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Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs

机译：Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs

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We provide a framework for probabilistic reasoning in Vadalog-based Knowledge Graphs (KGs),satisfying the requirements of ontological reasoning: full recursion, powerful existential quantification,expression of inductive definitions. Vadalog is a Knowledge Representation and Reasoning(KRR) language based on Warded Datalog+/?, a logical core language of existential rules,with a good balance between computational complexity and expressive power. Handling uncertaintyis essential for reasoning with KGs. Yet Vadalog and Warded Datalog+/? are not coveredby the existing probabilistic logic programming and statistical relational learning approaches forseveral reasons, including insufficient support for recursion with existential quantification andthe impossibility to express inductive definitions. In this work, we introduce Soft Vadalog, aprobabilistic extension to Vadalog, satisfying these desiderata. A Soft Vadalog program induceswhat we call a Probabilistic Knowledge Graph (PKG), which consists of a probability distributionon a network of chase instances, structures obtained by grounding the rules over a databaseusing the chase procedure. We exploit PKGs for probabilistic marginal inference. We discussthe theory and present MCMC-chase, a Monte Carlo method to use Soft Vadalog in practice.We apply our framework to solve data management and industrial problems and experimentallyevaluate it in the Vadalog system.

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