Paper Info
Title
Integration and publication of heterogeneous text-mined relationships on the Semantic Web.
Abstract
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.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.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.
Year
DOI
Venue
2011
10.1186/2041-1480-2-S2-S10
J. Biomedical Semantics
Keywords
Field
DocType
text mining,ontology,biomedical research,bioinformatics,pharmacogenomics
Data science,Ontology,Data mining,Text mining,Question answering,Computer science,Semantic Web,Natural language,Dependency graph
Journal
Volume
Issue
ISSN
2 Suppl 2
S-2
2041-1480
Citations 
PageRank 
References 
21
0.76
11
Authors
6
Name
Order
Citations
PageRank
Adrien Coulet116516.04
Yael Garten21838.73
Michel Dumontier389893.35
Russ B. Altman42500456.07
Mark A Musen57141766.74
Nigam Shah61380107.49