Lexical ambiguity and information retrieval
ACM Transactions on Information Systems (TOIS)
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Distributed and Parallel Databases
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Information Retrieval
Conceptual Indexing: A Better Way to Organize Knowledge
Conceptual Indexing: A Better Way to Organize Knowledge
Ontology Matching
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 engineering revisited: an iterative case study
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
SomeWhere: a scalable peer-to-peer infrastructure for querying distributed ontologies
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
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In P2P systems where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the "shared" parts of the ontologies) but, in some sense, limited to them. Then, to what extent the "unshared" parts can also contribute to and improve information exchange? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator's side and a query interpretation step at the document provider's. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show that our method enables to correctly evaluate the relevance of a document even if concepts of a query are not shared. In some cases, we are able to find up to 90% of the documents that would be selected when all the central concepts are shared.