(See the section called “Classification Is Biased”)


According to Friedman and Nissenbaum, what are the three types of bias in technical systems?


Three types of bias in technical systems are pre-existing, technical, and emergent bias.

(See the section called “Classification Is Biased”)


What are enumerative classification schemes?


Classification schemes in which all possible categories to which resources can be assigned are defined explicitly are called enumerative.

(See the section called “Classification Schemes”)


What is a taxonomic classification scheme?


When multiple resource properties are considered in a fixed sequence, each property creates another level in the system of categories and the classification scheme is hierarchical or taxonomic.

(See the section called “Classification Schemes”)


What is the relationship between classification and standardization?


Classification and standardization are not identical, but they are closely related. Some classifications become standards, and some standards define new classifications.

(See the section called “Classification and Standardization”)


What is a standard?


A standard is a published specification that is developed and maintained by consensus of all the relevant stakeholders in some domain by following a defined and transparent process.

(See the section called “Specifications vs. Standards”)


Why are standard semantics important?


Standard semantics are especially important in industries or markets that have significant network effects where the value of a product depends on the number of interoperable or compatible products.

(See the section called “Institutional Semantics”)


What is literary warrant?


The principle of literary warrant holds that a classification must be based only on the specific resources that are being classified.

(See the section called “Principles Embodied in the Classification Scheme”)


What is the uniqueness principle?


The uniqueness principle means the categories in a classification scheme are mutually exclusive. Thus, when a logical concept is assigned to a particular category, it cannot simultaneously be assigned to another category.

(See the section called “Principles for Assigning Resources to Categories”)


How is the uniqueness principle followed when resources do not clearly fit in a single category?


The general solution to satisfying the uniqueness principle in library classifications when resources do not clearly fit in a single category is to invent and follow a detailed set of often-arbitrary rules.

(See the section called “Principles for Assigning Resources to Categories”)


What motivates category change?


Categories sometimes change slowly, but they can also change quickly and radically as a result of technological, process, or geopolitical innovation or events.

(See the section called “Principles for Maintaining the Classification over Time”)


What is the relationship among flexibility, extensibility and hospitality in a classification system?


Flexibility, extensibility, and hospitality are synonyms for the degree to which the classification can accommodate new resources.

(See the section called “Principles for Maintaining the Classification over Time”)


What distinguishes bibliographic classification?


Bibliographic classification is distinctive because of a legacy of physical arrangement and its scale and complexity.

(See the section called “Bibliographic Classification”)


What distinguishes faceted classification systems?


Faceted classification systems enumerate all the categories needed to assign resources appropriately, but instead of combining them in advance in a fixed hierarchy, they are applied only if they are needed to sort resources with a particular combination of properties.

(See the section called “Faceted Classification”)


What is orthogonality in a faceted classification system?


Facets should be independent dimensions, so a resource can have values of all of them while only having one value on each of them.

(See the section called “Design Principles and Pragmatics”)


What is semantic balance in a faceted classification system?


Top-level facets should be the properties that best differentiate the resources in the classification domain. The values should be of equal semantic scope so that resources are distributed among the subcategories. Subfacets of “Cookware” like “Sauciers and Saucepans” and “Roasters and Brasiers” are semantically balanced as they are both named and grouped by cooking activity.

(See the section called “Design Principles and Pragmatics”)


What is scalability in a faceted classification system?


Facet values must accommodate potential additions to the set of instances. Including an “Other” value is an easy way to ensure that a facet is flexible and hospitable to new instances, but it not desirable if all new instances will be assigned that value.

(See the section called “Design Principles and Pragmatics”)


What is the relationship between classification and tagging?


Most tagging seems insufficiently principled to be considered classification, except when tags are treated as category labels or when decisions that make tagging more systematic turn a set of tags into a tagsonomy.

(See the section called “Classification vs. Tagging”)


What is a taskonomy?


A task or activity-based classification system is called a taskonomy.

(See the section called “Classification by Activity Structure”)


What is the relationship between supervised learning and classification?


Supervised learning techniques start with a designed classification scheme and then train computers to assign new resources to the categories.

(See the section called “Computational Classification”)


[453] [(Batley 2005 p. 1)].

[454] [(Hammond et al. 2004)] note that the “unstructured (or better, free structured) approach to classification with users assigning their own labels is variously referred to as a folksonomy, folk classification, ethnoclassification, distributed classification, or social classification.”

[455] Thomas Vander Wal invented the term “folksonomy” in 2004, and the term quickly gained traction. His personal account of the creation and dispersion of the term is [(Vander Wal 2007)].

[456] See [(Halvey and Keane 2007)], [(Sinclair and Cardrew-Hall 2007)]) for analyses of the usability of different presentations, and [(Kaser and Lemire 2007)] for algorithms for drawing tag clouds.

[457] See [(Millen, Feinberg, and Kerr 2006)], [(John and Seligmann 2006)].

[458] The statistical techniques used in topic models are intimidating; to vastly oversimplify, topic models start with a document x term matrix and extract topics by reducing the dimensionality through linear algebra techniques. [(Blei 2012)] is a relatively easy introduction.

[459] Gruenberg wrote this definition over a decade ago as a University of Illinois PhD student in an unpublished paper titled “Faceted Classification, Facet Analysis, and the Web” that was found by a web search by the first author of this chapter in 2005. When this chapter was being written several years later, the paper was no longer on the web, but a copy was located at Illinois by Matthew Beth on a backup disk.

[460] This is reflected in library call numbers, which assign a unique number to books to designate the order in which they are shelved. Most American libraries use a classification system as part of their call number, composing it from a class number of the classification and a unique identifier (derived from the author name and title), which identifies the book within the class, often using a system called Cutter numbers. See http://www.itsmarc.com/crs/mergedProjects/cutter/cutter/general_information_cutter.htm.

[461] The most “standard” of all standards organization is the International Organization for Standardization(ISO), whose members are themselves national standards organizations, which as a result gives the nearly 20,000 ISO standards the broadest and most global coverage. See http://ISO.org. In addition, there are scores of other national and industry-specific standards bodies whose work is potentially relevant to organizing systems of the sorts discussed in this book. We encounter these kinds of standards every day in codes for countries, currencies, and airports, in file formats, in product barcodes, and in many other contexts.

[462] Dewey Decimal Classification: http://www.oclc.org/dewey/DDC.

Similarly, the DSM is maintained and published by the American Psychiatric Association(APA) and it earns the many millions of dollars a year.

[463] [(OASIS 2006).] All the finished work of OASIS is freely available at https://www.oasis-open.org; the UBL committee is at https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=ubl.

[464] A small number of people can often informally agree on an organizing system that meets the needs of each participant. But each new person often brings new requirements and it is not feasible to resolve every disagreement between every pair of participants. Instead, for a large-scale organizing system, decisions are usually made by entities that have the authority to coordinate actions and prevent conflicts by imposing a single solution on all the participants. [(Rosenthal, Seligman, and Renner 2004)] call this the “person-concept” tradeoff, which we can paraphrase as “a few people can agree on a lot, but a lot of people can only agree on a little.”

This authority can come from many different sources, but they can be roughly categorized as “authority from power” and “authority from consensus.” Often the economic dominance of a firm allows it to control how business gets done in its industry. One key part of that is establishing specifications for data formats and classification schemes in organizing systems, which usually means requiring other firms to use the ones developed by the dominant firm for its own use. This ensures the continued efficiency of their own business processes while making it harder for other firms to challenge their market power.

In contrast, consensus is the authority mechanism embodied in the workings of the open source community, where the freedom to view and change data formats and code that uses them encourages cooperation and adoption. Consensus also underlies the authority of voluntary standards activities, where firms work together under the auspices of a standards body and agree to follow its procedures for creating, ratifying, and implementing standards.

[465] International and national standards bodies derive their authority from the authority of the governments that created them. But standards organizations arguably derive most of their authority from the collective power of their members, because many influential standards organizations like OASIS, W3C, OMG, and IETF are not chartered or sponsored by governments. In addition, firms often create ad hoc “quasi-standards” organizations or “communities of interest” to facilitate relatively short-term cooperative standards-making activities that in the former case would otherwise be prohibited by anti-trust considerations. Finally, at the extreme “lightweight” end of the standards-making continuum, the codification of simple and commonly used information models as “microformats” depends on authority that emerges from the collaboration of individuals rather than firms.

[466] Often a standard evolves from an existing specification submitted to a standards organization by the firm that created it. In other cases, the specifications used by a dominant firm becomes a de facto standard by other firms in its industry, and it is never submitted to a formal standards-making process.

[467] See [(Shapiro and Varian, 1998)].

[468] Even so, the LCC is not “open” standard. You can browse the classifications on the LOC site, but to get them packaged as a book or complete digital resource you have to pay for them.

[469] Governments have inherently long time horizons for their actions, they need to serve all citizens fairly and without discrimination, and they (should seek to) minimize cost to taxpayers. Each of these principles is an independent argument for standards and taken together they make a very strong one. Indeed, one the founding goals in the US Constitution is to protect the public interest, and this is enabled in Article I, Section 8 by granting Congress the power to set standards of Weights and Measures” to facilitate commerce. Setting standards is a key role of the National Institute of Standards and Technology(NIST), part of the Department of Commerce, and other departments have similar standards-setting responsibilities and agencies, like the Food and Drug Administration(FDA) in the Department of Health and Social Services. In addition, independent government agencies like the Federal Communications Commission(FCC) and Federal Trade Commission(FTC) set numerous standards that are relevant to information organizing systems. And of course, the Library of Congress(LOC) maintains procedures and standards needed “to sustain and preserve a universal collection of knowledge… for future generations” (LOC.gov/about).

[470] The Sarbanes-Oxley Act is US Public Law 107-204, http://www.sec.gov/about/laws/soa2002.pdf.

The definitive source for the Health Insurance Portability and Accountability Act(HIPAA) is the US Department of Health & Human Services, http://www.hhs.gov/ocr/privacy/hipaa/understanding/index.html.

The definitive source for the Family Educational Rights and Privacy Act(FERPA) is the US Department of Education, http://www2.ed.gov/policy/gen/guid/fpco/ferpa/index.html.

Complying with government regulations like these can be expensive and difficult, and many companies, especially smaller ones, complain about the cost. On the other hand, the argument can be made that investing in a rigorous system for organizing information can provide competitive advantages, turning the compliance burden into a competitive weapon [(Taylor 2006)].

[471] The application of classification and organizing principles more generally to the design of user interfaces to facilitate information access, navigation, and use is often called “Information Architecture.” See [(Morville and Rosenfeld 2006)].

[472] [(Grappone and Couzin 2011)] is a search engine optimization “cookbook” for do-it-yourselfers. See [(Malaga 2008)] for a critique of typical SEO practices.

[473] See [(Gladwell 1996)], [(Schwartz 2005)], [(Underhill 2008)].

[474] The RosettaNet standards are used by thousands of firms as specifications and implementations of business-to-business processes in several industries, especially component manufacturing and electronics. The specifications are defined using a three-level hierarchy of process clusters, segments, and partner interface processes (PIPs) to enable firms to find a level of process abstraction that works best for them. See http://RosettaNet.org.

[475] See [(Gaukroger 2001)] and [(Weinberger 1985)] for an introduction to Bacon’s philosophy, and [(Miksa 1984)] for an analysis of Bacon’s influence on systems of library classification.

[476] [(Svenonius 2000, Ch. 8)].

[477] Very detailed classification of knives are at http://www2.knifecenter.com/knifecenter/kitchen/ and http://kitchenknives.com/.

[478] For example, the introductory text for the Dewey Decimal Classification(DDC) is 38 pages long (http://www.oclc.org/dewey/resources/scholar.htm). A full set of online training modules “focused on the needs of experienced librarians needing Dewey application training” runs 30 hours (http://www.oclc.org/dewey/resources/teachingsite/courses/default.htm).

[479] [(Taylor and Joudrey 2009, p. 392)] define integrity as the stability of notations (class identifiers) in a classification so that resources are never given new notations when the category meaning changes. This is especially pertinent in a physical world where class notations are affixed to resources (books in a traditional library, for example) and where the changing of meaning would necessitate the changing of many numbers.

[480] For example, the Universal Decimal Classification(UDC) intentionally left the main class 4 blank in order to have space for currently unknown subjects on the highest hierarchy level. (http://www.udcc.org/udcsummary/php/index.php). The Library of Congress Classification(LCC) also left space on the highest hierarchical level by not using all letters in the alphabet. Classifications also leave spaces in the enumeration of more specific classes.

[481] [(Rahm and Bernstein 2006)] provide a crisp introduction to the challenges and approaches for changing deployed schemas in databases, conceptual models, ontologies, XML schemas, and software application interfaces. They operate an online bibliography on schema evolution that contains several hundred sources. See http://se-pubs.dbs.uni-leipzig.de/.

[482] See How the Government Measures Unemployment, http://www.bls.gov/cps/cps_htgm.htm from the Department of Labor’s Bureau of Labor Statistics, and a critical commentary about the measurement scheme titled Making 9 Million Jobless Vanish: How the Government Manipulates Unemployment Statistics at http://danielamerman.com/articles/2012/WorkC.html.

[483] [(Monmonier 1996)] is a highly-readable treatment of intentional and inadvertent bias in mapmaking. A web search for “lying with maps” yields a large number of examples. See also When Maps Lie” by Wiseman

[484] [(Friedman and Nissenbaum 1996)]

[485] [(Bowker and Star 2000, p. 4)].

[486] See the Wikipedia entry Race and ethnicity in the United States census, http://en.wikipedia.org/wiki/Race_and_ethnicity_in_the_United_States_Census, and [(Lee 1993)] for arguments against any racial categorization because of the “political motivations and non-scientific character of the classifications.”

[487] [(Feinberg 2012)].

[488] One of the earliest known libraries—at Nippur in Mesopotamia—was small enough that its catalog needed no particular organization: the list of titles in the collection fit onto two easily scanned clay tablets. As collections grew, scribes made it easier to browse the contents of a collection by adding “colophons,” brief descriptions containing a document’s title, author, and place in a sequence of tablets [(Casson 2002)]. A further step was the sorting of works into categories. A temple in the ancient Egyptian city of Edfu placed books into different trunks based on their topics, including royal duties, temple management, and timekeeping, as well as two trunks each for astronomy and protection from crocodiles. The fabled library of Alexandria in ancient Greece used categories based on Aristotle’s three modes of thought: theoretical (e.g. mathematics, physics, metaphysics), practical (ethics, politics, economics), and poetic (poetry, music, and art), plus a fourth “meta-category,” logic, that applied to all of them. Callimachus, one of the library’s directors, created the Pinakes, a library catalog whose top-level distinction was between poetry and prose (followed by genre, author, and work). A few centuries later, librarians in the Chinese Wei and Jin dynasties (third-fifth centuries CE) settled on four major categories—classics, philosophy, history, and literature—that lasted well into the twentieth century. [(Shamurin 1955)] Unlike the Greek system, which classified authors, the Chinese system classified individual works; some authors have suggested that this reflects Western cultures’ greater emphasis on the individual. Medieval libraries adapted ancient practices for their own needs: monastery libraries had separate cabinets for topics such as Bibles, Church history, and Christian poets, and divided their collections into Christian and secular literature (meanwhile, scholars in the intellectually flourishing Muslim world classified knowledge into Muslim and non-Muslim sciences) [(Christ 1984)]. Today’s classification systems reflect both their debt to earlier systems and the biases of their own cultures: the first category of the Universal Decimal Classification, just like Aristotle’s “logic” category, is a meta-category covering organization, documentation, and information science, while the first top-level category of the Chinese Classification System is “Marxism, Leninism, Maoism, and Deng Xiaoping theory.”

[(Taylor and Joudrey 2009, Ch. 3)] is a historical review of library classification. [(Svenonius 2000)] reviews the evolution of the theoretical foundations. [(Kilgour 1998)] focuses on the evolution of the book and the story of the co-evolution of libraries and classification comes along for the ride.

[489] Supermarkets typically carry anywhere from 15,000 to 60,000 SKUs (depending on the size of the store), and may offer a service deli, a service bakery, and/or a pharmacy. 300 standard product categories (http://www.fmi.org/research-resources/supermarket-facts).

[490] http://www.ala.org/tools/libfactsheets/alalibraryfactsheet22.

[491] Dewey Decimal Classification: http://www.oclc.org/dewey/.

[492] https://www.amherst.edu/aboutamherst/history. Today Amherst is aggressively co-ed and secular.

[493] That was not a typo. The “War of 1812” lasted well into 1815. The persistence of an inaccurate name for this war reflects its unique characteristics. Wars (in the English language) are generally named for the location of the fighting or the enemy being fought (the Mexican-American War, the Korean War, the Vietnam War, the Iraq War), or for a particular ideal or ambition (the Revolutionary War, the Civil War). The War of 1812 does not satisfy any of these naming conventions; the war was fought across a huge range of geography from eastern Canada to Louisiana, between a diverse range of groups from Canadians and Native American tribes, with national armies getting involved very late in the war. While nominally fought over freedom the seas, the war quickly morphed into one about territorial ambition in North America. Of course, if the world were a place where people could agree on naming standards for wars, it is likely we would no longer have wars. See http://en.wikipedia.org/wiki/List_of_wars_involving_the_United_States.

[494] [(Miksa 1984, p. 3)].

[495] For additional examples, [(Shirky 2005)].

[496] Cognitive Science has an even harder time finding its proper place in the LCC because it emerged as the intersection of psychology, linguistics, computer science, and other disciplines. Cognitive science books can be found scattered throughout the LCC, with concentrations in BF, P, and QA.

[497] The Book Industry Study Group(BISG) first and foremost is focused on resource description and classification as means to business ends; this purpose contrasts with goals of DDC or LOC. BISG classifications are used for barcodes and shipping labels to support supply chain and inventory management, marketing, and promotion activities. See http://www.bisg.org/.

[498] See [(Pope and Holley 2011)], [(Samuelson 2010)].

[499] What some call the “Perry Rebellion” or the “Dewey Dilemma” began in 2007 when the new Perry Branch Library in Gilbert, Arizona opened with its books classified using the BISAC rather than Dewey classifications. [(Fister 2009)]. This is a highly inflamed controversy that pits advocates of customer service and usability against the library establishment, which despises the idea of turning to retailing as inspiration when designing and operating a library. Even if BISAC gets more widely adopted in public libraries it is unimaginable that it can be used in research libraries.

[500] [(Ranganathan 1967)]. [(Satija 2001)]. See [(Svenonius 2000, p. 174-176)] for a quick introduction.

[501] Wikipedia article at http://en.wikipedia.org/wiki/Colon_classification.

[502] [(Baker 1962)]. The first quote is on page 104; the second one is on page 100. This article contains Condorcet’s 1805 essay in French, but fortunately for us Baker’s analysis is in English, This motivation of Condorcet’s classification scheme sounds like the description of a data warehouse or business intelligence system in which transactional data can be “sliced and diced” into new combinations to answer questions in support of strategic decision-making. See [(Watson and Wixon 2007)].

[503] See Joacim Hansson, Condorcet and the Origins of Faceted Classification, http://documentationandlibrarianship.blogspot.com/2011/02/condorcet-and-origins-of-faceted.html.

[504] LCSH uses facets for Topic, Place, Time, and Form (but they can be ordered in a variety of ways, not as rigidly as PMEST. [(Anderson and Hoffman 2006)] argue for a fully faceted syntax in LCSH.

[505] [(Svenonius 2000, p. 140)].

[506] The Getty AAT is online at http://www.getty.edu/research/tools/vocabularies/aat/index.html.

[507] This section of the thesaurus comes from http://www.getty.edu/vow/AATFullDisplay?find=leather8logic=AND8note=8english=N8prev_page=18subjectid=300193362.

[508] You might have thought that the US was in the new world, but according to wine.com, the new world of wine includes Australia, New Zealand, Argentina, Chile, and South Africa. The geography under the US facet is equally distorted by the uneven distribution of quality wine making regions, so the values of that facet are California. Oregon, Washington, and Other US.

[509] Denton, William. How to Make a Faceted Classification and Put It On the Web Nov. 2003. http://www.miskatonic.org/library/facet-web-howto.html. See also [(Spiteri 1998)].

[510] Should remind you of issues of lexical gap in the section called “The Lexical Perspective”.

[511] Semantic balance is a bit hard to define, but you can often tell when facet values are not balanced. A cookware facet whose values include saucepans, frying pans, stock pots, and pizza pans will not evenly distribute resources across the facets.

[512] See [(Dougherty and Keller 1985)] for the ethnography of blacksmithing, and also [(Norman 2006)], who extends the taskonomy idea to the design of user interfaces for cell phones and other computing devices. You probably have not worked as blacksmith, but you have certainly used taskonomic classification. For example, a student writing a term paper or doing a course project checks out books from the library’s taxonomic classification system (or prints them out from the web) and then organizes them in piles on a desk or on the floor according to the plan for the paper or project. Some of the original classification might persist, but the emphasis clearly shifts toward getting work done. When the task is completed the books go back to the library and are put back into the context-free taxonomy.

[513] See [(Kirsh 1995)] for theoretical motivation and a classification scheme for the “intelligent use of space,” and [(de Leon 2003)] for an example of cooking ethnography.

[514] [(SeeLi, Zheng, and Chen 2006)], [(Juola 2014)], [(Rowling 1997-2007)], [(Rowling as “Galbraith” 2013)],

[515] [(Ellis 2014)]. A compelling demonstration of the need to sample big data streams to ensure against bias is [(Morstatter et al 2013)].


The Discipline of Organizing Copyright © by Robert J. Glushko. All Rights Reserved.

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