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A case study of integrating protein interaction data using semantic web technology.

Lavanya Dhanapalan¹, Jake Yue Chen

¹Department of Computer and Information Science, Purdue University School of Science, Indiana University, Purdue University Indianapolis, Indianapolis, Indiana 46202-5132, USA. ldhanapa@iupui.edu

International Journal of Bioinformatics Research and Applications

|December 1, 2007

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This study introduces an ontology-driven approach to integrate post-genome biology data. It simplifies retrieving human protein interaction data from multiple databases using a virtual data repository.

Area of Science:

Bioinformatics
Data Integration
Semantic Web Technologies

Background:

Post-genome biology generates vast, heterogeneous datasets.
Integrating data from diverse sources is challenging.
Existing methods lack semantic consistency and efficient querying.

Purpose of the Study:

To develop an ontology-driven semantic data integration approach.
To address semantic inconsistencies in merged schemas.
To simplify human protein interaction data retrieval.

Main Methods:

Automatic generation of a view-based global schema by merging RDF schemas.
Resolution of semantic inconsistencies using 'RDF ontology maps'.
Implementation of a virtual data repository with a D2RQ-based 'relational-to-RDF' map for querying.

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Main Results:

Successfully integrated data from multiple local databases.
Demonstrated simplified retrieval of human protein interaction data.
Handled large datasets containing hundreds of thousands of records efficiently.

Conclusions:

The proposed approach effectively integrates post-genome biology data.
Ontology maps and virtual repositories enhance data accessibility.
This method significantly improves querying of complex biological datasets.