Adapting Open Information Extraction to Domain-Specific Relations

Abstract

dent processes and domain-specific knowledge. Until recently, information extraction has leaned heavily on domain knowledge, which requires either manual engineering or manual tagging of examples (Miller et al. 1998; Soderland 1999; Culotta, McCallum, and Betz 2006). Semisupervised approaches (Riloff and Jones 1999, Agichtein and Gravano 2000, Rosenfeld and Feldman 2007) require only a small amount of hand-annotated training, but require this for every relation of interest. This still presents a knowledge engineering bottleneck, when one considers the unbounded number of relations in a diverse corpus such as the web. Shinyama and Sekine (2006) explored unsupervised relation discovery using a clustering algorithm with good precision, but limited scalability. The KnowItAll research group is a pioneer of a new paradigm, Open IE (Banko et al. 2007, Banko and Etzioni 2008), that operates in a totally domain-independent manner and at web scale. An Open IE system makes a single pass over its corpus and extracts a diverse set of relational tuples without requiring any relation-specific human input. Open IE is ideally suited to corpora such as the web, where the target relations are not known in advance and their number is massive. Articles

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@article{Soderland2010AdaptingOI, title={Adapting Open Information Extraction to Domain-Specific Relations}, author={Stephen Soderland and Brendan Roof and Bo Qin and Shi Xu and Mausam and Oren Etzioni}, journal={AI Magazine}, year={2010}, volume={31}, pages={93-102} }