Review Comment:
The new version of the manuscript demonstrates improvement from the prior version. The overarching RQs are now tied to the different sections and well contextualised. However, the manuscript still needs a fair amount of work.
The mapping of the Knowledge as a Trainee/Trainer/Peer with those by Aditya et al. introduced in the new version definitely helps to situate the reader and to clarify the contribution.
However, the mapping of the KG as a reviewer category needs further clarification. In particular, the authors claim that their KG as a reviewer category is transversal to two categories in Adityia et al.: (i) knowledge integrated in post-processing, and (ii) knowledge integrated in the intermediate layers of the DNN, because “we see knowledge layers in the DNN as an intermediate reviewing and validation process.” However, it seems to me that this claim conflicts with the definition presented earlier, that, in the KG as a reviewer configuration, the DNN is an independent component, which is applied ahead of the KG. In other words, the intermediate layers of the DNN do not tap into any external knowledge coming from the KG. Thus, the definition of DNN layers/ embeddings as intermediate validation steps seems to go against the separation between DNN and KG in the KG as a Reviewer setup. Thus, at present, the “KG as a Reviewer” category still appears as isomorphic to category (i) above in Aditya et al.
The structure and organisation of the paper also needs further work:
- In Section 3.1, certain definitions are accompanied by their related citation, whereas others are not. I would suggest that all terms in the background section are opportunely referenced.
- In Section 3, the organisation of paragraphs into feature extractor, visual feature extractor and semantic feature extractor works well. However, I am still a bit confused about how the different types of KGE are presented (Section 3.3). A new categorisation is introduced compared to the prior version: i.e., unsupervised vs. supervised KGE. These two categories are then accompanied with a third paragraph, where the authors discriminate between KGEs based on hyper-relational graphs and hypergraphs respectively.
I suggest to either keep the same categories of the prior version of the paper (provided that Knowledge Graph Embedding is still kept as a main heading, with Entity Embedding, Directed GE, etc. being subsections) or that the choice to introduce the distinction between supervised and unsupervised is opportunely motivated. What function does this categorisation serve in the paper? How is it linked to the other terms/categories presented earlier in the paper?
- Section 3.4 is titled “Training objectives for joint embeddings”. The term joint embedding is introduced here for the first time. Please provide definition earlier in the paper that clarifies the link between "joint training objective" and "joint embedding", so that the paper is more accessible to the non-expert.
- Section 4. As mentioned in my previous review, the general definition of visual transfer learning should be presented earlier in the paper, before diving into the details of KGE for transfer learning. I suggest it is incorporated back in the Background section before the definition of Knowledge Graphs. This structure reflects the main narrative: “visual transfer learning using Knowledge Graphs”.
Minor note: Lines 49-51 -> punctuation seem to be missing “Posed by graph irregularities (GAT [30]) None Euclidian graph convolutional methods yield significant improvements on graphs with hierarchical structure.” + typo “No Euclidean graph convolutional methods...”
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