Abstract

The web is a vast repository of knowledge, but automatically extracting that knowledge, at scale, has proven to be a formidable challenge. A number of recent evaluation efforts have focused on automatic knowledge base population (Ji, Grishman, and Dang 2011; Artiles and Mayfield 2012), and many well-known broad domain and open information extraction systems exist, including the Never-Ending Language Learning (NELL) project (Carlson et al. 2010), OpenIE (Etzioni et al. 2008), and efforts at Google (Pasca et al. 2006), which use a variety of techniques to extract new knowledge, in the form of facts, from the web. These facts are interrelated, and hence, recently this extracted knowledge has been referred to as a knowledge graph (Singhal 2012). Unfortunately, most web-scale extraction systems do not take advantage of the rich dependencies found in the knowledge graph; instead approaches consider extractions independently, relying on simple heuristics to enforce consistency.
Recent work demonstrates that reasoning jointly is a promising approach to improving the knowledge graph.(Jiang, Lowd, and Dou 2012) choose candidate facts for inclusion in a knowledge base with a joint approach using Markov Logic Networks (MLNs)(Richardson and Domingos 2006). Jiang et al. provide a straightforward codification of ontological relations and candidate facts found in a knowledge base as rules in first-order logic and use MLNs to formulate a probabilistic model. However, due to the combinatorial explosion of Boolean assignments to random variables, inference and learning in MLNs pose intractable optimization problems. Jiang et al. limit …

Date

2013

Authors

Jay Pujara, Hui Miao, Lise Getoor, William W Cohen