Modern Information Retrieval
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond)
Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond)
Ontology Matching
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
Essential Dimensions of Latent Semantic Indexing (LSI)
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Ontology matching with semantic verification
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
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A crucial piece of semantic web development is the creation of viable ontology matching approaches to ensure interoperability in a wide range of applications such as information integration and semantic multimedia. In this paper, a new approach for ontology matching called IROM (Information Retrieval-based Ontology Matching) is presented. This approach derives the different components of an information retrieval (IR) framework based on the information provided by the input ontologies and supported by ontology similarity measures. Subsequently, a retrieval algorithm is applied to determine the correspondences between the matched ontologies. IROM was tested with ontology pairs taken from two resources for reference ontologies, OAEI and FOAM. The evaluation shows that IROM is competitive with top-ranked matchers on the benchmark test at OAEI campaign of 2009.