Earticle

초록

영어

This paper proposes a flexible translation-based knowledge graph embedding that learns unobserved entities by moving positions of embedding vectors from existed embedding space. To reflect unobserved entities, previous methods tend to learn knowledge graphs all over again. This process causes high cost of calculation. Thus, this paper introduces an adjusting method which moves positions of learned embedding vectors according to unobserved entity. This idea is based on TransE model that is a one of translation-based methods. According to experiments, the proposed method shows the plausibility at link prediction task and triple classification task. These experimental results prove that reducing learning cost is a crucial issue for embedding knowledge graphs.

목차

Abstract
 1. Introduction
 2. Related Researches
 3. A Translation-Based Knowledge Graph Embedding Adapting Unobserved Entities
  3.1. TransE
  3.2. Applying New Entities to Pre-trained Embedding Space
 4. Experiments
  4.1. Datasets
  4.2. Link Prediction
  4.3. Triple Classification
 5. Conclusion
 References

키워드

저자

• A-Yeong Kim [ School of Computer Science and Engineering, Kyungpook National University, Korea ]
• Seong-Bae Park [ School of Computer Science and Engineering, Kyungpook National University, Korea ]
• Sang-Jo Lee [ School of Computer Science and Engineering, Kyungpook National University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605