Paper Info
Title
Representation learning over multiple knowledge graphs for knowledge graphs alignment.
Abstract
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Mostly current works have demonstrated the benefits of knowledge graph embedding in single knowledge graph completion, such as relation extraction. The most significant distinction between multiple knowledge graphs embedding and single knowledge graph embedding is that the former must consider the alignments between multiple knowledge graphs which is very helpful to some applications built on multiple KGs, such as KB-QA and KG integration. In this paper, we proposed a new automatic representation learning model over Multiple Knowledge Graphs (MGTransE) by adopting a bootstrapping method. More specifically, MGTransE consists of three core components: Structure Model, Semantically Smooth Embedding Model and Iterative Smoothness Model. The experiment results on two real-world datasets show that our method achieves better performance on two new multiple KGs tasks compared with state-of-the-art KG embedding models and also preserves the key properties of knowledge graph embedding on traditional single KG tasks as compared to those methods learned from single KG.
Year
DOI
Venue
2018
10.1016/j.neucom.2018.08.070
Neurocomputing
Keywords
Field
DocType
Representation learning,Knowledge graph embedding,Knowledge graph
ENCODE,Knowledge graph,Embedding,Bootstrapping,Theoretical computer science,Artificial intelligence,Smoothness,Feature learning,Mathematics,Machine learning,Relationship extraction
Journal
Volume
ISSN
Citations 
320
0925-2312
0
PageRank 
References 
Authors
0.34
27
7
Name
Order
Citations
PageRank
Wenqiang Liu183.17
Jun Liu217825.96
Mengmeng Wu392.57
Samar Abbas400.34
Yuzhong Qu572662.49
Bifan Wei6386.39
Qinghua Zheng71261160.88