Hybrid Method for Automatic Ontology Building from Relational Database

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Relational Databases (RDB) are used as the backend database by most of information systems. RDB encapsulate conceptual model and metadata needed in the ontology construction. Most of existing methods for ontology building from RDB suffer from limitations that prevent advanced database mining for having rich ontologies. In this paper, we propose a hybrid method for automatic ontology building from a RDB. It combines reverse engineering, schema mapping and data analysis techniques. The extracted ontology is refined by renaming the components whose names do not reflect their real meaning. Our method allows (1) recovering lost tables, during the mapping of ER-Model components to relations, by using reverse engineering technique, for the generalization and specialization cases; (2) transforming in the schema mapping phase, the different constructs and cases such as multiple inheritance, n-ary relations, etc.; (3) analyzing stored data to detect disjointness and totalness constraints in hierarchies, and calculating the participation level of tables in n-ary relations. In addition, our method is generic; hence it can be applied to any RDB. Finally, the proposed method was evaluated using two RDBs. The obtained results show that the built ontologies are richer in terms of extracted concepts, taxonomic relationships and ontology's depth
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Semantic Web; Ontology Building; Relational Databases; Reverse Engineering; Schema Mapping; Data Analysis

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