As the use of Electronic Medical Records (EMRs) becomes more widespread, so does the need for effective information discovery within them. Recently proposed EMR standards are XML-based. A key characteristic in these standards is the frequent use of ontological references, i.e., ontological concept codes appear as XML elements and are used to associate portions of the EMR document with concepts defined in a domain ontology. A rich corpus of work addresses searching XML documents. Unfortunately, these works do not make use of ontological references to enhance search. In this paper we present the XOntoRank system which addresses the problem of ontology-aware keyword search of XML documents with a particular focus on EMR XML documents. Our current prototypes and experiments use the Health Level Seven (HL7) Clinical Document Architecture (CDA) Release 2.0 standard of EMR representation and the Systematized Nomenclature of Human and Veterinary Medicine (SNOMED) ontology, although the presented techniques and results are applicable to any EMR hierarchical format and any ontology that defines concepts and relationships.

This paper describes our participation in the TREC 2005 Genomics track. We took part in the ad hoc retrieval task and aimed at integrating thesauri in the retrieval model. We developed three thesauri-based methods, two of which made use of the existing MeSH thesaurus. One method uses blind relevance feedback on MeSH terms, the second uses an index of the MeSH thesaurus for query expansion. The third method makes use of a dynamically generated lookup list, by which acronyms and synonyms could be inferred. We show that, despite the relatively minor improvements in retrieval performance of individually applied methods, a combination works best and is able to deliver significant improvements over the baseline.