RDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems

RDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems
RDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems
Bibliographical Metadata
Subject:	Generating OWL Ontologies From Relational Database Systems
Year:	2006
Authors:	Quang Trinh, Ken Barker, Reda Alhajj
Venue	ICIW
Content Metadata
Problem:	Transforming Relational Databases into Semantic Web
Approach:	No data available now.
Implementation:	RDB2ONT
Evaluation:	No data available now.

Abstract

This paper describes a framework that uses the Semantic Web infrastructure to address semantic interoperability between relational database systems in large-scale environments and at multiple levels of granularities. Given a relational database system, we describe a formal algorithm to use the relational database Rs meta-data and structural constraints to construct its OWL ontology while preserving the structural constraints of the underlying relational database system. The generated ontology is described using and conforming to a set of vocabularies defined in an ontology that describes relational database systems on the web. Using this set of vocabularies guarantee that applications on the web can work with data instances that conformed to a set of known vocabularies and structures. Finally, we describe our prototype and how semantic conflicts are resolved between multiple relational database systems using the generated ontologies.

Conclusion

In this paper, an algorithm and tool for generating OWL ontologies from relational database systems is presented. The tool, RDB2ONT, helps the domain experts to quickly generate and publish OWL ontologies describing the underlying relational database systems while preserving their structural constraints. The generated ontologies are constructed using a set of vocabularies and structures defined in schema that describes relational database systems on the web so they guarantees that user applications can work with data instances that conformed to a set of known vocabularies and structures. The generated ontologies provide a standardized and meaningful way for describing the underlying relational database systems so they “bridge” the semantic gaps between the ontologies describing relational database systems and/or the ontologies describing other data sources on the web such as flat-files, semi-structures, etc. Concepts in OWL ontologies can be defined at multiple levels of granularities thus the generated OWL ontologies can be used to address the semantic heterogeneity problem at multiple levels. Evolutions of database systems in large-scale environments are inevitable so by using the RDB2ONT tool, OWL ontologies can be re-generated with little effort from the domain experts thus speed up the process of facilitating data in the underlying relational database systems with other data sources on the web. Although the generated OWL ontologies provide the explicit meaning of concepts and their semantic relationships between related concepts, there are still many open research questions that need to be addressed. One of the questions is how to merge the generated OWL ontologies into an integrated OWL ontology so that common views of concepts can be achieved? This would allow users to pose queries on the common views of concepts rather than the concepts defined in the individual ontologies.

Future work

[[has future work:=Merging OWLontologies will also require the resolution of the structural differences between OWL ontologies. Another question is how can existing OWL ontology inference engines such as Jena 2 or Pellet [8] can be used to infer the semantic relationships between concepts defined in multiple OWL ontologies at multiple levels of granularities? The result of this research is important since it would enable users to combine related data from multiple data sources at multiple levels of granularities. In the real world, different organizations are likely to use different data models, thus tools for generating OWL ontologies automatically and dynamically from other data models (e.g., flat-files, object-oriented, etc.) are also needed. As mentioned previously, having different OWL ontologies describing different data models will allow users to relate semantically related data in different data models together thus enabling these different data sources to interoperate with each other semantically.]]

Approach

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Implementations

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Programming Language: Java

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Evaluation

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