Skip to Main content Skip to Navigation
Conference papers

Using Maximal Join for Information Fusion

Abstract : Information fusion is a very active research do-main. A lot of studies exist dealing with informa-tion fusion at a low level of semantics. Our claimis that information should be fused at a high levelof semantics and using a symbolic representation.Previously, we intuitively presented a framework for high-level symbolic fusion. Our approach relieson the use of the conceptual graphs model. Domainknowledge is a major point of the fusion process.The use of conceptual graphs for knowledge rep-resentation fusion eases the process of expressingdomain knowledge and fusion heuristics. In thispaper, we formalize our approach. In particular,we detail and formalize the introduction of domainknowledge inside the fusion process. We validateour approach within the context of a TV programrecommendation system.
Document type :
Conference papers
Complete list of metadata
Contributor : Lip6 Publications <>
Submitted on : Tuesday, March 29, 2016 - 3:51:45 PM
Last modification on : Friday, January 8, 2021 - 5:32:07 PM


  • HAL Id : hal-01294645, version 1


Claire Laudy, Jean-Gabriel Ganascia. Using Maximal Join for Information Fusion. FIRST IJCAI WORKSHOP ON GRAPH STRUCTURES FORKNOWLEDGE REPRESENTATION AND REASONING, Jul 2009, Pasadena, California, United States. ⟨hal-01294645⟩



Record views