Adapting Open Information Extraction to Domain-Specific Relations

  • Stephen Soderland University of Washington
  • Brendan Roof University of Washington
  • Bo Qin University of Washington
  • Shi Xu University of Washington
  • - Mausam University of Washington
  • Oren Etzioni University of Washington

Abstract

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 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 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.

Author Biographies

Stephen Soderland, University of Washington
Department of Computer Science and Engineering
Brendan Roof, University of Washington
Department of Computer Science and Engineering
Bo Qin, University of Washington
Department of Computer Science and Engineering
Shi Xu, University of Washington
Department of Computer Science and Engineering
- Mausam, University of Washington
Department of Computer Science and Engineering
Oren Etzioni, University of Washington
Department of Computer Science and Engineering
Published
2010-07-28
Section
Articles