Paper Info
Title
Domain-Targeted, High Precision Knowledge Extraction.
Abstract
Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information extraction (IE) techniques, no suitable resource for our task already exists; existing resources are either too noisy, too named-entity centric, or too incomplete, and typically have not been constructed with a clear scope or purpose. To address these, we have created a domain-targeted, high precision knowledge extraction pipeline, leveraging Open IE, crowdsourcing, and a novel canonical schema learning algorithm (called CASI), that produces high precision knowledge targeted to a particular domain - in our case, elementary science. To measure the KB’s coverage of the target domain’s knowledge (it’s comprehensiveness with respect to science) we measure recall with respect to an independent corpus of domain text, and show that our pipeline produces output with over 80% precision and 23% recall with respect to that target, a substantially higher coverage of tuple-expressible science knowledge than other comparable resources. We have made the KB publicly available at  http://data.allenai.org/tuple-kb .
Year
Venue
Field
2017
TACL
Information retrieval,Computer science,Crowdsourcing,Information extraction,Natural language processing,Artificial intelligence,Knowledge extraction,Knowledge base,Predicate (grammar),Schema (psychology),Recall
DocType
Volume
Citations 
Journal
5
5
PageRank 
References 
Authors
0.44
25
3
Name
Order
Citations
PageRank
Bhavana Bharat Dalvi120117.31
Niket Tandon214617.32
Peter Clark320215.11