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

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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

Positive Aspects: No data available now.

Negative Aspects: No data available now.

Limitations: No data available now.

Challenges: No data available now.

Proposes Algorithm: No data available now.

Methodology: No data available now.

Requirements: No data available now.

Limitations: No data available now.

Implementations

Download-page: No data available now.

Access API: No data available now.

Information Representation: No data available now.

Data Catalogue: {{{Catalogue}}}

Runs on OS: No data available now.

Vendor: No data available now.

Uses Framework: No data available now.

Has Documentation URL: No data available now.

Programming Language: Java

Version: No data available now.

Platform: No data available now.

Toolbox: No data available now.

GUI: No

Research Problem

Subproblem of: No data available now.

RelatedProblem: No data available now.

Motivation: No data available now.

Evaluation

Experiment Setup: No data available now.

Evaluation Method : No data available now.

Hypothesis: No data available now.

Description: No data available now.

Dimensions: No data available now.

Benchmark used: No data available now.

Results: No data available now.

Access APINo data available now. +
Event in seriesICIW +
Has BenchmarkNo data available now. +
Has ChallengesNo data available now. +
Has DataCatalouge{{{Catalogue}}} +
Has DescriptionNo data available now. +
Has DimensionsNo data available now. +
Has DocumentationURLhttp://No data available now. +
Has Downloadpagehttp://No data available now. +
Has EvaluationNo data available now. +
Has EvaluationMethodNo data available now. +
Has ExperimentSetupNo data available now. +
Has GUINo +
Has HypothesisNo data available now. +
Has ImplementationRDB2ONT +
Has InfoRepresentationNo data available now. +
Has LimitationsNo data available now. +
Has NegativeAspectsNo data available now. +
Has PositiveAspectsNo data available now. +
Has RequirementsNo data available now. +
Has ResultsNo data available now. +
Has SubproblemNo data available now. +
Has VersionNo data available now. +
Has abstractThis paper describes a framework that uses
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.
se systems using the generated ontologies. +
Has approachNo data available now. +
Has authorsQuang Trinh +, Ken Barker + and Reda Alhajj +
Has conclusionIn this paper, an algorithm and tool for g
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.
epts defined in the individual ontologies. +
Has motivationNo data available now. +
Has platformNo data available now. +
Has problemTransforming Relational Databases into Semantic Web +
Has relatedProblemNo data available now. +
Has subjectGenerating OWL Ontologies From Relational Database Systems +
Has vendorNo data available now. +
Has year2006 +
ImplementedIn ProgLangJava +
Proposes AlgorithmNo data available now. +
RunsOn OSNo data available now. +
TitleRDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems +
Uses FrameworkNo data available now. +
Uses MethodologyNo data available now. +
Uses ToolboxNo data available now. +