Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Understand

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.