Multilanguage hierarchical logics, or: how we can do without modal logics
Artificial Intelligence
Semantic integration of semistructured and structured data sources
ACM SIGMOD Record
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Distributed Description Logics: Directed Domain Correspondences in Federated Information Sources
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Context matching for electronic marketplaces: a case study
The Knowledge Engineering Review
An Algebraic Framework for Schema Matching
Informatica
Multi-labeled graph matching: an algorithm model for schema matching
ASIAN'05 Proceedings of the 10th Asian Computing Science conference on Advances in computer science: data management on the web
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Ontology mapping by axioms (OMA)
WM'05 Proceedings of the Third Biennial conference on Professional Knowledge Management
DALT'06 Proceedings of the 4th international conference on Declarative Agent Languages and Technologies
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The development of more and more complex distributed applications over large networks of computers has raised the problem of semantic interoperability across applications based on local and autonomous semantic schemas (e.g., concept hierarchies, taxonomies, ontologies). In this paper we propose to view each semantic schema as a context (in the sense defined in [1]), and propose an algorithm for automatically discovering relations across contexts (where relations are defined in the sense of [7]). The main feature of the algorithm is that the problem of finding relationships between contexts is encoded as a problem of logical satisfiability, and so the discovered mappings have a well-defined semantic. The algorithm we describe has been implemented as part of a peer-to-peer system for Distributed Knowledge Management, and tested on significant cases.