Adapting Open Information Extraction to Domain-Specific Relations

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 prespecified relations. Due to its open-domain and open-relation nature, Open IE is purely textual and is unable to relate 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 domainindependent tuples to an ontology using domains from the DARPA Machine Reading Project. Our system achieves precision over 0.90 from as few as eight training examples for an NFL-scoring domain. Copyright © 2010.