Matches in ESWC 2020 for { ?s ?p The paper presents a framework for evaluate graph embeddings on several tasks. This framework includes both machine learning tasks (e.g., classification, regression, clustering) and semantic tasks (e.g., entity relatedness, document similarity). The paper is well written and clear. The framework is well explained and the given examples help to understand the user cases. The choices of tasks and parameters are reasonably justified. However, while I understand the rationale given for not including link prediction, I think that one of the main advantage of these kinds of framework is to be able to perform comprehensive evaluations that include all popular tasks. Therefore, it may be useful to include also this task in the future. I could not find the Permanent URL for the resource at the beginning of the paper. The authors should include one in the camera ready. The related work is well done and fairly comprehensive. However, I would suggest to briefly mention also the work relevant to the evaluation of ontology/knowledge graphs via relevant tasks (basically the “application and usage impact” category of the survey “Zablith et al. Ontology evolution: a process-centric survey”, e.g., https://www.zora.uzh.ch/id/eprint/174974/1/00-iswc2019-pernischova-dc.pdf, http://oro.open.ac.uk/55536/1/ISWC2018_Research.pdf). It would benefit the paper to include more details about the scalability of the frameworks. How does it handle parallelization? How long did it take for the evaluation discussed in section 5? What kind of machine did you use? How would the size of the vectors affect the computational time for the various tasks? The evaluation is not particularly comprehensive, since it focuses only on two tasks (classification and regression). Why not showcase all the tasks supported by the system? Figure 3 is not very readable. Please, improve it or change it to a table. Finally, I would suggest choosing a name for the framework, in order to facilitate people that would like to refer to it. In conclusion, the framework presented in the paper appears to be quite a useful resource for the communities of Semantic Web and Machine Learning.". }
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- Paper.191_Review.1 hasContent "The paper presents a framework for evaluate graph embeddings on several tasks. This framework includes both machine learning tasks (e.g., classification, regression, clustering) and semantic tasks (e.g., entity relatedness, document similarity). The paper is well written and clear. The framework is well explained and the given examples help to understand the user cases. The choices of tasks and parameters are reasonably justified. However, while I understand the rationale given for not including link prediction, I think that one of the main advantage of these kinds of framework is to be able to perform comprehensive evaluations that include all popular tasks. Therefore, it may be useful to include also this task in the future. I could not find the Permanent URL for the resource at the beginning of the paper. The authors should include one in the camera ready. The related work is well done and fairly comprehensive. However, I would suggest to briefly mention also the work relevant to the evaluation of ontology/knowledge graphs via relevant tasks (basically the “application and usage impact” category of the survey “Zablith et al. Ontology evolution: a process-centric survey”, e.g., https://www.zora.uzh.ch/id/eprint/174974/1/00-iswc2019-pernischova-dc.pdf, http://oro.open.ac.uk/55536/1/ISWC2018_Research.pdf). It would benefit the paper to include more details about the scalability of the frameworks. How does it handle parallelization? How long did it take for the evaluation discussed in section 5? What kind of machine did you use? How would the size of the vectors affect the computational time for the various tasks? The evaluation is not particularly comprehensive, since it focuses only on two tasks (classification and regression). Why not showcase all the tasks supported by the system? Figure 3 is not very readable. Please, improve it or change it to a table. Finally, I would suggest choosing a name for the framework, in order to facilitate people that would like to refer to it. In conclusion, the framework presented in the paper appears to be quite a useful resource for the communities of Semantic Web and Machine Learning."".