Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

TL;DR

This paper introduces a self-supervised adaptive graph alignment method for multilingual knowledge graph completion, effectively leveraging limited seed alignments and controlling noise to improve fact prediction across languages.

Contribution

It proposes a novel self-supervised adaptive graph alignment approach that fuses multiple KGs and dynamically identifies alignment pairs, addressing limitations of previous methods.

Findings

Outperforms existing methods on multilingual DBPedia KG

Effective noise control via relation-aware attention weights

Demonstrates robustness on industrial multilingual E-commerce KG

Abstract

Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages. However, language alignment used in prior works is still not fully exploited: (1) alignment pairs are treated equally to maximally push parallel entities to be close, which ignores KG capacity inconsistency; (2) seed alignment is scarce and new alignment identification is usually in a noisily unsupervised manner. To tackle these issues, we propose a novel self-supervised adaptive graph alignment (SS-AGA) method. Specifically, SS-AGA fuses all KGs as a whole graph by regarding alignment as a new edge type. As such, information propagation and noise influence across KGs can be adaptively controlled via relation-aware attention weights. Meanwhile, SS-AGA features a new pair generator that dynamically captures potential alignment pairs in a self-supervised paradigm. Extensive experiments on both the public multilingual DBPedia KG and newly-created industrial multilingual E-commerce KG empirically demonstrate the effectiveness of SS-AG