Adapting Open Information Extraction to Domain-Specific Relations

Stephen Soderland, Brendan Roof, Bo Qin, Shi Xu, Mausam, Oren Etzioni
2010 The AI Magazine  
Information extraction (IE) can identify a set of relations from free text to support question answering (QA). Until recently, IE systems were domain-specific and needed a combination of manual engineering and supervised learning to adapt to each target domain. A new paradigm, Open IE operates on large text corpora without any manual tagging of relations, and indeed without any pre-specified relations. Due to its open-domain and open-relation nature, Open IE is purely textual and is unable to
more » ... late the surface forms to an ontology, if known in advance. We explore the steps needed to adapt Open IE to a domain-specific ontology and demonstrate our approach of mapping domain-independent tuples to an ontology using domains from DARPA's Machine Reading Project. Our system achieves precision over 0.90 from as few as 8 training examples for an NFL-scoring domain.
doi:10.1609/aimag.v31i3.2305 fatcat:dt6hu6tzgve47a2oe2r75u3rse