Transformer-based Information Fusion for Knowledge Graph Completion
Authors: Sharmishtha Dutta, Alex Gittens, Mohammed J Zaki, Charu C. Aggarwal


Reviewer paper summaries: The paper proposes a method using transformer architecture for knowledge graph completion. The main contribution, as the authors argued, is the connection-biased attention module, which captures the relative distance between all entity pairs.
Summary of strengths: The proposed method outperform baselines methods.
Summary of weaknesses: The proposed method appears somewhat trivial to me. The Transformer architecture has already been employed as a fundamental backbone in knowledge graph completion. While the primary contribution is the commendable connection-biased attention module, I believe this alone is insufficient to demonstrate significant insight to the knowledge graph community.
Comments, suggestions and typos: More discussion and empirical validation are needed to verify the soundness of the proposed method.
Overall assessment scores: 2