| dc.description.abstract |
Finding relevant concepts from a corpus of ontologies is useful in many scenarios, such as document classification, web page annotation, and automatic ontology population. Many millions of concepts are contained in a large number of ontologies across diverse domains. A SPARQL-based query demands the knowledge of the structure of ontologies and the query language, whereas user-friendlier and, simpler keyword-based approaches suffer from false positives. This is because concept descriptions in ontologies may be ambiguous and may overlap. In this paper, we propose a keyword-based concept search framework, which (1) exploits the structure and semantics in ontologies, by constructing contexts for each concept; (2) generates the interpretations of a query; and (3) balances the relevance and diversity of search results. A comprehensive evaluation against the domain-specific BioPortal and the general-purpose Falcons on widely-used performance metrics demonstrates that our system outperforms both. |
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