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In order to solve medical multimodal queries, we propose to split the queries in different dimensions using ontology. We extract both textual and visual terms depending on the ontology dimension they belong to. Based on these terms, we build different sub queries each corresponds to one query dimension. Then we use Boolean expressions on these sub queries to filter the entire document collection. The filtered document set is ranked using the techniques in Vector Space Model. We also combine the ranked lists generated using both text and image indexes to further improve the retrieval performance. We have achieved the best overall performance for the Medical Image Retrieval Task in CLEF 2005. These experimental results show that while most queries are better handled by the text query processing as most semantic information are contained in the medical text cases, both textual and visual ontology dimensions are complementary in improving the results during media fusion.

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In professional environments which are characterized by a domain (Medicine, Law, etc.), information retrieval systems must be able to process precise queries, mostly because of the use of a specific domain terminology, but also because the retrieved information is meant to be part of the professional task (a diagnosis, writing a law text, etc.). In this paper we address the problem of solving domain-specific precise queries. We present an information retrieval model based on description logics to represent external knowledge resources and provide expressive document indexing and querying.

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Abstract-We present a precise search engine adapted to professional environments which are characterized by a domain (e.g. medicine, law, sport, and so on). In our approach, each domain has its own terminology (i.e. a set of terms that denote its concepts: team, player, etc.) and it is organized along dimensions, such as person, location, etc. The dimensions, as described below, are made of concepts and semantic relationships that represent a particular perspective or point of view on the domain. We mainly use the notion of domain dimension to: i) precisely index document content, and ii) develop an interactive interface which allows the user to precisely describe his or her information need and therefore precisely access the document collection.Index Terms-Information retrieval, domain dimensions, user interface.