KISTI at CLEF eHealth 2015 Task 2

Laypeople (e.g., patients and their caregivers) usually use queries which describe a sign, symptom or condition to obtain relevant medical information on the Web. They can fail to find useful information for diagnosing or understanding their health conditions because the search results delivered by existing medical search engines do not fit the information needs of users. To deliver useful medical information, we attempted to combine multiple ranking methods, explicit semantic analysis (ESA), a cluster-based external expansion model (CBEEM), and concept-based document centrality (CBDC), using external medical resources to improve retrieval performance. As a first step, initial documents are searched using a baseline method. Based on the initial documents, ranking methods are selectively applied. Our experiments with combinations of ranking methods aim to find the best means of computing accurate similarity scores using different external medical resources. The best performance was obtained when the CBEEM and the CBDC were used together.