Open AccessAdapting Open Information Extraction to Domain‐Specific Relations
Author(s) -
Soderland Stephen,
Roof Brendan,
Qin Bo,
Xu Shi,
Etzioni Oren
Publication year - 2010
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v31i3.2305
Subject(s) - open domain , computer science , relationship extraction , domain (mathematical analysis) , ontology , relation (database) , information extraction , tuple , information retrieval , set (abstract data type) , artificial intelligence , natural language processing , ontology learning , question answering , domain knowledge , data mining , upper ontology , suggested upper merged ontology , mathematics , programming language , mathematical analysis , philosophy , epistemology , discrete mathematics
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 domain‐independent 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.