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Lecture Notes in Computer Science
In the past ten years, the areas of probabilistic inductive logic programming and statistical relational learning put forth a large collection of approaches to combine relational representations of knowledge with probabilistic reasoning. Here, we develop a series of evaluation and comparison criteria for those approaches and focus on the point of view of knowledge representation and reasoning. These criteria address abstract demands such as language aspects, the relationships to propositionaldoi:10.1007/978-3-642-24455-1_6 fatcat:orp6j3hmj5btnovnt4czh436xa