Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === Directly adding the knowledge triples obtained from open information extraction systems into a knowledge base is often impractical due to a vocabulary gap between natural language expressions and knowledge base representation. This thesis aims at learning to ma...

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Main Authors:	Chin-Ho Lin, 林勤和
Other Authors:	Hsin-Hsi Chen
Format:	Others
Language:	en_US
Published:	2017
Online Access:	http://ndltd.ncl.edu.tw/handle/hcqdyq

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spelling	ndltd-TW-105NTU053920962019-05-15T23:39:40Z http://ndltd.ncl.edu.tw/handle/hcqdyq Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction 學習將自然語言敘述映射為知識圖譜表示形式以利知識庫之建立 Chin-Ho Lin 林勤和碩士國立臺灣大學資訊工程學研究所 105 Directly adding the knowledge triples obtained from open information extraction systems into a knowledge base is often impractical due to a vocabulary gap between natural language expressions and knowledge base representation. This thesis aims at learning to map relational phrases in triples from natural-language-like statement to knowledge base predicate format. We train a word representation model on a vector space and link each natural language relational pattern to semantically equivalent knowledge base predicate. Our mapping result shows not only high quality, but also promising coverage on relational phrases compared to previous researches. Hsin-Hsi Chen 陳信希 2017 學位論文 ; thesis 38 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === Directly adding the knowledge triples obtained from open information extraction systems into a knowledge base is often impractical due to a vocabulary gap between natural language expressions and knowledge base representation. This thesis aims at learning to map relational phrases in triples from natural-language-like statement to knowledge base predicate format. We train a word representation model on a vector space and link each natural language relational pattern to semantically equivalent knowledge base predicate. Our mapping result shows not only high quality, but also promising coverage on relational phrases compared to previous researches.
author2	Hsin-Hsi Chen
author_facet	Hsin-Hsi Chen Chin-Ho Lin 林勤和
author	Chin-Ho Lin 林勤和
spellingShingle	Chin-Ho Lin 林勤和 Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction
author_sort	Chin-Ho Lin
title	Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction
title_short	Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction
title_full	Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction
title_fullStr	Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction
title_full_unstemmed	Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction
title_sort	learning to map natural language statements into knowledge base representations for knowledge base construction
publishDate	2017
url	http://ndltd.ncl.edu.tw/handle/hcqdyq
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Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction

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