Thank you very much for your valuable and detailed response. We truly appreciate the opportunity to address your concerns and provide further clarifications.
**Writing and grammartical errors.**
We have carefully addressed the identified typos and conducted a thorough review of the entire paper to ensure the clarity.
1. Improper use of references.
We have revised the sentence by reducing the number of cited papers.
2. Grammar issues.
We have carefully corrected all typos and highlighted them in blue in the new revised version. Thank you again for your detailed comments.
**The definitions of modalities are presented in an unclear manner. It is not immediately apparent how images can be represented as discrete features.**
We have revised Section 2.1 as Preliminaries, which now consists of three subsubsections: Entity Alignment, Entity Synthesis, and Generative Models. In the Entity Alignment section, we have added an example to illustrate multi-modal settings:
For instance, the relational graph feature of an entity *Star Wars (film)* is represented as triplets, such as *(Star Wars (film), founded by, George Lucas)*. Similarly, the attribute feature is represented as attribute triplets, e.g., *(Star Wars (film), title, Star Wars (English))*. For the image feature, we follow the existing multi-modal EEA works to use a constant pretrained embedding from a vision model as the image feature of *Star Wars (film)*.
We have also added a similar example in the Entity Synthesis section:
For instance, to reconstruct the neighborhood (or attribute) information of *Star Wars (film)* from its embedding, we can leverage a decoder module to convert the embedding into a probability distribution of all candidate entities (or attributes). As the image features are constant pretrained embeddings, we can use the image corresponding to the nearest neighbor of the reconstructed image embedding of *Star Wars (film)* as the output image.
**The paper could benefit from a stronger motivation, particularly regarding the necessity of a generative approach to EA and the rationale behind Entity Synthesis.**
Thank you very much for your suggestion. We have updated the introduction section to include an explanation of the motivation for studying a generative approach:
In this paper, we demonstrate that adopting a generative perspective in studying EEA allows us to interpret negative sampling algorithms and GAN-based methods. Furthermore, we provide theoretical proof that optimizing the generative objectives contributes to minimizing the EEA objective, thereby enhancing overall performance.
We have also included an example to illustrate the rationale behind entity synthesis:
In fact, entity alignment is not the ultimate aim of many applications. The results of entity alignment are used to enrich each other's KGs, but there are often entities in the source KG that do not have aligned counterparts in the target KG, known as *dangling entities*.
**For instance, a source entity *Star Wars (film)* may not have a counterpart in the target KG, which means we cannot directly enrich the target KG with the information of *Star Wars (film)* via entity alignment. However,
if we can convert entities like *Star Wars (film)* from the source KG to the target KG,** it would save a major expenditure of time and effort for many knowledge engineering tasks, such as knowledge integration and fact checking.
**The structure of the paper requires refinement. Essential preliminary knowledge is introduced after Section 2.**
Great idea. As mentioned earlier, we have integrated all preliminary knowledge into Section 2.1 Preliminaries. We have moved the preliminary knowledge from Section 3 to the Generative Models subsubsection, in which we introduce the objective of generative models and use VAE as an exmaple for illustratation.