caa a22 4500 605478198 CHVBK 20210128100404.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s10994-015-5488-x doi (NATIONALLICENCE)springer-10.1007/s10994-015-5488-x Efficient inference and learning in a large knowledge base [Elektronische Daten] Reasoning with extracted information using a locally groundable first-order probabilistic logic [William Wang, Kathryn Mazaitis, Ni Lao, William Cohen] One important challenge for probabilistic logics is reasoning with very large knowledge bases (KBs) of imperfect information, such as those produced by modern web-scale information extraction systems. One scalability problem shared by many probabilistic logics is that answering queries involves "grounding” the query—i.e., mapping it to a propositional representation—and the size of a "grounding” grows with database size. To address this bottleneck, we present a first-order probabilistic language called ProPPR in which approximate "local groundings” can be constructed in time independent of database size. Technically, ProPPR is an extension to stochastic logic programs that is biased towards short derivations; it is also closely related to an earlier relational learning algorithm called the path ranking algorithm. We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank on a linearized version of the proof space, and based on this connection, we develop a provably-correct approximate grounding scheme, based on the PageRank-Nibble algorithm. Building on this, we develop a fast and easily-parallelized weight-learning algorithm for ProPPR. In our experiments, we show that learning for ProPPR is orders of magnitude faster than learning for Markov logic networks; that allowing mutual recursion (joint learning) in KB inference leads to improvements in performance; and that ProPPR can learn weights for a mutually recursive program with hundreds of clauses defining scores of interrelated predicates over a KB containing one million entities. The Author(s), 2015 Probabilistic logic nationallicence Personalized PageRank nationallicence Scalable learning nationallicence Wang William School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA aut Mazaitis Kathryn School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA aut Lao Ni Google Inc., 1600 Amphitheatre Parkway, 94043, Mountain View, CA, USA aut Cohen William School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA aut Machine Learning Springer US; http://www.springer-ny.com 100/1(2015-07-01), 101-126 0885-6125 100:1<101 2015 100 10994 https://doi.org/10.1007/s10994-015-5488-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5488-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang William School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA aut NATIONALLICENCE 700 1- Mazaitis Kathryn School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA aut NATIONALLICENCE 700 1- Lao Ni Google Inc., 1600 Amphitheatre Parkway, 94043, Mountain View, CA, USA aut NATIONALLICENCE 700 1- Cohen William School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/1(2015-07-01), 101-126 0885-6125 100:1<101 2015 100 10994