| Document Date: 2015-04-10 22:35:36
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City Pittsburgh / Mountain View / / Company USA 2 Google Inc. / Markov Logic Networks / / Country United States / / / Facility Amphitheatre Parkway / Carnegie Mellon University / / IndustryTerm unstructured web data / graph search / usual depth-first search / depth-bounded breadth-first search procedure / inference algorithm / depth-first search / earlier relational learning algorithm / information extraction systems / search graph / easily-parallelized weight-learning algorithm / computing / crucial algorithm / webscale information extraction systems / particular solution / / Organization School of Computer Science / Carnegie Mellon University / / Person William Yang / Gerson Zaverucha / Yang Wang / William W. Cohen / Vítor Santos Costa / Kathryn Mazaitis / / Position Author / sprinter / Prolog interpreter / head / programmer / / ProgrammingLanguage D / Prolog / Datalog / L / / ProvinceOrState Pennsylvania / California / / Technology easily-parallelized weight-learning algorithm / Nibble algorithm / approximate Personalized PageRank algorithm / earlier relational learning algorithm / following inference algorithm / inference algorithm / ProPPR inference algorithm / crucial algorithm / /
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Mach Learn DOIs10994x Efficient inference and learning in a large knowledge base Reasoning with extracted information using a locally groundable first-order probabilistic logic