A structural representation learning for multi-relational networks

Lin Liu, Xin Li*, Kwok Wai CHEUNG, Chengcheng Xu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Most of the existing multi-relational network embedding methods, e.g., TransE, are formulated to preserve pair-wise connectivity structures in the networks. With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations. Scalable learning algorithms are derived using the stochastic gradient descent algorithm and negative sampling. Extensive experiments on real multi-relational network datasets of WordNet and Free-base demonstrate the efficacy of the proposed model when compared with the state-of-the-art embedding methods.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, (IJCAI-17)
EditorsCarles Sierra
PublisherAAAI press
Pages4047-4053
Number of pages7
ISBN (Print)9780999241103
DOIs
Publication statusPublished - Aug 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia, Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume0
ISSN (Print)1045-0823

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Country/TerritoryAustralia
CityMelbourne
Period19/08/1725/08/17

Scopus Subject Areas

  • Artificial Intelligence

User-Defined Keywords

  • Natural Language Processing
  • Information Extraction
  • Information Retrieval
  • Machine Learning
  • Multi-instance/Multi-label/Multi-view learning
  • Knowledge-based Learning

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