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<Paper uid="W98-0713">
  <Title>I I I ! I ! I I I ! I I I I I i I I Lexical Acquisition with WordNet and the Mikrokosmos Ontology</Title>
  <Section position="7" start_page="99" end_page="99" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> Combining traditioaal symbolic heuristics with a statistical approach yields an effective method for augmenting lexical acquisition. This report illusi00 null trated how this facilitated the mapping of Word-Net synsets into a KBMT ontology. The symbolic approach included heuristics for structure matching and intersection-based comparisons. The statistical approach added a similarity test based on synset frequency estimated from a Wall Street Journal corpus. The result is a lexicon acquisition system that produces accurate mappings. This system has been used within the Mikrokosmos project to produce a basic lexicon of over 2000 entries, which were manually validated to ensure correctness. Additional mappings will be possible when the ontology is extended to other domain.% since it now emphasizes business transactions. To allow for broader coverage, future work will address producing mappings that include refinements of the concepts from the ontology.</Paragraph>
    <Paragraph position="1"> Although this work concentrated on nouns, the techniques can be extended to include other types of words. Furthermore, it can be generalized to handle ontology merging, in particular, the problem of merging classification systems. Lehmann (1995) points out that there axe several practical ontologies suitable for merging to be used with a variety of inteliigent applications, such as the Electronic Data Interchange (EDI) standard for descriptions of business transactions (ANSI, 1994). The idea is to take advantage of the time-consuming classification work already done.</Paragraph>
  </Section>
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