Knowledge graph embedding (KGE) learns latent vector representations of named entities (i.e., vertices) and relations (i.e., edge labels) of knowledge graphs. Herein, we address two problems in KGE. First, relations may belong to one or multiple categories, such as functional, symmetric, transitive, reflexive, and so forth; thus, relation categories are not exclusive. Some relation categories cause non-trivial challenges for KGE. Second, we found that zero gradients happen frequently in many translation based embedding methods such as TransE and its variations. To solve these problems, we propose i) converting a knowledge graph into a bipartite graph, although we do not physically convert the graph but rather use an equivalent trick; ii) using multiple vector representations for a relation; and iii) using a new hinge loss based on energy ratio(rather than energy gap) that does not cause zero gradients. We show that our method significantly improves the quality of embedding.
|Title of host publication||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Editors||Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2019 Dec|
|Event||2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States|
Duration: 2019 Dec 9 → 2019 Dec 12
|Name||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Conference||2019 IEEE International Conference on Big Data, Big Data 2019|
|Period||19/12/9 → 19/12/12|
Bibliographical noteFunding Information:
This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-05-ETRI). This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energys National Nuclear Security Administration under contract DE-NA-0003525. Noseong Park is the corresponding author.
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management