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Automatic Semantic Mapping between Query Terms and Controlled Vocabulary through Using WordNet and Wikipedia

Xiaozhong Liu, Jian Qin, Miao Chen and Ji-Hong Park

ASIS&T 2008 Annual Meeting (AM08 2008)
Columbus, Ohio, October 24-29, 2008


Query log analysis can provide valuable information for improving information retrieval. This paper reports the findings from a query log mining project, in which query terms falling in the very long tail of low to zero similarity (with the controlled vocabulary) scores were analyzed by using similarity algorithms. The query log data were collected from the Gateway to Educational Materials (GEM). The limited number of terms in the GEM controlled vocabulary was a major source for the long tail of low or zero similarity scores for the query terms. To mitigate this limitation, we employed a strategy that involves using the general-purpose (domain-independent) ontology WordNet and community-created Wikipedia as the bridge to establish semantic relatedness between GEM controlled vocabulary (as well as new concept classes identified by human experts) and user query. The two sources are complementary in mapping different types of query terms. A combination of both sources achieved a modest rate of mapping accuracy. The paper discussed the implications of the findings for automatic semantic analysis and vocabulary development and validation.

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