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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| Format: | Others |
| Language: | en_US |
| Published: |
2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/hcqdyq |
| Summary: | 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.
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