Abstract
BackgroundAdvances 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.ResultsWe 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.ConclusionsThe 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.
Highlights
Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text
We report on the construction of a relationship ontology and describe its use for integrating and publishing text-mined relationships on the Semantic Web
The PHARE-Knowledge Base (PHARE-KB) The ontology-driven integration process described in the method section takes as input a set of relationships extracted from MEDLINE abstracts and outputs a set of normalized relationships of the form Role(subject, object) represented using entity types and roles defined in PHARE
Summary
Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. Our work is motivated by the need for automated approaches capturing and formalizing knowledge extracted from the literature via manual or computational approaches. Consider for example, that five curators at the Pharmacogenomics Knowledge Base (PharmGKB) manually browse the pharmacogenomics (PGx) literature to curate relationships relevant for storage in the PharmGKB [2]. The result of this curation process is a high quality database queried by clinicians and bioinformaticians. Automatic approaches using Natural Language Processing (NLP) are increasingly utilized [4]
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