Paper: Revisit and Outstrip Entity Alignment: A Perspective of Generative Models (ICLR-2024)

Reviewer comment (Writing):
The objective of prior reconstruction could be made more comprehensible. There's ambiguity regarding the priors of features in different sub-embeddings. Specifically, when dealing with images, how does one retrieve its original, tangible feature?

Author response:
Sorry for the confusion. As stated in Section 3.6 Implementation, we move the details of reconstructing the tangible features of each module to Appendix B due to space constraints. Specifically, the reconstruction of neighborhood and attribute information can be viewed as multi-label classification. As for image reconstruction, we also hightlight the process in Section 4.3 Entity Synthesis Results. We state, "Since the image features are pretrained embeddings of a vision model, we use the image corresponding to the nearest neighbor of the reconstructed feature as the output image." In the revision, we have added a breif introduction to the reconstruction of images in Section 3.6.

Reviewer comment (Experiments):
The paper could provide a more extensive set of experiments to offer insights into the rationale behind the design of individual components.

Author response:
Thanks for your suggestion. We conducted ablation studies to assess the effectiveness of each component and present the results in Table 5. Specifically, we evaluated various alternative methods by removing different components while keeping all parameter settings identical to GEEA. We assessed the performance on both tasks and found that the complete GEEA achieved the best results, with any module removal leading to a performance loss.

    Additionally, we have incorporated several new experiments in the revision, thanks to the valuable comments from all reviewers. These experiments contribute to a better understanding of our method. Please let us know if you have any further suggestions.

Reviewer comment (Writing):
Could you elaborate on what constitutes the multi-modal information within the knowledge graph (KG) area?

Author response:
Sorry for the confusion. We have clarified the description in Section 4.1 Experiment Settings in the revised version. Our paper considers three different modalities: relational graphs (including the relations and entities linked to each entity), (textual) attributes, and images. We also present the statistics of each modality in each dataset in Table 7.

Reviewer comment (Writing):
What types of attribute information are being referred to in this context?

Author response:
Similar to previous works, we treat attribute information as bags of words and represent them using pre-trained word embeddings.

Reviewer comment (Writing):
What does $\mathcal{L}_{mf}$ represent within the paper?

Author response:
The reviewer may have overlooked the explanation on the next page, specifically after Equation (22). "where $\mathcal{F} = \{x\rightarrow x, y\rightarrow y, x\rightarrow y, y\rightarrow x\}$ is the set of all flows, and $\{g, a, i, ...\}$ is the set of all available modalities."  In this context, $m$ denotes the modality (e.g, $g$), and f denotes the flow (e.g., $x\rightarrow y$). $\mathcal{L}_{m_f}$ is similar to

    $\mathcal{L}_{g, x\rightarrow y}$, which is defined in Equation (19) (We have to insert a newline here because the original Latex formula collapsed).