University of Jaén at ImagePhoto 2008: filtering the results with the cluster term
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Combining TEXT-MESS systems at ImageCLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Query expansion on medical image retrieval: MeSH vs. UMLS
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
University of Jaén at ImageCLEF 2009: medical and photo tasks
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Using web sources for improving video categorization
Journal of Intelligent Information Systems
An ontological representation of documents and queries for information retrieval systems
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Expert Systems with Applications: An International Journal
Proxemic conceptual network based on ontology enrichment for representing documents in IR
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Discovering implicit communities in Web forums through ontologies
Web Intelligence and Agent Systems
Semantic concept-enriched dependence model for medical information retrieval
Journal of Biomedical Informatics
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This paper describes the SINAI team participation in the ImageCLEFmed campaign. The SINAI research group has participated in the multilingual image retrieval subtask. The experiments accomplished are based on the integration of specific knowledge in the topics.We have used the MeSH ontology to expand the queries. The expansion consists in searching terms from the topic query in the MeSH ontology in order to add similar terms. We have processed the set of collections using Information Gain (IG) in the same way as in ImageCLEFmed 2006.In our experiments mixing visual and textual information we obtain better results than using only textual information. The weigth of the textual information is very strong in this mixed strategy. In the experiments with a low textual weight, the use of IG improves the results obtained.