Clinical care and research deal with large volumes of complex information that originates from different sources and that has different structures and semantics. By establishing an explicit, formal specification of the concepts and their relations for a particular domain, e.g. medicine, ontologies facilitate the integration and reuse of valuable knowledge across applications. External medical knowledge in the ontologies can be utilized to semantically enhance clinical data, including medical images, for the purposes of improved search and retrieval.

This semantic cross-modal medical image and information retrieval scenario constitues the contents of the German funded MEDICO project, which is an application scenario of the THESEUS research programme. The vision of MEDICO is to create a universally usable search engine for retrieving medical images and the related patient and textual data based on semantic technologies. The interdisciplinary MEDICO consortium that consists of a number of partners from the industry, academia and healthcare sector is coordinated by Siemens.

In this presentation we first give an overview of the MEDICO project. Subsequently, we report on a medical knowledge engineering methodology we have developed based on the experiences collected along the MEDICO. In particular, we discuss the specific medical knowledge engineering requirements, we identified for the semantic medical image and text retrieval. We then introduce our methodology that is derived from these requirements and that relies on a novel technique for semi-automatically generating a set of potential user queries to support the knowledge elicitation process