A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?

With the new and significantly improved version of the survey, all my comments and concerns have been resolved.

SWJ Guidelines:

(1) Suitability as introductory text, targeted at researchers, PhD students, or practitioners, to get started on the covered topic.

Answer: Work is accessible to a broad audience, even if the focus is on literals.

(2) How comprehensive and how balanced is the presentation and coverage.

Answer: Good and extensive review of papers, with experimental evaluations and details on runtimes.

(3) Readability and clarity of the presentation.

Answer: Well written and clear.

(4) Importance of the covered material to the broader Semantic Web community.

Answer: Very important for the semantic web community.

Thanks to the authors for the careful work on the paper. All my comments and suggestions were taken into consideration and applied when needed. Also, many thanks for explaining to me those things that I've misunderstood while reading the paper. I read the other reviews and the responses and after reading again the paper I think that it is in a very good shape. As I said in my previous review I think this is a significant contribution and of interest for the SW community.

The authors addressed all my comments and extended the previous version substantially. Although the addition of a triple classification task in the experiments would be very valuable to this survey, I agree that the link prediction is a common task among all published papers. Therefore, I recommend the acceptance of this survey.

I have only three very minor points.

1) In the abstract, I would change the following passage below to make clear that the authors are focusing on literals contained in a given KG and not from external data.

...entities and relations in a KG but also the unstructured
information represented as literals such as text, numerical...

to

...entities and relations in a KG but also **its** unstructured
information represented as literals such as text, numerical...

2) In "The categories are translation based models, semantic matching models, models incorporating entity types, models incorporating relation paths, models using logical rules, models with temporal information, and models using graph structures." and Table 1 as well, is it necessary to have "Models using" all the time? The authors could find a better solution.

3) Point to this work, https://arxiv.org/pdf/1911.03903.pdf, as a possible new evaluation method in the discussion section. It was recently accepted at ACL 2020 and has shown some drawbacks with current evaluation methods for KGEs.

I am happy to see that the authors have addressed all the points I raised very thoroughly. Thank you for the hard work you put into this!

I have just one more small remark: when it comes to combining information from the graph and other sources, i.e., images and text literals, one very simple, yet potentially effective approach is to simply generate embeddings for each of the modalities, and then combine the vectors by simple concatenation and further reduction, e.g., by PCA or a neural autoencoder. This has been demonstrated in [1]. I'm not sure whether it is applicable, e.g., on FB15k, but it would be worth mentioning.

[1] Thoma et al.: Towards Holistic Concept Representations:Embedding Relational Knowledge, Visual Attributes, and Distributional Word Semantics. ISWC 2017