Fuzzy TransE: A Fuzzy Type Semantic-Based Learning for Translating Embedding Models

Translating Embeddings (TransE) is a widely adopted model for knowledge graph completion which represents relationships as vector translations in an embedding space. While it is known for its efficiency and simplicity, TransE struggles with capturing complex relational patterns, particularly in one-to-many (1-to-N), many-to-one (N-to-1), and many-to-many (N-to-N) relationships. This challenge arises from its rigid distance-based formulation. In this paper, we propose an enhancement to TransE that integrates fuzzy type constraints, which provide a soft regularization of entity embeddings based on their degree of membership in semantic categories (e.g., city, person). This extension aims to improve the model’s ability to represent intricate relationships and enhance the overall performance in knowledge graph tasks.