WordNet: a lexical database for English
Communications of the ACM
An algorithm for suffix stripping
Readings in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Constructing virtual documents for ontology matching
Proceedings of the 15th international conference on World Wide Web
Ontology Matching
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
Falcon-AO: A practical ontology matching system
Web Semantics: Science, Services and Agents on the World Wide Web
Cross species analysis of microarray expression data
Bioinformatics
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size
Web Semantics: Science, Services and Agents on the World Wide Web
An adaptive ontology mapping approach with neural network based constraint satisfaction
Web Semantics: Science, Services and Agents on the World Wide Web
An Asymmetric Similarity Measure for Ontologies Based on the Feature Contrast Model
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
Bioinformatics
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Semantic querying over knowledge in biomedical text corpora annotated with multiple ontologies
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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The biomedical sciences is one of the few domains where ontologies are widely being developed to facilitate information retrieval and knowledge sharing, but there still remains the problem that applications using different ontologies cannot share knowledge without explicit references between overlapping concepts. Ontology alignment is the task of identifying such equivalence relations between concepts across ontologies. Its application to the biomedical domain should address two open issues: (1) determining the equivalence of concept-pairs which have overlapping terms in their names, and (2) the high run-time required to align large ontologies which are typical in the biomedical domain. To address them, we present a novel approach, named the Biomedical Ontologies Alignment Technique (BOAT), which is state-of-the-art in terms of F-measure, precision and speed. A key feature of BOAT is that it considers the informativeness of each component word in the concept labels, which has significant impact on biomedical ontologies, resulting in a 12.2% increase in F-measure. Another important feature of BOAT is that it selects for comparison only concept pairs that show high likelihoods of equivalence, based on the similarity of their annotations. BOAT's F-measure of 0.88 for the alignment of the mouse and human anatomy ontologies is on par with that of another state-of-the-art matcher, AgreementMaker, while taking a shorter time.