Guest Editors' Introduction: A Brain for Humankind
IEEE Intelligent Systems
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Merging ontologies requires interlocking institutional worlds
Applied Ontology
Merging taxonomies under RCC-5 algebraic articulations
Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
Acquiring advanced properties in ontology mapping
Proceedings of the 2nd PhD workshop on Information and knowledge management
Merging ontologies requires interlocking institutional worlds
Applied Ontology
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Ontology merging and alignment is one of the effective methods for ontology sharing and reuse on the Semantic Web. A number of ontology merging and alignment tools have been developed, many of those tools depend mainly on concept (dis)similarity measure derived from linguistic cues. We present in this paper a linguistic information based approach to ontology merging and alignment. Our approach is based on two observations: majority of concept names used in ontology are composed of multiple-word combinations, and ontologies designed independently are, in most cases, organized in very different hierarchical structure even though they describe overlapping domains. These observations led us to a merging and alignment algorithm that utilizes both the local and global meaning of a concept. We devised our proposed algorithm in MoA, an OWL DL ontology merging and alignment tool. We tested MoA on 3 ontology pairs, and human experts followed 93% of the MoA's suggestions.