Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Improving The Performance of Triple Generation Based on Distant Supervision By Using Semantic Similarity

의미 유사도를 활용한 Distant Supervision 기반의 트리플 생성 성능 향상

  • Received : 2016.01.12
  • Accepted : 2016.03.16
  • Published : 2016.06.15
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Abstract

The existing pattern-based triple generation systems based on distant supervision could be flawed by assumption of distant supervision. For resolving flaw from an excessive assumption, statistics information has been commonly used for measuring confidence of patterns in previous studies. In this study, we proposed a more accurate confidence measure based on semantic similarity between patterns and properties. Unsupervised learning method, word embedding and WordNet-based similarity measures were adopted for learning meaning of words and measuring semantic similarity. For resolving language discordance between patterns and properties, we adopted CCA for aligning bilingual word embedding models and a translation-based approach for a WordNet-based measure. The results of our experiments indicated that the accuracy of triples that are filtered by the semantic similarity-based confidence measure was 16% higher than that of the statistics-based approach. These results suggested that semantic similarity-based confidence measure is more effective than statistics-based approach for generating high quality triples.

기존의 패턴기반 트리플 생성 시스템은 distant supervision의 가정으로 인해 오류 패턴을 생성하여 트리플 생성 시스템의 성능을 저하시키는 문제점이 있다. 이 문제점을 해결하기 위해 본 논문에서는 패턴과 프로퍼티 사이의 의미 유사도 기반의 패턴 신뢰도를 측정하여 오류 패턴을 제거하는 방법을 제안한다. 의미 유사도 측정은 비지도 학습 방법인 워드임베딩과 워드넷 기반의 어휘 의미 유사도 측정 방법을 결합하여 사용한다. 또한 한국어 패턴과 영어 프로퍼티 사이의 언어 및 어휘 불일치 문제를 해결하기 위해 정준 상관 분석과 사전 기반의 번역을 사용한다. 실험 결과에 따르면 제안한 의미 유사도 기반의 패턴 신뢰도 측정 방법이 기존의 방법보다 10% 높은 정확률의 트리플 집합을 생성하여, 트리플 생성 성능 향상을 증명하였다.

Keywords

Acknowledgement

Grant : WiseKB: 빅데이터 이해 기반 자가학습형 지식베이스 및 추론 기술 개발

Supported by : 정보통신기술진흥센터

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