Analogical Inference for Multi-Relational Embeddings

TL;DR

This paper introduces a scalable, differentiable framework for multi-relational embeddings that leverages analogical properties, unifies existing methods, and outperforms benchmarks in knowledge graph inference.

Contribution

It presents a novel analogical inference framework for multi-relational embeddings that unifies and improves upon existing methods with theoretical and computational advantages.

Findings

Outperforms baseline methods on benchmark datasets

Unifies several existing embedding methods

Offers scalable and theoretically sound optimization

Abstract

Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the \textit{analogical} properties of the embedded entities and relations. By formulating the learning objective in a differentiable fashion, our model enjoys both theoretical power and computational scalability, and significantly outperformed a large number of representative baseline methods on benchmark datasets. Furthermore, the model offers an elegant unification of several well-known methods in multi-relational embedding, which can be proven to be special instantiations of our framework.