RSCF: Relation-Semantics Consistent Filter for Entity Embedding of Knowledge Graph

ACL ARR 2024 December Submission1790 Authors

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), containing more consistent entity-transformation characterized by three features: 1) shared affine transformation of relation embeddings across all relations, 2) rooted entity-transformation that adds an entity embedding to its change represented by the transformed vector, and 3) normalization of the change to prevent scale reduction.
To amplify the advantages of consistency that preserve semantics on embeddings, RSCF adds relation transformation and prediction modules for enhancing the semantics.
In knowledge graph completion tasks with distance-based and tensor decomposition models,
RSCF significantly outperforms state-of-the-art KGE methods, showing robustness across all relations and their frequencies.

Paper Type: Long

Research Area: Information Extraction

Research Area Keywords: knowledge base construction, knowledge graph completion, knowledge graph embedding

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings

Languages Studied: English

Submission Number: 1790