Matches in ESWC 2020 for { ?s ?p Symbolic reasoning methods cannot produce any derivation if the underlying database is incomplete. This paper addresses this limitation with a method that creates extracts new knowledge on-the-fly so that reasoning can be carried on anyway. The reasoning problem that is being investigated is subsumption checking under Description Logic. Whenever needed knowledge is missing during this process, this paper proposes a method tgat attempts at extracting it from the labels of concepts. The paper describes the methodology used to extract knowledge from labels (using traversals on the dependency parsing tree) and several heuristics to integrate such knowledge into the reasoning pipeline. I liked this idea, and the paper is clear and well-written. The various contributions are motivated with good examples and the evaluation (in the biomedical domain) is convincing. In my opinion, the paper fits the scope of this venue and should be accepted. I have nevertheless some questions/comments for the rebuttal. - I understand the idea of extracting knowledge from labels, but it would be even nicer if this knowledge was extracted from larger textual corpora. I have some doubts about the quality of dependency parsing on such small textual snippets. Did you perform any systematic experiment to evaluate the quality of this extraction? - You mention that some doctors have evaluated the results of your system. However, you do not mention how many they are (I would expect at least three people) nor report any statistic about their agreement. Can you clarify on this issue? - The statements "Large KBs like DBPedia, YAGO, and Wikidata are usually stored in a distributed manner and are accessible only via SPARQL end-points. Hence the use of existing in-memory reasoning systems is not possible" and "triple-store of graph DBs do not reason over existential quantifiers" are not true. The knowledge bases that you cite can be easily stored in a single machine and both RDFox and VLog support reasoning using existentially quantified rules. I would use different motivations to motivate your contribution in section 5. --- I read the rebuttal and I confirm my score. However, I do not agree with the response that existentially quantified reasoning is not possible because the systems that are used in production do not support it. I still do not see any reason why reasoners like RDFox/VLog cannot be used. Anyway, revising that statement will probably fix this issue.". }
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- Paper.107_Review.1 hasContent "Symbolic reasoning methods cannot produce any derivation if the underlying database is incomplete. This paper addresses this limitation with a method that creates extracts new knowledge on-the-fly so that reasoning can be carried on anyway. The reasoning problem that is being investigated is subsumption checking under Description Logic. Whenever needed knowledge is missing during this process, this paper proposes a method tgat attempts at extracting it from the labels of concepts. The paper describes the methodology used to extract knowledge from labels (using traversals on the dependency parsing tree) and several heuristics to integrate such knowledge into the reasoning pipeline. I liked this idea, and the paper is clear and well-written. The various contributions are motivated with good examples and the evaluation (in the biomedical domain) is convincing. In my opinion, the paper fits the scope of this venue and should be accepted. I have nevertheless some questions/comments for the rebuttal. - I understand the idea of extracting knowledge from labels, but it would be even nicer if this knowledge was extracted from larger textual corpora. I have some doubts about the quality of dependency parsing on such small textual snippets. Did you perform any systematic experiment to evaluate the quality of this extraction? - You mention that some doctors have evaluated the results of your system. However, you do not mention how many they are (I would expect at least three people) nor report any statistic about their agreement. Can you clarify on this issue? - The statements "Large KBs like DBPedia, YAGO, and Wikidata are usually stored in a distributed manner and are accessible only via SPARQL end-points. Hence the use of existing in-memory reasoning systems is not possible" and "triple-store of graph DBs do not reason over existential quantifiers" are not true. The knowledge bases that you cite can be easily stored in a single machine and both RDFox and VLog support reasoning using existentially quantified rules. I would use different motivations to motivate your contribution in section 5. --- I read the rebuttal and I confirm my score. However, I do not agree with the response that existentially quantified reasoning is not possible because the systems that are used in production do not support it. I still do not see any reason why reasoners like RDFox/VLog cannot be used. Anyway, revising that statement will probably fix this issue."".