swMATH ID: 32915
Software Authors: William Yang Wang; Kathryn Mazaitis; William W. Cohen
Description: ProPPR: efficient first-order probabilistic logic programming for structure discovery, parameter learning, and scalable inference. A key challenge in statistical relational learning is to develop a semantically rich formalism that supports efficient probabilistic reasoning using large collections of extracted information. This paper presents a new, scalable probabilistic logic called ProPPR, which further extends stochastic logic programs (SLP) to a framework that enables efficient learning and inference on graphs: using an abductive second-order probabilistic logic, we show that first-order theories can be automatically generated via parameter learning; that in parameter learning, weight learning can be performed using parallel stochastic gradient descent with a supervised personalized PageRank algorithm; and that most importantly, queries can be approximately grounded with a small graph, and inference is independent of the size of the database.
Homepage: https://www.aaai.org/ocs/index.php/WS/AAAIW14/paper/view/8768/8250
Source Code:  https://github.com/TeamCohen/ProPPR
Related Software: TensorLog; Adam; TensorFlow; Church; ProbLog; SciPy; Theano
Cited in: 1 Publication

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