Si-SEEKER: Ontology-Based Semantic Search over Databases

  • Jun Zhang
  • Zhaohui Peng
  • Shan Wang
  • Huijing Nie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4092)


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.


Semantic Similarity Recall Rate Domain Ontology User Query Precision Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Zhang
    • 1
    • 2
    • 3
  • Zhaohui Peng
    • 1
    • 2
  • Shan Wang
    • 1
    • 2
  • Huijing Nie
    • 1
    • 2
  1. 1.School of InformationRenmin University of ChinaBeijingP.R. China
  2. 2.Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China), MOEBeijingP.R. China
  3. 3.Computer Science and Technology CollegeDalian Maritime UniversityDalianP.R. China

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