Multi-strategy semantic web reasoning for medical knowledge bases

Semantic Web technology offers excellent advantages for real-world medical knowledge bases, both on and off the Web. Based on ontologies from (bio)medical domains, OWL inferencing enhances knowledge bases with new facts, while deductive rules, written in semantic rule languages, supply additional inferences based on deterministic knowledge from e.g., Clinical Practice Guidelines (CPG). We argue that other mechanisms, representing weaker forms of inferencing, are also useful in dealing with incomplete healthcare knowledge. This includes inductive generalization, which leverages data similarities to induce new rules, and analogical reasoning, which relies on plausible domain knowledge. To cope with their shortcomings, we propose integrating such weak inferencing with a single, explanation-based generalization, allowing us to leverage their complementary strengths as well as apply a tutor-based paradigm for verification. In this integrated approach, justifications are generated explaining the potential correctness of queries, where missing medical knowledge is compensated by injecting plausible inferences. Based on their expertise, healthcare experts may then confirm particular justifications, materializing them in the knowledge base. Inversely, we argue that by leveraging OWL ontological knowledge, weak inferencing methods benefit from Semantic Web technology as well.