TorusE: Knowledge Graph Embedding on a Lie Group

11/15/2017
by   Takuma Ebisu, et al.
0

Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models map entities and relations in a knowledge graph to a vector space and predict unknown triples by scoring candidate triples. TransE is the first translation-based method and it is well known because of its simplicity and efficiency for knowledge graph completion. It employs the principle that the differences between entity embeddings represent their relations. The principle seems very simple, but it can effectively capture the rules of a knowledge graph. However, TransE has a problem with its regularization. TransE forces entity embeddings to be on a sphere in the embedding vector space. This regularization warps the embeddings and makes it difficult for them to fulfill the abovementioned principle. The regularization also affects adversely the accuracies of the link predictions. On the other hand, regularization is important because entity embeddings diverge by negative sampling without it. This paper proposes a novel embedding model, TorusE, to solve the regularization problem. The principle of TransE can be defined on any Lie group. A torus, which is one of the compact Lie groups, can be chosen for the embedding space to avoid regularization. To the best of our knowledge, TorusE is the first model that embeds objects on other than a real or complex vector space, and this paper is the first to formally discuss the problem of regularization of TransE. Our approach outperforms other state-of-the-art approaches such as TransE, DistMult and ComplEx on a standard link prediction task. We show that TorusE is scalable to large-size knowledge graphs and is faster than the original TransE.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/05/2019

Graph Pattern Entity Ranking Model for Knowledge Graph Completion

Knowledge graphs have evolved rapidly in recent years and their usefulne...
research
09/01/2020

More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings

RDF2vec is an embedding technique for representing knowledge graph entit...
research
07/05/2017

Complex and Holographic Embeddings of Knowledge Graphs: A Comparison

Embeddings of knowledge graphs have received significant attention due t...
research
05/26/2018

From Knowledge Graph Embedding to Ontology Embedding: Region Based Representations of Relational Structures

Recent years have witnessed the enormous success of low-dimensional vect...
research
05/07/2018

Improving Knowledge Graph Embedding Using Simple Constraints

Embedding knowledge graphs (KGs) into continuous vector spaces is a focu...
research
08/10/2017

Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs

Knowledge graphs and vector space models are robust knowledge representa...
research
10/13/2020

Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations

Knowledge Graph Embeddings (KGEs) have shown promising performance on li...

Please sign up or login with your details

Forgot password? Click here to reset