Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

2-2022

Abstract

A key to knowledge graph embedding (KGE) is to choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose a unified perspective of embedding and introduce uncertainty into KGE from the view of group theory. Our model can incorporate existing models (i.e., generality), ensure the computation is tractable (i.e., efficiency) and enjoy the expressive power of complex random variables (i.e., expressiveness). The core idea is that we embed entities/relations as elements of a symmetric group, i.e., permutations of a set. Permutations of different sets can reflect different properties of embedding. And the group operation of symmetric groups is easy to compute. In specific, we show that the embedding of many existing models, point vectors, can be seen as elements of a symmetric group. To reflect uncertainty, we first embed entities/relations as permutations of a set of random variables. A permutation can transform a simple random variable into a complex random variable for greater expressiveness, called a normalizing flow. We then define scoring functions by measuring the similarity of two normalizing flows, namely NFE. We construct several instantiating models and prove that they are able to learn logical rules. Experimental results demonstrate the effectiveness of introducing uncertainty and our model.

Keywords

Complex random variables, Embeddings, Euclidean spaces, Graph embeddings, Knowledge graphs, Point wise, Representation space, Space Vector, Symmetric groups, Uncertainty

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, DC, February 7-14

Volume

37

First Page

4756

Last Page

4764

ISBN

9781577358800

Publisher

AAAI Press

City or Country

USA

Download

Share

COinS