Dynamic Entity Alignment with Attribute Integration
Authors: Zhehuan ZHAO, WenshuoFeng, Menghan Wang, Bo Xu, Yang Zhou


Reviewer paper summaries: This paper focuses on the problem of entity alignment in dynamic knowledge graph (KG). It aims to address the problems of varying influence of neighbors connected by different relations on the entity and the long-tail entity. The authors propose DEA-AttrAlign, which uses attention-based weighted averaging to address the varying influence, and integrates knowledge facts to enrich information of new entities. The proposed method is simple and easy-to-follow. Extensive experiments have been conducted and the results show that the proposed method has achieved optimal performance whether in the base KG or after KG growth.

However, the novelty of this work is limited. Using weighted averaging instead of mean pooling and employing widely used attribute information makes the proposed method merely an incremental improvement.

Some descriptions are unclear. In the introduction, the authors mention that they first utilize the attribute information to address problem 2. However, in the method section (3.2.1), I cannot find the relevant scheme design.

This paper lacks a literature review.

The experiments are insufficient. In the ablation study, there is no ablation of the attention mechanism, which is one of the innovations claimed by the authors.

The presentation of this paper needs improvement. Some symbols are mixed up. For example, "A" represents both attributes and the alignment set. Additionally, there are some typos.
Summary of strengths: - The proposed  DEA-AttrAlign is simple and easy-to-follow;
 -  Extensive experiments show the superiority of the proposed method compared with baselines.
Summary of weaknesses: - The novelty is limited.
 - Some descriptions are unclear.
 - This paper lacks a literature review.
 - The experiments are insufficient.
 - The presentation of this paper needs improvement.
Comments, suggestions and typos: See paper summary.
Overall assessment scores: 2