MODEL-THEORETIC METHODS OF EXTRACTION OF KNOWLEDGE ON THE MEANING OF CONCEPTS FROM THE NATURAL LANGUAGE TEXTS
The paper is devoted to the development of model-theoretic methods of knowledge extraction from the natural language texts and, in particular, methods of extraction of concept definitions which are complete relative to the fixed context. The context may be determined by ambient ontology as well as by a set of precedents. The completeness of definitions is considered modulo a special set of "interesting" sentences. The extracted knowledge is represented in the form of fragments of atomic diagrams of algebraic systems. Algorithms of mapping some quantifier-free sentences of predicate logic onto Description Logics (DL) as well as of their further mapping onto OWL are developed and implemented. It gives a possibility to generate new knowledge based on existing knowledge that already contained in the ontology by using automated logical reasoning.