NDLI: PIDGIN: ontology alignment using web text as interlingua

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PIDGIN: ontology alignment using web text as interlingua

Content Provider	ACM Digital Library
Author	Mitchell, Tom Talukdar, Partha Pratim Wijaya, Derry
Abstract	The problem of aligning ontologies and database schemas across different knowledge bases and databases is fundamental to knowledge management problems, including the problem of integrating the disparate knowledge sources that form the semantic web's Linked Data [5]. We present a novel approach to this ontology alignment problem that employs a very large natural language text corpus as an interlingua to relate different knowledge bases (KBs). The result is a scalable and robust method (PIDGIN) that aligns relations and categories across different KBs by analyzing both (1) shared relation instances across these KBs, and (2) the verb phrases in the text instantiations of these relation instances. Experiments with PIDGIN demonstrate its superior performance when aligning ontologies across large existing KBs including NELL, Yago and Freebase. Furthermore, we show that in addition to aligning ontologies, PIDGIN can automatically learn from text, the verb phrases to identify relations, and can also type the arguments of relations of different KBs.
Starting Page	589
Ending Page	598
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505559
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Knowledge bases Natural language processing Graph-based self-supervised learning Label propagation Ontology alignment
Content Type	Text
Resource Type	Article

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