Mining query log to assist ontology learning from relational database

Authors:
Jie Zhang;Miao Xiong;Yong Yu
Affiliations:
APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China
Venue:
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Year:
2006

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Abstract

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.