There's a lack of detailed explanation of why a particular model component is chosen and not the alternatives, sometimes an intuitive example or reason behind the choice would be helpful, or if there was an implementation of an alternative but later was determined inferior, there should be a description of it. For example, why using attention as the aggregator in graph neural network? have you tried other gnn architectures? how are their performance?
The example in fig1 seems a bit weird since it's clear from the entity labels that "Boris Johnson" and "David Cameron" are different entities despite them having the same neighbors or structures in the graph. Knowledge graphs are not just a graph, they also have labels, entities types, schemas, and other information associated with them that can help align and differentiate entities, temporal information is just another information that can help. So even for models without using temporal information, they may still be able to differentiate "Boris Johnson" and "David Cameron" by simply looking into their labels. Perhaps, in the entity alignment baseline models, there should be a model that uses this kind of label or textual information. Also, all the baseline models in EA do not use time information, this seems to be an unfair comparison as the improvement might simply come from this introduction of additional (temporal) information. So it would be better if there could be a baseline model that uses temporal information and another baseline model that uses another additional information (such as textual or label information) to 1) see whether TR-GAT can outperform other temporal kge models in EA task and 2) understand whether temporal info is more useful or textual (label) info is more useful in EA task.
Wikidata12k is also a popular benchmark dataset for tkg completion and it is missing in the experiment
A popular task in tkg models and related papers is time prediction task which predictions the time interval of a particular statement (s, r, o). For example this paper (https://arxiv.org/abs/2005.05035). This task seems to be missing in the paper.
It seems that the EA is possible because two KGs are trained simultaneously. In this case, can this (training 2 KGs together and having an additional margin rank loss) be applied to existing TKG embedding models and then create additional baseline models? How would these additional baselines perform compared with TR-GAT?
The TR-GAT models doesn't not seem to beat existing models on ICEWS05-15 dataset though.
1) see whether TR-GAT can outperform other temporal kge models in EA task and