Concept clustering and knowledge integration from a children’s dictionary

Concept clustering and knowledge integration from a children's dict ionary Caroline Barri;~re an(I I,¥ed l'opowich School of (k)nqmting S(:iencc, Si]n(m l,'rascr Ullivcrsity l~urnaby, l~C, (;n.nada, V5A 1S6 ba.rric're,l)Ol)OWi(;ll(@(:s.stTu.(;a Abstract Knowledge structures called Concel)t (?lustering Knowledge (]raphs (CCKGs) are introduced along with a process for their construction from a machine read- able dictionary. C(3K(]s contain multi- ple concepts interrelated through multi- l)le semantic relations together forming a semantic duster represented by a con-. ceptual graph. '1'he knowledge acquisi- tion is performed on a children's first dic- tionary. The concepts inw)lved are gen- eral and typical of a daily lid conw'a'sa- lion. A collection of conceptual clusters together can lbrm the basis of a lexi- cal knowledge base, where each C'(,'l((.~ contains a limited nnmber of highly con- nected words giving usefid information about a particular domain or situation. it Introduction When constructing a l,exieal Knowledge Ilase (1,KB) useful for Natural l,anguage Processing, the source of information from which knowledge is acquired and the structuring of this informa- tion within the LKB are two key issues. Ma- chine Readable Dictionaries (MIH)s) are a good sour(:e of lexical information and have been shown to be al)plical)le to the task of I,KII COllStruction (l)ola.n ct al., 1993; Calzolari, t992; Copestake, [990; Wilks et al., 1989; Byrd et al., 1987). Often though, a localist approaeh is adopted whereby the words are kept in alphabetical order with some representation of their definitions in the form of a template or feature structure. F, flbrt in find- lug cormections between words is seen in work on automatic extraction of sem~mtic relations Dora MRI)s (Ahlswede and Evens, 1988; Alshawi, 1989; Montemagrfi and Vandorwende, 19!32). Addition- ally, effort in finding words that are close seman- tically is seen by the current interest in statisti- cal techniques for word clustering, looking at (-o- occurrences of words in text corpora or dictionar- ies (Church and IIanks, 1989; Wilks et al., 1989; Brown et al., 11992; l'ereira et al., 11995). Inspired by research in the. areas of semantic relations, semantic distance, concept clustering, and using (,once I tual (Ji a l hs (Sowa, 1984) as our knowledge representation, we introduce (;oncept (?lustering I{nowledge Graphs (CCKGs). Each (JCKG will start as a Conceptual Graph represen- tation of a trigger word and will expaud following a search algorit, hm to incorporate related words and ibrm a C'oncept Cn,s(,er. The concept chls- tcr in itself is interesting for tasks such as word disambiguation, but the C(~K(] will give more to that cluster. It will give the relations between the words, making the graph in some aspects similar to a script (Schank and Abelson, 11975). llowever, a CCK(I is generated automaticMly and does not rely on prin,itives but on an unlimited number of concel)ts , showing objects, persons, and actions interacting with each other. This interaction will be set, within a lmrtieular domain, and the trig- ger word should be a key word of the domain to represent. 11' that process would be done for the whole …

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