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- Paper.89_Review.0 type ReviewVersion.
- Paper.89_Review.0 issued "2001-01-30T13:44:00.000Z".
- Paper.89_Review.0 creator Paper.89_Review.0_Reviewer.
- Paper.89_Review.0 hasRating ReviewRating.2.
- Paper.89_Review.0 hasReviewerConfidence ReviewerConfidence.3.
- Paper.89_Review.0 reviews Paper.89.
- Paper.89_Review.0 issuedAt easychair.org.
- Paper.89_Review.0 issuedFor Conference.
- Paper.89_Review.0 releasedBy Conference.
- Paper.89_Review.0 hasContent "In this study the authors design an approach that can recommend potential collaborators. They use a large knowledge graph to train embeddings I have one particular concern. By creating the negative sample, considering that it is based on random replacement of subject/object, is there chance that you are creating instances that are certainly not true in the present but very likely in the future? For instance, if you create (A, co-author, B) this is definitely not true at the present time as you have your knowledge graph evidencing that such triple does not exist. However, the fact that it currently does not exist does not mean that it will not happen in the future. By creating such sample, you are letting you model learn that such triple is a bad sample as instead it did not occur yet. At page 12, it would be interesting to observe more details, for the recommended authors and how many of them you kept because the was not co-authorship, not belonging to the same organisation and so on. Minor: References 19 and 20 are the same References 24 and 25 are the same After the REBUTTAL: Thank you for addressing my concerns"".