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

Authors:
Lavanya Dhanapalan;Jake Yue Chen
Affiliations:
Department of Computer and Information Science, Purdue University School of Science, Indiana University, Purdue University Indianapolis, Indianapolis, Indiana 46202-5132, USA.;Indiana University School of Informatics, Indiana University, Purdue University Indianapolis, Indianapolis, Indiana 46202-5132, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2007

Citing 7
Cited 1

The Semantic Web: The Roles of XML and RDF

IEEE Internet Computing
Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema

ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
YeastHub: a semantic web use case for integrating data in the life sciences domain

Bioinformatics
Online Predicted Human Interaction Database

Bioinformatics
RDF/RDFS-based Relational Database Integration

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
An integrated computational proteomics method to extract protein targets for Fanconi Anemia studies

Proceedings of the 2006 ACM symposium on Applied computing

Integration of Genomic, Proteomic and Biomedical Information on the Semantic Web

ER '08 Proceedings of the ER 2008 Workshops (CMLSA, ECDM, FP-UML, M2AS, RIGiM, SeCoGIS, WISM) on Advances in Conceptual Modeling: Challenges and Opportunities

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a new ontology-driven semantic data integration approach for post-genome biology studies. Here, a view-based global schema can be automatically generated by merging RDF schemas from local databases. The semantic inconsistency of the merged schema is resolved by the creation of 'RDF ontology maps'. Data querying capability is accomplished with a virtual data repository, in which a D2RQ-based 'relational-to-RDF' map is developed to link schema to the relational database backend. With sample RDQL queries, we demonstrate that our approach significantly simplifies the retrieval of human protein interaction data from different databases containing hundreds of thousands of records.