Inter-domain Multi-relational Link Prediction

TL;DR

This paper introduces a novel method for inter-domain multi-relational link prediction that uses optimal transport and MMD regularizers to improve the prediction of hidden relations between entities across different graphs.

Contribution

It proposes a new approach combining optimal transport and MMD regularizers to address inter-domain link prediction, a challenge not handled by existing intra-domain methods.

Findings

Optimal transport regularizer improves baseline performance.

The method effectively predicts hidden inter-domain relations.

Experiments on real-world datasets validate the approach.

Abstract

Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities. Similar to other graph-structured data, link prediction is one of the most important tasks on multi-relational graphs and is often used for knowledge completion. When related graphs coexist, it is of great benefit to build a larger graph via integrating the smaller ones. The integration requires predicting hidden relational connections between entities belonged to different graphs (inter-domain link prediction). However, this poses a real challenge to existing methods that are exclusively designed for link prediction between entities of the same graph only (intra-domain link prediction). In this study, we propose a new approach to tackle the inter-domain link prediction problem by softly aligning the entity distributions between different domains with optimal transport and maximum mean discrepancy regularizers. Experiments on real-world datasets show that optimal transport regularizer is beneficial and considerably improves the performance of baseline methods.