Abstract
Keyword Search Over Relational Databases(KSORD) has been widely studied. While keyword search is helpful to access databases, it has inherent limitations. Keyword search doesn’t exploit the semantic relationships between keywords such as hyponymy, meronymy and antonymy, so the recall rate and precision rate are often dissatisfactory. In this paper, we have designed an ontology-based semantic search engine over databases called Si-SEEKER based on our i-SEEKER system which is a KSORD system with our candidate network selection techniques. Si-SEEKER extends i-SEEKER with semantic search by exploiting hierarchical structure of domain ontology and a generalized vector space model to compute semantic similarity between a user query and annotated data. We combine semantic search with keyword search over databases to improve the recall rate and precision rate of the KSORD system. We experimentally evaluate our Si-SEEKER system on the DBLP data set and show that Si-SEEKER is more effective than i-SEEKER in terms of the recall rate and precision rate of retrieval results.
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Zhang, J., Peng, Z., Wang, S., Nie, H. (2006). Si-SEEKER: Ontology-Based Semantic Search over Databases. In: Lang, J., Lin, F., Wang, J. (eds) Knowledge Science, Engineering and Management. KSEM 2006. Lecture Notes in Computer Science(), vol 4092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811220_51
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DOI: https://doi.org/10.1007/11811220_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37033-8
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