RSCF: Relation-Semantics Consistent Filter for Entity Embedding of Knowledge Graph
Junsik Kim, Jinwook Park, Kangil Kim

TL;DR
This paper introduces RSCF, a novel relation-semantics consistent filter for knowledge graph embeddings, improving semantic consistency and significantly enhancing performance in knowledge graph completion tasks.
Contribution
RSCF employs shared affine transformations, rooted entity transformations, and normalization to maintain semantic consistency in entity embeddings, addressing limitations of previous methods.
Findings
RSCF outperforms state-of-the-art KGE methods in knowledge graph completion.
RSCF demonstrates robustness across all relations and frequencies.
Semantic consistency preservation improves embedding quality.
Abstract
In knowledge graph embedding, leveraging relation specific entity transformation has markedly enhanced performance. However, the consistency of embedding differences before and after transformation remains unaddressed, risking the loss of valuable inductive bias inherent in the embeddings. This inconsistency stems from two problems. First, transformation representations are specified for relations in a disconnected manner, allowing dissimilar transformations and corresponding entity embeddings for similar relations. Second, a generalized plug-in approach as a SFBR (Semantic Filter Based on Relations) disrupts this consistency through excessive concentration of entity embeddings under entity-based regularization, generating indistinguishable score distributions among relations. In this paper, we introduce a plug-in KGE method, Relation-Semantics Consistent Filter (RSCF). Its entity…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsData Quality and Management · Semantic Web and Ontologies · Advanced Graph Neural Networks
