SRDF: Extracting Lexical Knowledge Graph for Preserving Sentence Meaning

Sangha Nam, GyuHyeon Choi, Younggyun Hahm, Key-Sun Choi


Abstract
In this paper, we present an open information extraction system so-called SRDF that generates lexical knowledge graphs from unstructured texts. In semantic web, knowledge is expressed in the RDF triple form but the natural language text consist of multiple relations between arguments. For this reason, we combine open information extraction with the reification for the full text extraction to preserve meaning of sentence in our knowledge graph. And also our knowledge graph is designed to adapt for many existing semantic web applications. At the end of this paper, we introduce the result of the experiment and a Korean template generation module developed using SRDF.
Anthology ID:
W16-4411
Volume:
Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
77–81
Language:
URL:
https://aclanthology.org/W16-4411
DOI:
Bibkey:
Cite (ACL):
Sangha Nam, GyuHyeon Choi, Younggyun Hahm, and Key-Sun Choi. 2016. SRDF: Extracting Lexical Knowledge Graph for Preserving Sentence Meaning. In Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016), pages 77–81, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
SRDF: Extracting Lexical Knowledge Graph for Preserving Sentence Meaning (Nam et al., 2016)
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PDF:
https://aclanthology.org/W16-4411.pdf