Multiplying Concept Sources for Graph Modeling - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2007

Multiplying Concept Sources for Graph Modeling

Loic Maisonnasse
  • Function : Author
Jean-Pierre Chevallet


This paper presents the LIG contribution to the CLEF 2007 medical retrieval task (i.e. ImageCLEFmed). The main idea in this paper is to incorporate medical knowledge in the language modeling approach to information retrieval (IR). Our model makes use of the textual part of ImageCLEFmed corpus and of the medical knowledge as found in the UnifiedMedical Language System (UMLS) knowledge sources. The use of UMLS allows us to create a conceptual representation of each sentence in the corpus. We use these sentence representations to create a graph model for each document. As in the standard language modeling approach, we evaluate the probability that a document graph model generates the query graph. Graphs are created from medical texts and queries, and are built for different languages, with different methods. The use of a conceptual representation allows the system to work at a higher semantic level, which solves some of the information retrieval problems, as term variation. After developing the graph model in the first part of the paper, we present our tests, which involve mixing different concepts sources (i.e. languages and methods) for the matching of the query and text graphs. Results show that using language model on concepts provides good results in IR.Multiplying the concept sources further improves the results. Lastly, using relations between concepts (provided by the graphs under consideration) improves results when only few conceptual sources are used to analyze the query.
No file

Dates and versions

hal-00954098 , version 1 (28-02-2014)


  • HAL Id : hal-00954098 , version 1


Loic Maisonnasse, Eric Gaussier, Jean-Pierre Chevallet. Multiplying Concept Sources for Graph Modeling. Workshop CLEF 2007, 2007, Budapest. ⟨hal-00954098⟩
40 View
0 Download


Gmail Facebook Twitter LinkedIn More