<scp>IFGAN</scp>: Information fusion generative adversarial network for knowledge base completion-Reference-Cited by-同舟云学术

IFGAN: Information fusion generative adversarial network for knowledge base completion

Published:2022-03-08 Issue:6 Volume:39 Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Zhang Tianchen¹^ORCID,Bi Zhongqin²^ORCID,Shan Meijing³,Li Yongbin²

Affiliation:

1. State Grid Shanghai Information & Telecommunication Company Shanghai China

2. College of Computer Science and Technology Shanghai University of Electric Power Shanghai China

3. Institute of Information Science and Technology East China University of Political Science and Law Shanghai China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12984

Reference49 articles.

1. Berant J. &Liang P.(2014).Semantic parsing via paraphrasing. InProceedings of the 52nd annual meeting of the association for computational linguistics (volume 1: Long papers)(pp. 1415–1425).

2. Knowledge Transfer for Out-of-Knowledge-Base Entities: Improving Graph-Neural-Network-Based Embedding Using Convolutional Layers

3. Bollacker K. Evans C. Paritosh P. Sturge T. &Taylor J.(2008).Freebase: A collaboratively created graph database for structuring human knowledge. InProceedings of the 2008 acm sigmod international conference on management of data(pp. 1247–1250).

4. Translating embeddings for modeling multi‐relational data;Bordes A.;Advances in Neural Information Processing Systems,2013

5. Kbgan: Adversarial learning for knowledge graph embeddings;Cai L.;In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2017