Mining for Lexons: Applying Unsupervised Learning Methods to Create Ontology Bases

摘要

Ontologies in current computer science parlance are computer based resources that represent agreed domain semantics. This paper first introduces ontologies in general and subsequently, in particular, shortly outlines the DOGMA ontology engineering approach that separates "atomic" conceptual relations from "predicative" domain rules. In the main part of the paper, we describe and experimentally evaluate work in progress on a potential method to automatically derive the atomic conceptual relations mentioned above from a corpus of English medical texts. Preliminary outcomes are presented based on the clustering of nouns and compound nouns according to co-occurrence frequencies in the subject-verb-object syntactic context.