Matches in ESWC 2020 for { ?s ?p This paper describes a method for taxonomy induction on knowledge graphs. The method is based on working with the knowledge graph as if it contained a collection of documents (subjects) which have annotations- tags- (property, object), so they can adapt and apply tag induction methods. The authors justify the need for extracting taxonomies from the knowledge graphs and summarize the state of the art by analyzing methods for taxonomy induction and tag induction. The paper contains a description of the method and its application to three datasets: Life, DBPedia and WordNet. The description of the experiment is not clear to me. It seems that in the three cases there are properties that define the taxonomy in the resource and that these properties are used as tags for the induction of the taxonomy, which could bias the results. The data used and the experiments have not been shared or their reproducibility facilitated. The results are compared with state of the art methods. The authors have used their own implementation of methods like Heymann and Garcia-Molina / Schmitz, which is a valuable effort but it is not clear if they are reproducing correctly the methods. This is also a limitation regarding the scalability analysis and the fact that some of these methods do not finish for some datasets. The text mentions the comparison with the tag induction methods, but Table 1 also includes the results of class taxonomy induction methods like Volker and Niepert, which is the one obtaining the best results. There is no discussion about these other methods, which are only applied to DBPedia, would they be applicable to the other ones? The results in Table 1 show that the method works better in some datasets but not in all, why?". }
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- Paper.193_Review.0 hasContent "This paper describes a method for taxonomy induction on knowledge graphs. The method is based on working with the knowledge graph as if it contained a collection of documents (subjects) which have annotations- tags- (property, object), so they can adapt and apply tag induction methods. The authors justify the need for extracting taxonomies from the knowledge graphs and summarize the state of the art by analyzing methods for taxonomy induction and tag induction. The paper contains a description of the method and its application to three datasets: Life, DBPedia and WordNet. The description of the experiment is not clear to me. It seems that in the three cases there are properties that define the taxonomy in the resource and that these properties are used as tags for the induction of the taxonomy, which could bias the results. The data used and the experiments have not been shared or their reproducibility facilitated. The results are compared with state of the art methods. The authors have used their own implementation of methods like Heymann and Garcia-Molina / Schmitz, which is a valuable effort but it is not clear if they are reproducing correctly the methods. This is also a limitation regarding the scalability analysis and the fact that some of these methods do not finish for some datasets. The text mentions the comparison with the tag induction methods, but Table 1 also includes the results of class taxonomy induction methods like Volker and Niepert, which is the one obtaining the best results. There is no discussion about these other methods, which are only applied to DBPedia, would they be applicable to the other ones? The results in Table 1 show that the method works better in some datasets but not in all, why?"".