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An Automatic Method for Constructing Domain-Specific Ontology Resources
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An Automatic Method for Constructing Domain-Specific Ontology Resources
Data flow across multiple independent applications and further natural language analysis both require the establishment of a common foundation of terms and relations. Such a foundation can provide in-depth understanding of term equivalence within a domain sublanguage, and serve as a model of concept relations and dependencies. In this paper we discuss a domain-independent, corpus-based method for dictionary-less automatic extraction of ontological knowledge from domain-specific unannotated documents. We present the architecture, algorithms, and results for OntoStruct - a system that uses machine learning and statistical techniques to analyze text sources, discover terms, link equivalent terms into concepts, and learn both hierarchical and non-hierarchical conceptual relations. We report on OntoStruct's results in constructing domain-specific ontological resources and empirical evaluation of their quality.
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