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
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Distributed and Parallel Databases
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Information Retrieval
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Ontological Engineering
Conceptual Indexing: A Better Way to Organize Knowledge
Conceptual Indexing: A Better Way to Organize Knowledge
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Ontology Matching
Ontology engineering revisited: an iterative case study
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Semantic Heterogeneity Measures of Unstructured P2P Systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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In semantic web applications 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, how 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 an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared.