Mining Query Log to Assist Ontology Learning from Relational Database

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.