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Lightweight predicate extraction for patient-level cancer information and ontology development
Muhammad Amith1, Hsing-Yi Song1, Yaoyun Zhang1
1School of Biomedical Informatics, University of Texas Health Science Center, Fannin Street, Houston, USA.
BMC Medical Informatics and Decision Making
|July 13, 2017
Summary
Open Information Extraction (OIE) simplifies ontology development for public health by extracting knowledge triples from patient-centric sources. This automated approach achieves 80-89% precision, making ontology creation more accessible.
Area of Science:
- Biomedical Informatics
- Natural Language Processing
- Knowledge Engineering
Background:
- Ontology development is resource-intensive, posing challenges for novices.
- Automated natural language processing tools can aid ontology creation.
- Focus on public health ontologies using patient-centric MedlinePlus data for HPV-related cancers.
Purpose of the Study:
- Demonstrate the utility of lightweight Open Information Extraction (OIE) for ontology seeding.
- Develop a custom application to facilitate knowledge triple extraction.
- Enable novices to contribute to public health knowledgebase development.
Main Methods:
- Utilized a lightweight Open Information Extraction (OIE) tool.
- Developed a custom application interfacing with an information extraction library.
- Extracted knowledge triples from patient-centric MedlinePlus sources on HPV-causing cancers.
Main Results:
- Achieved 80-89% precision in knowledge triple extraction.
- Generated accurate triples suitable for OWL/RDF conversion.
- Demonstrated the feasibility of automated ontology seeding.
Conclusions:
- Open Information Extraction (OIE) provides an effective and accessible method for ontology development.
- Automated extraction significantly reduces the resource and time investment.
- Facilitates the creation of public health knowledgebases.
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