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A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR. Our approach builds on the recent success of meta-interpretive learning methods in Inductive Logic Programming (ILP), and we further extends it to a framework that enables robust and efficient structure learning ofdoi:10.1145/2661829.2662022 dblp:conf/cikm/WangMC14 fatcat:waltuvdfxfeh3huk6lssow52se