Mining Query Log to Assist Ontology Learning from Relational Database

  • Jie Zhang
  • Miao Xiong
  • Yong Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3841)


Ontology learning plays a significant role in migrating legacy knowledge base into the Semantic Web. Relational database is the vital source that stores the structured knowledge today. Some prior work has contributed to the learning process from relational database to ontology. However, a majority of the existing methods focus on the schema dimension, leaving the data dimension not well exploited. In this paper we present a novel approach that exploits the data dimension by mining user query log to glorify the ontology learning process. In addition, we propose a set of rules for schema extraction which serves as the basis of our theme. The presented approach can be applied to a broad range of today’s relational data warehouse.


Relational Database Schema Extraction Formal Context Formal Concept Analysis Ontology Learning 
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

  • Jie Zhang
    • 1
  • Miao Xiong
    • 1
  • Yong Yu
    • 1
  1. 1.APEX Data and Knowledge Management Lab, Department of Computer Science and EngineeringShanghai JiaoTong UniversityShanghaiP.R. China

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