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Evaluation of lexical methods for detecting relationships between concepts from multiple ontologies.
Helen L Johnson1, K Bretonnel Cohen, William A Baumgartner
1Center for Computational Pharmacology, University of Colorado School of Medicine, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|November 11, 2006
Summary
We discovered over 91,000 relationships between ontologies using text matching methods. Careful evaluation of these strategies, including exact string matching, is recommended for accurate biological data integration.
Area of Science:
- Bioinformatics
- Computational Biology
- Ontology Engineering
Background:
- Gene Ontology (GO) and other Open Biomedical Ontologies (OBO) are crucial for standardizing biological data.
- Integrating information across different ontologies facilitates comprehensive biological knowledge discovery.
- Challenges exist in automatically identifying and validating relationships between diverse ontological resources.
Purpose of the Study:
- To discover and quantify relationships between the Gene Ontology and three other OBO ontologies: ChEBI, Cell Type, and BRENDA Tissue.
- To evaluate the correctness of different text-matching strategies for ontology relationship discovery.
- To provide a comprehensive set of discovered relationships for use in biological research.
Main Methods:
- Utilized the Lucene information retrieval library for implementing text-matching strategies.
- Employed exact term matching, stemming, and synonym inclusion for relationship discovery.
- Engaged domain experts to evaluate the accuracy and validity of proposed ontology relationships.
Main Results:
- Successfully discovered 91,385 relationships between the Gene Ontology and the ChEBI, Cell Type, and BRENDA Tissue ontologies.
- Demonstrated that different text-matching methods yield a wide range of correctness.
- Identified the need for rigorous validation of all automated relationship discovery techniques.
Conclusions:
- Automated methods, including exact string matching, can uncover a substantial number of cross-ontology relationships.
- The correctness of discovered relationships varies significantly based on the matching strategy employed.
- Thorough evaluation of matching algorithms is essential before their application in biological data integration projects.
- The full dataset of discovered relationships is publicly available for research purposes.
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