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- Paper.138_Review.0 type ReviewVersion.
- Paper.138_Review.0 issued "2001-02-07T08:39:00.000Z".
- Paper.138_Review.0 creator Paper.138_Review.0_Reviewer.
- Paper.138_Review.0 hasRating ReviewRating.1.
- Paper.138_Review.0 hasReviewerConfidence ReviewerConfidence.5.
- Paper.138_Review.0 reviews Paper.138.
- Paper.138_Review.0 issuedAt easychair.org.
- Paper.138_Review.0 issuedFor Conference.
- Paper.138_Review.0 releasedBy Conference.
- Paper.138_Review.0 hasContent "The paper is a prosecution of the work presented at ISWC 2019 related to the application of graph embeddings for the detection of synonyms in RDF graphs (citation [14] in the paper). The authors expand on that work and develop a novel approach by relying on rule mining techniques, particularly, the AMIE+ rule miner - citation [7]. The main argument for adopting a strategy based on the generation of declarative statements (horn clauses) is the explainability of the output, that better fits a workflow were humans may want to intervene and verify the quality of the output, for example, in a knowledge graph maintenance activity. I believe strongly in these type of techniques to be better, in general, than black-box algorithms, when introduced in a human-centred workflow. This is why I think the paper suffers from a lack of discussion on the pragmatic value of the method. The examples shown are quite obvious and could be found without the need of computing horn rules for 10 hours! How many synonym pairs were detected on DBpedia? 5? 10? 50? Also, how many of them could have been found without the support of this approach? I think these questions are crucial for demonstrating the value of the approach and answers must be in the paper. Moreover, the evaluation is performed by analysing the precision @ 500 but then only the first 3 rules from 1 experiment are shown. A longer list of examples would certainly help on understanding the relevance of the contribution. The evaluation compares the approach with the previous results (in a good set of variants) and a more naive baseline based on closed object sets (assuming that properties that have the same type of objects are most probably synonyms). The results are very positive and demonstrate the benefit of the approach. However, the techniques are not particularly innovative and it is quite surprising that these type of experiments come after the ones based on graph embeddings. The paper is well written and clear in most of its parts. A depiction of the general workflow would have helped. ---- I thank the authors for their clarifications that solve my concerns related to motivation and impact. I increased my score but ask the authors to perform the related changes for the camera-ready if accepted."".