This article is part of the supplement: IEEE 7th International Conference on Bioinformatics and Bioengineering at Harvard Medical School
Use artificial neural network to align biological ontologies
1 Dept Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA
2 Siemens Corporate Research, Princeton, NJ 08540, USA
3 Dept Computer Science & Engineering, University of South Carolina, Columbia, SC 29208, USA
BMC Genomics 2008, 9(Suppl 2):S16 doi:10.1186/1471-2164-9-S2-S16Published: 16 September 2008
Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics.
In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others.
The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.