Elements of relational database theory
Handbook of theoretical computer science (vol. B)
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
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
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Ontology mapping: the state of the art
The Knowledge Engineering Review
Reference reconciliation in complex information spaces
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond)
Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond)
Bridging the gap between OWL and relational databases
Proceedings of the 16th international conference on World Wide Web
An FCA-based solution for ontology mediation
Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Journal on data semantics X
What's happening in semantic web: and what FCA could have to do with it
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
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In this paper, we propose a solution to the problem of merging ontologies when instances associated to two source ontologies are available. The solution we propose is based on Formal Concept Analysis (FCA) and considers that ontologies are formalized in expressive Description Logics. Our approach creates a merged ontology which captures the knowledge of the two source ontologies. Contributions of this work are (i) enabling the creation of concepts not originally in the source ontologies, (ii) providing a definition to these concepts in terms of elements of both ontologies and (iii) optimizing the merged ontology. We have studied our approach in the context of spatial information, a domain which exploits many existing ontologies represented with Description Logics.