Automatic semantic mapping between query terms and controlled vocabulary through using wordnet and wikipedia

Query log analysis can provide valuable information for improving information retrieval performance. This paper reports 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 was 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 involved 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 terms. The two sources, WordNet and Wikipedia, were 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.