Introduction to WordNet

(Fellbaum, 1998, p. 6)
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People often draw the distinction between word (or lexical) knowledge and world (or encyclopedia) knowledge. Two kinds of books reflect this distinction: dictionaries are generally the repository of word knowledge, and encyclopedias the repository of world knowledge. The boundaries between the two are in fact fuzzy. We can probably all agree that knowing that hitting someone is a hostile act constitutes world knowledge, whereas knowing that the verb hit is a strong verb, that it is more or less synonymous with strike, and that it takes a direct argument constitutes word knowledge. It is less clear how knowing that the direct argument of hit must be a solid object (as opposed to, say, a gas) should be classified. But there is no question that understanding the meaning and uses of a word requires both kinds of knowledge. Kay (1989) points out that our mental lexicon must contain both word and world knowledge, and that only those lexicons that contain both kinds of knowledge are likely to yield successful applications. Encyclopedic knowledge is vast and difficult to assemble, although Lenat and his colleagues (Lenat and Guha 1990) have been working for a number of years on constructing an intelligent system that has all facts pertaining to words and concepts at its disposition. WordNet does not attempt to include encyclopedic knowledge, although the definitions that accompany the synonym sets (synsets) provide information about the concepts that is not strictly part of their lexical structure. G. A. Miller points out in the foreword that, although WordNet's synsets were initially intended to contain no information other than pointers to other synsets, it was found that definitions and illustrative sentences were needed to distinguish closely related synsets whose members were polysemous. And in the case of many technical concepts, such as uncommon plants and animals, lexical and encyclopedic knowledge are merged in the definitions, which are likely to constitute all the knowledge everyday speakers need to access.
Knowledge beyond htat usually given in dictionaries is needed for reasoning and making inferences about states and events referred to by sentences. Harabagiu and Moldovan (chapter 16) show how the information in WordNet can be augmented to create a knowledge base that can be successfully used for inferencing.
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