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This article is part of the supplement: Proceedings of the Bio-Ontologies Special Interest Group Meeting 2010

Open Access Proceedings

Integration and publication of heterogeneous text-mined relationships on the Semantic Web

Adrien Coulet123*, Yael Garten23, Michel Dumontier4, Russ B Altman235, Mark A Musen2 and Nigam H Shah2

Author Affiliations

1 LORIA – INRIA Nancy – Grand-Est, Campus Scientifique - BP 239 - 54506 Vandoeuvre-lès-Nancy Cedex, France

2 Department of Medicine, 300 Pasteur Drive, Mail Code 5110, Stanford University, Stanford, CA, 94305, USA

3 Department of Genetics, Mail Code 5120, Stanford University, Stanford, CA, 94305, USA

4 Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada, K1S5B6

5 Department of Bioengineering, 318 Campus Drive, Mail Code 5444, Stanford University, Stanford, CA, 94305, USA

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Journal of Biomedical Semantics 2011, 2(Suppl 2):S10  doi:10.1186/2041-1480-2-S2-S10

Published: 17 May 2011

Abstract

Background

Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering.

Results

We report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining. These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles. Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology. The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network.

Conclusions

The PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics. The PHARE ontology forms the foundation of a knowledge base named PHARE-KB. Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions. PHARE is available at http://purl.bioontology.org/ontology/PHARE webcite.