Matches in ESWC 2020 for { ?s ?p 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.". }
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- 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."".