Conceptual Indexing Contribution to ImageCLEF Medical Retrieval Tasks

In this chapter, we study conceptual indexing using a language modeling approach to information retrieval. In particular, we propose a conceptual representation of documents that allows the use of both concepts and labelled relations when matching documents and queries. Such semantic indexing gives effective results when large ontologies are used. We first present a model derived from the language modeling approach to information retrieval based on a conceptual representation of documents. We then introduce an extension to take into account relations between concepts. Concept and relation detection methods are, however, error–prone. We thus develop an approach to limit such errors by combining different methods. In order to illustrate various aspects of the model proposed, we conducted a series of experiments on various medical ImageCLEF collections. Our experiments in ImageCLEFmed show that the conceptual model proposed here provides good results in medical information retrieval. Experiments furthermore show that combining concept extraction methods through fusion improves the standard language model by up to 17% MAP on the medical ImageCLEF collections.