Review Comment:
Summary of the paper:
In this paper, authors evaluate the quality of twelve RDF2vec variants on GEval benchmark and DLCC benchmark, and compare them to seven knowledge graph embedding methods. They analyze the results from the perspective that which ontological patterns in a knowledge graph can actually be learned by different embeddings.
I would like to order my review following the response(R) from authors by adding comments(C) on each point:
Q: Considering that some RDF2vec variants mentioned in the related work are not included in the evaluation, and DLCC is already proposed in previous work, the originality and the novelty of this paper are limited.
R: We agree that this paper builds on quite a few already published parts. However, it provides a comprehensive and compact account of a topic which can otherwise only be obtained by reading a bunch of different papers. Moreover, it still contains quite a few novel aspects. In particular, the discussion of the representational power of the different RDF2vec variants has not been published before.
As far as the variants from the related work section, we have added an explanation why certain extensions were not considered in the experiments.
C: I appreciate that authors add reasons for choosing extensions and agree the main contribution of this paper comes from the comprehensive experimental study and discussion on the representational power of the different variants.
Q: Quality of writing: The paper is easy to understand. Some paragraphs are exactly the same as paragraphs in [17], which is not expected to happen.
R: As this paper builds on top of existing works, we would assume that overlaps, both in terms of content as well as in terms of wording, are acceptable. The submission guidelines of SWJ state that “results previously published at conferences or workshops may be submitted as extended versions.” In our opinion, this does not require a complete rephrasing of the original publications’ contents.
C: I agree.
Q: Some of the statement is improper and should be rewritten.
R: We have tried to rephrase them such that it becomes clear which parts are genuine contributions in this submission and which are contributions in the papers that this submission is an extended version of.
C: I appreciate the updates.
Q: In the related work section, it is said [18] distinguish five different techniques for KGE, while such category is proposed for graph embedding which is a broader topic. Is it proper to use this category for KGE?
R: The authors of [18] list different kinds of graphs, including knowledge graphs, and make no restrictions in the sense that certain categories are only valid for particular kinds of graphs. Exceptions for methods which are not commonly used for KGE are mentioned in the paper. Therefore, we deem the categorization valid. We have added a corresponding remark in the paper.
C: I agree.
Q: For evaluation of KGE, MRR is also a common metric which is missing in related work of knowledge graph embedding evaluation.
R: MRR is mostly used in evaluations for link prediction, however, link prediction is not among the tasks used in this paper. For an evaluation of RDF2vec based approaches on link prediction tasks, we refer to our paper “Knowledge Graph Embedding for Data Mining vs. Knowledge Graph Embedding for Link Prediction - Two Sides of the same Coin?”, also published in SWJ.
C: I agree that MRR is not chosen to be used in this paper. But considering that this is a common metric, it could be added to the subsection "knowledge graph embedding" in Related work and put together with rank/HITT@10.
Q: SG and CBOW are first mentioned in the first line of section 3.2 without explanations.
R:We have added a general introduction into RDF2vec in that section, including SG and CBOW.
C: I appreciate the updates.
Q: It is not explained what is the difference between tick and (tick) in table 2.
R:This is obsolete, as the corresponding parts of the table were removed by suggestion of reviewer 1. The intended semantics were: tick means: “expected to be learnable”, (tick) means: “expected to be learnable to a certain extent”.
C: OK
Q: Figures 2 and 3 are the same as in [17]
R: That is true. We have added the corresponding source to the figure caption.
C: Fine.
Q: As a work target at evaluating RDF2vec variants, it needs more in deep analysis of RDF2vec variants based on experiment results.
R: We have evaluated the RDF2vec variants on various datasets (GEval, and the two datasets of DLCC).
C: I agree.
Q: In summary, my main concern about this paper is the limited contribution and novelty.
R: We see that point, but we consider the paper rather an extended version of previously published works, rather than a new paper from scratch.
C: I agree.
Following are some minor points:
1. Page 3: "machine learning applications section 4" -> "machine learning applications in section 4"
2. Page 5: "by word embedding algorithm RDF2vec" -> "by word embedding algorithm word2vec"
3. Suggest putting Table 6 and 7 after Table 4.
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