We have previously discussed the most important principles for creating categories: resource properties, similarity, and goals. When we use one or more of these principles to develop a system of categories, we must make decisions about its depth and breadth. Here, we examine the idea that some levels of abstraction in a system of categories are more basic or natural than others. We also consider how the choices we make affect how we create the organizing system in the first place, and how they shape our interactions when we need to find some resources that are categorized in it.
Category Abstraction and Granularity
We can identify any resource as a unique instance or as a member of a class of resources. The size of this class—the number of resources that are treated as equivalent—is determined by the properties or characteristics we consider when we examine the resources in some domain. The way we think of a resource domain depends on context and intent, so the same resource can be thought of abstractly in some situations and very concretely in others. As we discussed in Resource Description and Metadata, this influences the nature and extent of resource description, and as we have seen in this chapter, it then influences the nature and extent of categories we can create.
Consider the regular chore of putting away clean clothes. We can consider any item of clothing as a member of a broad category whose members are any kind of garment that a person might wear. Using one category for all clothing, that is, failing to distinguish among the various items in any useful or practical way would likely mean that we would keep our clothes in a big unorganized pile.
However, we cannot wear any random combination of clothing items—we need a shirt, a pair of pants, socks, and so on. Clearly, our indiscriminate clothing category is too broad for most purposes. So instead, most people organize their clothes in more fine-grained categories that fit the normal pattern of how they wear clothes.
This tendency to use specific categories instead of broader ones is a general principle that reflects how people organize their experience when they see similar, but not identical, examples or events. This “size principle” for concept learning, as cognitive scientist Josh Tenenbaum describes it, is a preference for the most specific rules or descriptions that fit the observations. For example, if you visit a zoo and see many different species of animals, your conception of what you saw is different than if you visited a kennel that only contained dogs. You might say “I saw animals at the zoo,” but would be more likely to say “I saw dogs at the kennel” because using the broad “animal” category to describe your kennel visit conveys less of what you learned from your observations there.
In “Single Properties” we described an organizing system for the shirts in our closet, so let us talk about socks instead. When it comes to socks, most people think that the basic unit is a pair because they always wear two socks at a time. If you are going to need to find socks in pairs, it seems sensible to organize them into pairs when you are putting them away. Some people might further separate their dress socks from athletic ones, and then sort these socks by color or material, creating a hierarchy of sock categories analogous to the shirt categories in our previous example.
Questions of resource abstraction and granularity also emerge whenever the information systems of different firms, or different parts of a firm, need to exchange information or be merged into a single system. All parties must define the identity of each thing in the same way, or in ways that can be related or mapped to each other either manually or electronically.
For example, how should a business system deal with a customer’s address? Printed on an envelope, “an address” typically appears as a comprehensive, multi-line text object. Inside an information system, however, an address is best stored as a set of distinctly identifiable information components. This fine-grained organization makes it easier to sort customers by city or postal codes, for sales and marketing purposes. Incompatibilities in the abstraction and granularity of these information components, and the ways in which they are presented and reused in documents, will cause interoperability problems when businesses need to share information.
The Universal Business Language(UBL) (mentioned briefly in “Institutional Semantics”) is a library of information components designed to enable the creation of business document models that span a range of category abstraction. UBL comes equipped with XML schemas that define document categories like orders, invoices, payments, and receipts that many people are familiar with from their personal experiences of shopping and paying bills. However, UBL can also be used to design very specific or subordinate level transactional document types like “purchase order for industrial chemicals when buyer and seller are in different countries,” or document types at the other end of the abstraction hierarchy like “fill-in-the-blank” legal forms for any kind of contract.
Bowker and Star point out that there is often a pragmatic tradeoff between precision and validity when defining categories and assigning resources to them, particularly in scientific and other highly technical domains. More granular categories make more precise classification possible in principle, but highly specialized domains might contain instances that are so complex or hard to understand that it is difficult to decide where to organize them.
As an example of this real-world messiness that resists precise classification, Bowker and Star turn to medicine and the World Health Organization’s International Classification of Diseases (ICD), a system of categories for cause-of-death reporting. The ICD requires that every death be assigned to one and only one category out of thousands of possible choices, which facilitates important uses such as statistical reporting for public health research.
In practice, however, doctors often lack conclusive evidence about the cause of a particular death, or they identify a number of contributing factors, none of which could properly be described as the sole cause. In these situations, less precise categories would better accommodate the ambiguity, and the aggregate data about causes of death would have greater validity. But doctors have to use the ICD’s precise categories when they sign a death certificate, which means they sometimes record the wrong cause of death just to get their work done.
It might seem counterintuitive, but when a system of human-generated categories is too complex for people to interpret and apply reliably, computational classifiers that compute statistical similarity between new and already classified items can outperform people.
Basic or Natural Categories
Category abstraction is normally described in terms of a hierarchy of superordinate, basic, and subordinate category levels. “Clothing,” for example, is a superordinate category, “shirts” and “socks” are basic categories, and “white long-sleeve dress shirts” and “white wool hiking socks” are subordinate categories. Members of basic level categories like “shirts” and “socks” have many perceptual properties in common, and are more strongly associated with motor movements than members of superordinate categories. Members of subordinate categories have many common properties, but these properties are also shared by members of other subordinate categories at the same level of abstraction in the category hierarchy. That is, while we can identify many properties shared by all “white long-sleeve dress shirts,” many of them are also properties of “blue long-sleeve dress shirts” and “black long-sleeve pullover shirts.”
Psychological research suggests that some levels of abstraction in a system of categories are more basic or natural than others. Anthropologists have also observed that folk taxonomies invariably classify natural phenomena into a five- or six-level hierarchy, with one of the levels being the psychologically basic or “real” name (such as “cat” or “dog”), as opposed to more abstract names (e.g. “mammal”) that are used less in everyday life. An implication for organizing system design is that basic level categories are highly efficient in terms of the cognitive effort they take to create and use. A corollary is that classifications with many levels at different abstraction levels may be difficult for users to navigate effectively.
The Recall / Precision Tradeoff
The abstraction level we choose determines how precisely we identify resources. When we want to make a general claim, or communicate that the scope of our interest is broad, we use superordinate categories, as when we ask, “How many animals are in the San Diego Zoo?” But we use precise subordinate categories when we need to be specific: “How many adult emus are in the San Diego Zoo today?”
If we return to our clothing example, finding a pair of white wool hiking socks is very easy if the organizing system for socks creates fine-grained categories. When resources are described or arranged with this level of detail, a similarly detailed specification of the resources you are looking for yields precisely what you want. When you get to the place where you keep white wool hiking socks, you find all of them and nothing else. On the other hand, if all your socks are tossed unsorted into a sock drawer, when you go sock hunting you might not be able to find the socks you want and you will encounter lots of socks you do not want. But you will not have put time into sorting them, which many people do not enjoy doing; you can spend time sorting or searching depending on your preferences.
If we translate this example into the jargon of information retrieval, we say that more fine-grained organization reduces recall, the number of resources you find or retrieve in response to a query, but increases the precision of the recalled set, the proportion of recalled items that are relevant. Broader or coarse-grained categories increase recall, but lower precision. We are all too familiar with this hard bargain when we use a web search engine; a quick one-word query results in many pages of mostly irrelevant sites, whereas a carefully crafted multi-word query pinpoints sites with the information we seek. We will discuss recall, precision, and evaluation of information retrieval more extensively in Interactions with Resources.
This mundane example illustrates the fundamental tradeoff between organization and retrieval. A tradeoff between the investment in organization and the investment in retrieval persists in nearly every organizing system. The more effort we put into organizing resources, the more effectively they can be retrieved. The more effort we are willing to put into retrieving resources, the less they need to be organized first. The allocation of costs and benefits between the organizer and retriever differs according to the relationship between them. Are they the same person? Who does the work and who gets the benefit?
Category Audience and Purpose
The ways in which people categorize depend on the goals of categorization, the breadth of the resources in the collection to be categorized, and the users of the organizing system. Suppose that we want to categorize languages. Our first step might be determining what constitutes a language, since there is no widespread agreement on what differentiates a language from a dialect, or even on whether such a distinction exists.
What we mean by “English” and “Chinese” as categories can change depending on the audience we are addressing and what our purpose is, however. A language learning school’s representation of “English” might depend on practical concerns such as how the school’s students are likely to use the language they learn, or which teachers are available. For the purposes of a school teaching global languages, and one of the standard varieties of English (i.e., those associated with political power), or an amalgamation of several standard varieties, might be thought of as a single instance (“English”) of the category “Languages.”
Similarly, the category structure in which “Chinese” is situated can vary with context. While some schools might not conceptualize “Chinese” as a category encompassing multiple linguistic varieties, but rather as a single instance within the “Languages” category, another school might teach its students Mandarin, Wu, and Cantonese as dialects within the language category “Chinese,” that are unified by a single standard writing system. In addition, a linguist might consider Mandarin, Wu, and Cantonese to be mutually unintelligible, making them separate languages within the broader category “Chinese” for the purpose of creating a principled language classification system.
If people could only categorize in a single way, the Pyramid game show, where contestants guess what category is illustrated by the example provided by a clue giver, would pose no challenge. The creative possibilities provided by categorization allow people to order the world and refer to interrelationships among conceptions through a kind of allusive shorthand. When we talk about the language of fashion, we suggest that in the context of our conversation, instances like “English,” “Chinese,” and “fashion” are alike in ways that distinguish them from other things that we would not categorize as languages.
(Tenenbaum 2000) argues that this preference for the most specific hypothesis that fits the data is a general principle of Bayesian learning with random samples.
Consider what happens if two businesses model the concept of “address” in a customer database with different granularity. One may have a coarse “Address” field in the database, which stores a street address, city, state, and Zip code all in one block, while the other stores the components “StreetAddress,” “City,” and “PostalCode” In separate fields. The more granular model can be automatically transformed into the less granular one, but not vice versa (Glushko and McGrath 2005).
Statistician and baseball fan Nate Silver rejected a complex system that used twenty-six player categories for predicting baseball performance because “it required as much art as science to figure out what group a player belonged in.” (Silver 2012, p, 83). His improved system used the technique of “nearest neighbor” analysis to identify current baseball players whose minor league statistics were most similar to the current minor league players being evaluated. (See “Categories Created by Clustering”).
Silver later became famous for his extremely accurate predictions of the 2008 US presidential elections. He is the founder and editor of the FiveThirtyEight blog, so named because there are 538 senators and representatives in the US Congress.
(Rosch 1999) calls this the principle of cognitive economy, that “what one wishes to gain from one’s categories is a great deal of information about the environment while conserving finite resources as much as possible. [...] It is to the organism’s advantage not to differentiate one stimulus from another when that differentiation is irrelevant to the purposes at hand.” (Pages 3-4.)
For example, some linguists think of “English” as a broad category encompassing multiple languages or dialects, such as “Standard British English,” “Standard American English,” and “Appalachian English.”
If we are concerned with linguistic diversity and the survival of minority languages, we might categorize some languages as endangered in order to mobilize language preservation efforts. We could also categorize languages in terms of shared linguistic ancestors (“Romance languages,” for example), in terms of what kinds of sounds they make use of, by how well we speak them, by regions they are commonly spoken in, whether they are signed or unsigned, and so on. We could also expand our definition of the languages category to include artificial computer languages, or body language, or languages shared by people and their pets—or thinking more metaphorically, we might include the language of fashion.