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Matches in ESWC 2020 for { ?s ?p The paper describes a rule-based approach for detecting equivalent or synonymous properties in a KG. While this approach gives promising results compared to embedding approaches, the experiments with only two datasets DBpedia and Wikidata raise concerns about the applicability and scalability of this approach in different datasets. Since this approach is a data-driven, more experiments are expected with the real-world datasets having the problem, and an in-depth discussion on the computational complexity and hardware requirements would be desirable. Positive: - The rule-based approach for finding synonymous properties is well-presented and easy to follow. - The results in the DBpedia is comparable to the best embedding approach HolE - The rule-based approach offers explanations over embedding approach - The datasets and source code are provided. Negative: - The previous work by the same authors had performed the experiments with Freebase. However, they do not include it in this paper for comparison. What is the rationale behind not providing the experiments with Freebase? Is it not implemented? Or is the result not good? Since this approach is data-driven, we expect to see the experiments in more different datasets. - DBpedia is the only dataset with a real problem and the proposed approach gives a comparable performance, not superior to HolE. - Wikidata doesn’t have synonymous properties and the authors introduced synthetic synonyms into it. It would be more convincing to find the datasets having real problems than creating a “fake” one, especially for a data-driven approach. - While the authors state that their approach is scalable, we do not see any analytical discussion on the scalability. Instead, the evaluation was performed on sampling datasets with reduced sizes to ~11-12M triples. - Scalability. The authors claim that both KG embedding and rule mining have problems with the state-of-the-art hardware but never state which hardware they are using in their experiments. Both GPUs and RAM are getting cheaper and cheaper, and the cloud options are also available. - What is the computational complexity of the rule mining process? How big are the joins? === After rebuttal ==== Thanks the authors for addressing my concerns.". }

• Paper.138_Review.1 hasContent "The paper describes a rule-based approach for detecting equivalent or synonymous properties in a KG. While this approach gives promising results compared to embedding approaches, the experiments with only two datasets DBpedia and Wikidata raise concerns about the applicability and scalability of this approach in different datasets. Since this approach is a data-driven, more experiments are expected with the real-world datasets having the problem, and an in-depth discussion on the computational complexity and hardware requirements would be desirable. Positive: - The rule-based approach for finding synonymous properties is well-presented and easy to follow. - The results in the DBpedia is comparable to the best embedding approach HolE - The rule-based approach offers explanations over embedding approach - The datasets and source code are provided. Negative: - The previous work by the same authors had performed the experiments with Freebase. However, they do not include it in this paper for comparison. What is the rationale behind not providing the experiments with Freebase? Is it not implemented? Or is the result not good? Since this approach is data-driven, we expect to see the experiments in more different datasets. - DBpedia is the only dataset with a real problem and the proposed approach gives a comparable performance, not superior to HolE. - Wikidata doesn’t have synonymous properties and the authors introduced synthetic synonyms into it. It would be more convincing to find the datasets having real problems than creating a “fake” one, especially for a data-driven approach. - While the authors state that their approach is scalable, we do not see any analytical discussion on the scalability. Instead, the evaluation was performed on sampling datasets with reduced sizes to ~11-12M triples. - Scalability. The authors claim that both KG embedding and rule mining have problems with the state-of-the-art hardware but never state which hardware they are using in their experiments. Both GPUs and RAM are getting cheaper and cheaper, and the cloud options are also available. - What is the computational complexity of the rule mining process? How big are the joins? === After rebuttal ==== Thanks the authors for addressing my concerns."".