Multi-Relation Modeling on Multi Concept Extraction LIG participation at ImageClefMed
Conference and Labs of the Evaluation Forum
This paper presents the LIG contribution to the CLEF 2008 medical retrieval task (i.e. ImageCLEFmed). The main idea behind our contribution is to incorporate knowledge in the language modeling approach to information retrieval (IR). On ImageCLEFmed our model makes use of the textual part of the corpus and of the medical knowledge found in the Unified Medical Language System (UMLS) knowledge sources. Last year, we used UMLS to create a conceptual representation for each sentence in the corpus,
... d proposed a language modeling approach on these representations. The use of a conceptual representation allows the system to work at a more abstract semantic level, which solves some of the information retrieval problems, as the one of terminological variation. We also used different concept extraction methods, and tested how to combine these extraction methods on queries. This year, we have extended our previous method in two ways: first, we have used, in addition to relations derived from UMLS, co-occurrence relations; second, we have combined concept extraction methods not only on queries, but also on documents. In this paper, we first detail some IR approaches that use advanced index terms. We then develop the graph model used in our submission to ImageCLEFmed 2008, and the different ways use to combine graphs derived from different concept extraction methods. After this, we present our results on this year collection, showing that combined concept extraction on document improves the MAP results and that relations impact more first results precision. Finally, we conclude this work and present some possible extensions.