Interactive reasoning in uncertain RDF knowledge bases

Recent advances in Web-based information extraction have allowed for the automatic construction of large, semantic knowledge bases, which are typically captured in RDF for-mat. The very nature of the applied extraction techniques however entails that the resulting RDF knowledge bases may face a significant amount of incorrect, incomplete, or even in-consistent (i.e., uncertain) factual knowledge, which makes query answering over this kind of data a challenge. Our reasoner, coined URDF1, supports SPARQL queries along with rule-based, first-order predicate logic to infer new facts and to resolve data uncertainty over millions of RDF triplets directly at query time. We demonstrate a fully interactive reasoning engine, combining a Java-based reasoning back-end and a Flash-based visualization frontend in a dynamic client-server architecture. Our visualization frontend pro-vides interactive access to the reasoning backend, including tasks like exploring the knowledge base, rule-based and sta-tistical reasoning, faceted browsing of large query graphs, and explaining answers through lineage. 1