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

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.

From Pharmacogenomic Knowledge Acquisition to Clinical Applications: The PharmGKB as a Clinical Pharmacogenomic Biomarker Resource

“…A goal of the PharmGKB is to integrate NLP technology in an automated pipeline to identify relevant scientific publications and extract key pharmacogenomic information [14,15]. The information would be prioritized and displayed to the curators for their approval prior to entry into the knowledge base, thereby maintaining our high level of curated information while significantly increasing the speed and throughput of our current curation pipeline.…”

Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug–drug interaction extraction and classification

“…They used predictions of their improved version of the ChemSpot tool 2 (Rocktäschel et al, 2012) and features derived from (i) Jochem 3 , a dictionary for the identification of small molecules and drugs in text (Hettne et al, 2009), (ii) the PHARE ontology (Coulet et al, 2011) and (iii) the ChEBI ontology (de Matos et al, 2010).…”

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

“…A simple rule-based approach might search for hard coded patterns in the text, e.g., <drug> induces <disease> or <drug> treats <disease>. More sophisticated approaches use linguistic and semantic analyses via part of speech (POS) tagging and parse trees[31, 32]. Machine-learning-based approaches draw on classifiers that operate over POS tags, parse trees, N-grams, terms frequencies, and other textual constructs.…”