Using Semantics and Statistics to Turn Data into Knowledge

SPRING 2015 65 Agrowing body of research focuses on extracting knowledge from text such as news reports, encyclopedic articles, and scholarly research in specialized domains. Much of this data is freely available on the World Wide Web and harnessing the knowledge contained in millions of web documents remains a problem of particular interest. The scale and diversity of this content pose a formidable challenge for systems designed to extract this knowledge. Many well-known broad domain and open information-extraction systems seek to build knowledge bases from text, including the Never-Ending Language Learning (NELL) project (Carlson et al. 2010), OpenIE (Etzioni et al. 2008), DeepDive (Niu et al. 2012), and efforts at Google (Pasca et al. 2006). Ultimately, these information-extraction systems produce a collection of candidate facts that include a set of entities, attributes of these entities, and the relations between these entities. Information-extraction systems use a sophisticated collection of strategies to generate candidate facts from web documents, spanning the syntactic, lexical, and structural features of text (Weikum and Theobald 2010, Wimalasuriya and Dou 2010). Although these systems are capable of extracting many candidate facts from the web, their output is often hampered by noise. Documents contain inaccurate, outdat-