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• Author.138.2 isHeldBy Stephan_Mennicke.
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• Author.138.3 label "Philipp Ehler, 3rd Author for Paper 138".
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• Author.138.3 isHeldBy Philipp_Ehler.
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• Author.138.4 label "Wolf-Tilo Balke, 4th Author for Paper 138".
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• Stephan_Mennicke type Person.
• Stephan_Mennicke name "Stephan Mennicke".
• Stephan_Mennicke label "Stephan Mennicke".
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• Philipp_Ehler type Person.
• Philipp_Ehler name "Philipp Ehler".
• Philipp_Ehler label "Philipp Ehler".
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• Wolf-Tilo_Balke type Person.
• Wolf-Tilo_Balke name "Wolf-Tilo Balke".
• Wolf-Tilo_Balke label "Wolf-Tilo Balke".
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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.
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• Paper.138_Review.0 reviews Paper.138.
• Paper.138_Review.0 issuedAt easychair.org.
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• 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."".
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• Paper.138_Review.1 issued "2001-02-14T14:56:00.000Z".
• Paper.138_Review.1 creator Paper.138_Review.1_Reviewer.
• Paper.138_Review.1 hasRating ReviewRating.1.
• Paper.138_Review.1 hasReviewerConfidence ReviewerConfidence.4.
• Paper.138_Review.1 reviews Paper.138.
• Paper.138_Review.1 issuedAt easychair.org.
• Paper.138_Review.1 issuedFor Conference.
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• 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."".
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• Paper.138_Review.2 issued "2001-02-09T22:43:00.000Z".
• Paper.138_Review.2 creator Paper.138_Review.2_Reviewer.
• Paper.138_Review.2 hasRating ReviewRating.2.
• Paper.138_Review.2 hasReviewerConfidence ReviewerConfidence.4.
• Paper.138_Review.2 reviews Paper.138.
• Paper.138_Review.2 issuedAt easychair.org.
• Paper.138_Review.2 issuedFor Conference.
• Paper.138_Review.2 releasedBy Conference.
• Paper.138_Review.2 hasContent "In this paper, the authors aim at detecting what they call synonymous properties in large knowledge graphs. They consider that two properties are synonymous if they share the same (formal) definition, which means in their case that the two properties are defined with two conjunctions pf properties that match at least partially. This paper extends previous work dedicated to learning property definitions from large knowledge graphs using techniques like rules, frequent item sets or knowledge graph embeddings. The paper states very clearly the objectives of this work and the related work that were used to mine relations as well as contrastive approaches like ontology matching. A very nice and relevant example provides very clear illustrations of the phenomenon to be captured, of the formal definitions that are mined as well as the support of each rule (given the triples that contain the relation). The authors propose the RuleAlign approach as a way to align relations, here in the same knowledge graph. The rule mining technique relies on mining property definitions using rule induction on definitions that are turned into Horn clauses. The evaluation of the confidence and support of each rule is a means to select the learned rules among all possible rules. The relations are matched using these definitions. Two relations are said to be synonymous when they refer to (almost) the same conjunction of relations. The evaluation of the approach compares 6 embedding implementations with rules RuleAlign and frequent item set algorithms as a baseline. The dataset to be mined is DBPedia. Several baselines are manually evaluated, with a precision to k with k going up to 500. Results show that RuleAlign outperforms other implementations, with results very close to the one obtained with the best embedding solution (using HolE). The advantage of RuleAlign is that synonymy of relations is "explained" thanks to their definition. The paper as well as the results are of high quality. The paper is clear, well structured and well written. The state of the art is relevant. It is a nice contribution to knowledge graph exploitation. At the end of section 4, it would be nice to give a synthetic view of your approach, in the form of a kind of algorithm, of the process carried out by RuleAlign. _____ after the rebuttal phase _____ I thank the authors for their answers to the comments and requests of the reviewers. I hope that the final version of the paper will integrate the suggested changes."".
• Paper.142 type SubmissionsPaper.
• Paper.142 label "Ontological Foundations for Vulnerability Analysis of Security Policies".
• Paper.142 title "Ontological Foundations for Vulnerability Analysis of Security Policies".
• Paper.142 issued "2001-12-04T13:27:00.000Z".
• Paper.142 authorList b0_g362.
• Paper.142 submission Paper.142.
• Paper.142 track Track.Ontologies%20and%20Reasoning.
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• Author.142.1 type RoleDuringEvent.
• Author.142.1 label "Karolina Bataityte, 1st Author for Paper 142".
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• Author.142.1 isHeldBy Karolina_Bataityte.
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• Author.142.2 label "Vassil Vassilev, 2nd Author for Paper 142".
• Author.142.2 withRole PublishingRole.
• Author.142.2 isHeldBy Vassil_Vassilev.
• b0_g364 first Author.142.3.
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• Author.142.3 type RoleDuringEvent.
• Author.142.3 label "Olivia Gill, 3rd Author for Paper 142".
• Author.142.3 withRole PublishingRole.
• Author.142.3 isHeldBy Olivia_Gill.
• Karolina_Bataityte type Person.
• Karolina_Bataityte name "Karolina Bataityte".
• Karolina_Bataityte label "Karolina Bataityte".
• Karolina_Bataityte holdsRole Author.142.1.
• Vassil_Vassilev type Person.
• Vassil_Vassilev name "Vassil Vassilev".
• Vassil_Vassilev label "Vassil Vassilev".
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