Improving Information Retrieval Effectiveness by Using Domain Knowledge Stored in Ontologies
The huge number of available documents on the Web makes finding relevant ones a challenging task. The quality of results that traditional full-text search engines provide is still not optimal for many types of user queries. Especially the vagueness of natural languages, abstract concepts, semantic relations and temporal issues are handled inadequately by full-text search. Ontologies and semantic metadata can provide a solution for these problems. This work examines how ontologies can be optimally exploited during the information retrieval process, and proposes a general framework which is based on ontology-supported semantic metadata generation and ontology-based query expansion. The framework can handle imperfect ontologies and metadata by combining results of simple heuristics, instead of relying on a “perfect” ontology. This allows integrating results from traditional full-text engines, and thus supports a gradual transition from classical full-text search engines to ontology-based ones.
KeywordsSemantic Relation Query Expansion Vector Space Model Query Execution Test Collection
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