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Abstract

We present URDF, an efficient reasoning framework for graph-based, nonschematic RDF knowledge bases and SPARQL-like queries. URDF augments first-order reasoning by a combination of soft rules, with Datalog-style recursive implications, and hard rules, in the shape of mutually exclusive sets of facts. It incorporates the common possible worlds semantics with independent base facts as it is prevalent in most probabilistic database approaches, but also supports semantically more expressive, probabilistic first-order representations such as Markov Logic Networks. As knowledge extraction on theWeb often is an iterative (and inherently noisy) process, URDF explicitly targets the resolution of inconsistencies between the underlying RDF base facts and the inference rules. Core of our approach is a novel and efficient approximation algorithm for a generalized version of the Weighted MAX-SAT problem, allowing us to dynamically resolve such inconsistencies directly at query processing time. Our MAX-SAT algorithm has a worst-case running time of O(jCj jSj), where jCj and jSj denote the number of facts in grounded soft and hard rules, respectively, and it comes with tight approximation guarantees with respect to the shape of the rules and the distribution of confidences of facts they contain. Experiments over various benchmark settings confirm a high robustness and significantly improved runtime of our reasoning framework in comparison to state-of-the-art techniques for MCMC sampling such as MAP inference and MC-SAT. Keywords