IKE - An Interactive Tool for Knowledge Extraction

Abstract

Recent work on information extraction has suggested that fast, interactive tools can be highly effective; however, creating a usable system is challenging, and few publically available tools exist. In this paper we present IKE, a new extraction tool that performs fast, interactive bootstrapping to develop high-quality extraction patterns for targeted relations. Central to IKE is the notion that an extraction pattern can be treated as a search query over a corpus. To operationalize this, IKE uses a novel query language that is expressive, easy to understand, and fast to execute -essential requirements for a practical system. It is also the first interactive extraction tool to seamlessly integrate symbolic (boolean) and distributional (similarity-based) methods for search. An initial evaluation suggests that relation tables can be populated substantially faster than by manual pattern authoring while retaining accuracy, and more reliably than fully automated tools, an important step towards practical KB construction. We are making IKE publically available (http://allenai.org/ software/interactive-knowledge-extraction).

From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems

“…While there has been a lot of work in IE on domains such as web documents (Chang et al, 2003;Etzioni et al, 2004;Cafarella et al, 2005;Chang et al, 2006;Banko et al, 2007;Etzioni et al, 2008;Mitchell et al, 2015) and scientific publication data (Shah et al, 2003;Peng and McCallum, 2006;Saleem and Latif, 2012), work on IE from educational material is much more sparse. Most of the research in IE from educational material deals with extracting simple educational concepts (Shah et al, 2003;Canisius and Sporleder, 2007;Liu et al, 2016b;Wang et al, 2016) or binary relational tuples (Balasubramanian et al, 2002;Clark et al, 2012;Dalvi et al, 2016) using existing IE techniques. On the other hand, our approach extracts axioms and parses them to horn clause rules.…”

Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks

“…Although there has been a lot of work in IE on domains such as Web documents (Chang, Hsu, and Lui 2003;Etzioni et al 2004;Cafarella et al 2005;Chang et al 2006;Banko et al 2007;Etzioni et al 2008;Mitchell et al 2015) and scientific publication data (Shah et al 2003;Peng and McCallum 2006;Saleem and Latif 2012), work on IE from educational material is much more sparse. Most of the research in IE from educational material deals with extracting simple educational concepts (Shah et al 2003;Canisius and Sporleder 2007;Yang et al 2015;Wang et al 2015;Liang et al 2015;Wu et al 2015;Liu et al 2016b;Wang et al 2016) or binary relational tuples (Balasubramanian et al 2002;Clark et al 2012;Dalvi et al 2016) using existing IE techniques. On the other hand, our approach extracts axioms and parses them to horn-clause rules.…”

Scalable semantic querying of text

“…We ended up with 84 articles and 137 labeled cafe names in the BARISTAMAG dataset and 1645 articles and 671 cafe names in SPRUDGE. We compare the performance of KOKO against IKE [18], CRFsuite [31], and NELL [8,29]. We compare to IKE because IKE supports distributional similarity based search which is similar to KOKO descriptors as we described in Section 5.…”