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Matches in ESWC 2020 for { ?s ?p This paper focuses on the problem of subsumption checking in KBs, with the limitation of having incomplete information in the KB, which makes it impossible to find concept equivalences that an expert would otherwise expect. The general approach is to, instead of checking subsumption on the original concepts, these are expanded, or better, unfolded into more complex but (hopefully) equivalent ones, using natural language statistical techniques. Therefore, instead of checking subsumption/equivalence of the original classes, it is performed on the unfolded versions of them. Even though the methods used for this general approach do not add much in terms of novelty if taken individually, I find the overall concept rather practical, as it has been shown in the use cases presented in the paper. KBs, even those that are well curated, will never instantiate all possible ways of representing a concept, using a large number of classes and predicates. In this way, the proposed approach shows a rather practical way of addressing a common problem in KBs. As the authors show in the results, there is logically still considerable amounts of false positives/negatives, and the concept on-the-fly construction still does not match what the quality that experts would otherwise provide. Although in the e-health industry this can be rather problematic, in the mentioned uses cases (e.g. chatbot) the given results are already positive compared to what is usually delivered in terms of symptom data acquisition. Nevertheless, it is still not clear from these results to what point this would compare to a totally data-driven approach, e.g. based entirely on ML data matching. The usage of KB-driven logical reasoning has of course other advantages, but a full comparison with an ML approach (which actually is being used in several e-health chatbot prototypes nowadays) would be of enormous interest. Having said so, I think this more comprehensive study would probably be out of the scope of what this paper is addressing. As the authors mention, the proposed approach does not need to be limited to healthcare. In this respect, I think the authors missed the opportunity to show a stronger version of this work, showing it in action with other KBs like DBPedia or others. They do mention this possibility in the paper, but I think it would have been important to include it, as it is well known that from different KBs the results can be rather different, and pitfalls of the approach can be learned from those differences. Furthermore, applying the approach in more datasets strengthens the results and its credibility. This also applies to other elements of the evaluation, such as the queries (which were unfortunately not real due to patient protection, which it's understandable), but perhaps on a different domain with other KBs, this could have been tested in more 'real' conditions. ------------------ Thanks to the authors for their answers. With this information I confirm my assessment.". }

• Paper.107_Review.0 hasContent "This paper focuses on the problem of subsumption checking in KBs, with the limitation of having incomplete information in the KB, which makes it impossible to find concept equivalences that an expert would otherwise expect. The general approach is to, instead of checking subsumption on the original concepts, these are expanded, or better, unfolded into more complex but (hopefully) equivalent ones, using natural language statistical techniques. Therefore, instead of checking subsumption/equivalence of the original classes, it is performed on the unfolded versions of them. Even though the methods used for this general approach do not add much in terms of novelty if taken individually, I find the overall concept rather practical, as it has been shown in the use cases presented in the paper. KBs, even those that are well curated, will never instantiate all possible ways of representing a concept, using a large number of classes and predicates. In this way, the proposed approach shows a rather practical way of addressing a common problem in KBs. As the authors show in the results, there is logically still considerable amounts of false positives/negatives, and the concept on-the-fly construction still does not match what the quality that experts would otherwise provide. Although in the e-health industry this can be rather problematic, in the mentioned uses cases (e.g. chatbot) the given results are already positive compared to what is usually delivered in terms of symptom data acquisition. Nevertheless, it is still not clear from these results to what point this would compare to a totally data-driven approach, e.g. based entirely on ML data matching. The usage of KB-driven logical reasoning has of course other advantages, but a full comparison with an ML approach (which actually is being used in several e-health chatbot prototypes nowadays) would be of enormous interest. Having said so, I think this more comprehensive study would probably be out of the scope of what this paper is addressing. As the authors mention, the proposed approach does not need to be limited to healthcare. In this respect, I think the authors missed the opportunity to show a stronger version of this work, showing it in action with other KBs like DBPedia or others. They do mention this possibility in the paper, but I think it would have been important to include it, as it is well known that from different KBs the results can be rather different, and pitfalls of the approach can be learned from those differences. Furthermore, applying the approach in more datasets strengthens the results and its credibility. This also applies to other elements of the evaluation, such as the queries (which were unfortunately not real due to patient protection, which it's understandable), but perhaps on a different domain with other KBs, this could have been tested in more 'real' conditions. ------------------ Thanks to the authors for their answers. With this information I confirm my assessment."".