Leveraging Semantic Networks for Personalized Content in Health Recommender Systems

Since the emergence of the Internet in the early 90's of the last century medical knowledge is spreading around the globe increasingly fast. Though publicly available, it is a difficult task to determine individual relevance for most non professionals. Additionally, relationships between medical terms are hard to discover even for professionals. In this paper we present an approach on how semantic query expansion can be exploited to enhance classic information retrieval (IR) techniques in order to gather health information artifacts for consumers. The approach is based on health related semantic networks which are automatically generated from public resources such as Wikipedia. A scenario for integrating such networks is a so-called health recommender systems (HRS) which can be embedded into a personal health record system (PHRS). This way, relevant personalized medical content can be delivered automatically to end users and owners of health records.