# Taxonomies

As their central role in WordNet suggests, the hierarchic BT/NT or hypernymy relations are especially important. Because of the constant analytic pressure of the Western intellectual tradition, topics continue to be refined into smaller topics, which the next generation of scholars immediately refine further. This has led to a wide range of classification taxonomies associated with various social groups of scholars and scientists.

Figure (figure) shows several different sources from which indexing information might be obtained. The most important single source of subject indexing is the Library of Congress (LoC). This indexing system is the basis of most large libraries because its indexing scheme covers all disciplines. For exactly the same reason, however, the indexers at LoC have far too much to do, and the resulting indices are admittedly crude. Partly as a response to the lack of adequate indexing structures, various professional groups have developed their own taxonomies to help organize the information within their particular technical specialty. For example, the Association of Computing Machinery has developed an ACM Computing Reviews Classification for use by its {\em Computing Reviews} publication [REF396] . This taxonomy is much more specific, and therefore more widely used by computer specialists. It lacks, however, many of the advantages of the LoC system. In general, the keywords are assigned by the authors rather than by trained librarians. The system is rarely if ever integrated into the operations of libraries. And while the indexing structure is much more refined than that of the LoC, it is still too crude for most research currently going on in any one sub-specialty. This has caused some practitioners in various sub-specialties to develop their own extensions. For example, David Waltz was commissioned by Scientific Data-Link to extend the ACM's {\em Computing Reviews} taxonomy for the sub-specialty of artificial intelligence (AI) [REF331] . Waltz's extension is extremely refined and helpful to AI practitioners. At the same time, it is even more {\em ad hoc}, its sponsoring institution'' has less impact, and consequently it is even less well accepted within libraries.

All three of these indexing systems are examples of TOP-DOWN KNOWLEDGE STRUCTURES. That is, they are developed by various social institutions as prescriptive languages used to represent the consensus opinion as to how information is to be organized. Such consensual'' knowledge structures are critical if individuals are to {\em share} information. Each indexing system represents a compromise between increased scope and diminished resolution. Increased scope brings along with it broader acceptance and adherence. These advantages are bought at the expense of acceptance by users actively involved in technical specialties.

The central role of hierarchic BT/NT relations in organizing keyword vocabularies should make us especially concerned with a precise semantics for this relationship. Most would agree that if {A} is a broader-term than {\tt B}, then {\tt B} is a'' {\tt A}. But as knowledge engineers within AI have known for a long time, the ubiquitous {\tt IS\_A} relation admits a number of interpretations which can support much different types of inference [REF55] [REF97] . In general, the BT/NT relation seems to correspond most closely to the a kind of'' implication relating predicates {\tt A} and {\tt B}: (\forall x) B(x) \rightarrow A(x)

Earlier generations of Internet users participated in the construciton of the extensive UseNet hierarchy of discussion boards, on topics from {\tt ALT.SEX.FETISH.ROBOTS} to {\tt COMP.SYS.MAC.OOP.TCL}; see Section §7.4.5 for an example of the use of this hierarchy in text classification tasks. These days the most widely know taxonomies are WWW DIRECTORIES such as Yahoo! , where employees of this company have constructed a hierarchy, primarily of places to spend money. One of the most exciting recent developments is the development of \defn{collaborative classification} efforts such as the Open Directory Project (DMOZ) which involve large communities of experts, each working on making sense within their own are of expertise.

FOA © R. K. Belew - 00-09-21