Information-Theoretic Minimal Sufficient Representation for Multi-Domain Knowledge Graph Completion

Multi-domain knowledge graph completion (MKGC) seeks to predict missing triples in a target KG by leveraging triples from multiple KGs in different domains (e.g., languages or sources). Existing studies typically learn and fuse multi-domain KG representations solely with alignments or fusion modules, which can be affected by redundant information within KGs. This issue can conceal task-relevant information in representations, impeding further improvements when scaling to numerous KGs. To this end, we propose IMKGC, an information-theoretic MKGC framework to learn minimal sufficient representations. In particular, IMKGC learns entity representations by explicitly preserving endogenous contextual information within each KG, exogenous complementary information from other KGs, and consistent information of equivalent entities, while suppressing redundant information through variational constraints. Furthermore, we achieve compressed relation representations with a devised relation reasoning decoder that captures relatedness among relations, also improving triple prediction. Extensive experiments on 14 KGs in three benchmark datasets demonstrate that IMKGC significantly outperforms previous state-of-the-art methods, especially in redundant scenarios.