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Matches in ESWC 2020 for { ?s ?p ?o. }

• Giorgos_Stoilos holdsRole Author.107.1.
• Damir_Juric type Person.
• Damir_Juric name "Damir Juric".
• Damir_Juric label "Damir Juric".
• Damir_Juric holdsRole Author.107.2.
• Szymon_Wartak type Person.
• Szymon_Wartak name "Szymon Wartak".
• Szymon_Wartak label "Szymon Wartak".
• Szymon_Wartak holdsRole Author.107.3.
• Claudia_Schulz type Person.
• Claudia_Schulz name "Claudia Schulz".
• Claudia_Schulz label "Claudia Schulz".
• Claudia_Schulz holdsRole Author.107.4.
• Mohammad_Khodadadi type Person.
• Mohammad_Khodadadi name "Mohammad Khodadadi".
• Mohammad_Khodadadi label "Mohammad Khodadadi".
• Mohammad_Khodadadi holdsRole Author.107.5.
• Jean-Paul_Calbimonte type Person.
• Jean-Paul_Calbimonte name "Jean-Paul Calbimonte".
• Jean-Paul_Calbimonte label "Jean-Paul Calbimonte".
• Jean-Paul_Calbimonte holdsRole Paper.107_Review.0_Reviewer.
• Jean-Paul_Calbimonte holdsRole Paper.247_Review.0_Reviewer.
• Jean-Paul_Calbimonte mbox mailto:jpcik@outlook.com.
• Paper.107_Review.0_Reviewer type RoleDuringEvent.
• Paper.107_Review.0_Reviewer label "Jean-Paul Calbimonte, Reviewer for Paper 107".
• Paper.107_Review.0_Reviewer withRole ReviewerRole.
• Paper.107_Review.0_Reviewer withRole NonAnonymousReviewerRole.
• Paper.107_Review.0_Reviewer isHeldBy Jean-Paul_Calbimonte.
• Paper.247_Review.0_Reviewer type RoleDuringEvent.
• Paper.247_Review.0_Reviewer label "Jean-Paul Calbimonte, Reviewer for Paper 247".
• Paper.247_Review.0_Reviewer withRole ReviewerRole.
• Paper.247_Review.0_Reviewer withRole NonAnonymousReviewerRole.
• Paper.247_Review.0_Reviewer isHeldBy Jean-Paul_Calbimonte.
• Paper.107_Review.0 type ReviewVersion.
• Paper.107_Review.0 issued "2001-01-28T11:56:00.000Z".
• Paper.107_Review.0 creator Paper.107_Review.0_Reviewer.
• Paper.107_Review.0 hasRating ReviewRating.1.
• Paper.107_Review.0 hasReviewerConfidence ReviewerConfidence.3.
• Paper.107_Review.0 reviews Paper.107.
• Paper.107_Review.0 issuedAt easychair.org.
• Paper.107_Review.0 issuedFor Conference.
• Paper.107_Review.0 releasedBy Conference.
• 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."".
• Paper.107_Review.1_Reviewer type RoleDuringEvent.
• Paper.107_Review.1_Reviewer label "Anonymous Reviewer for Paper 107".
• Paper.107_Review.1_Reviewer withRole ReviewerRole.
• Paper.107_Review.1_Reviewer withRole AnonymousReviewerRole.
• Paper.107_Review.1 type ReviewVersion.
• Paper.107_Review.1 issued "2001-01-27T08:31:00.000Z".
• Paper.107_Review.1 creator Paper.107_Review.1_Reviewer.
• Paper.107_Review.1 hasRating ReviewRating.2.
• Paper.107_Review.1 hasReviewerConfidence ReviewerConfidence.4.
• Paper.107_Review.1 reviews Paper.107.
• Paper.107_Review.1 issuedAt easychair.org.
• Paper.107_Review.1 issuedFor Conference.
• Paper.107_Review.1 releasedBy Conference.
• 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."".
• Paper.109 type SubmissionsPaper.
• Paper.109 label "Is it a Fruit, an Apple or a Granny Smith? Predicting the Basic Level in a Concept Hierarchy".
• Paper.109 title "Is it a Fruit, an Apple or a Granny Smith? Predicting the Basic Level in a Concept Hierarchy".
• Paper.109 issued "2001-12-04T09:19:00.000Z".
• Paper.109 authorList b0_g281.
• Paper.109 submission Paper.109.
• Paper.109 track Track.Social%20and%20Human.
• b0_g281 first Author.109.1.
• b0_g281 rest b0_g282.
• Author.109.1 type RoleDuringEvent.
• Author.109.1 label "Laura Hollink, 1st Author for Paper 109".
• Author.109.1 withRole PublishingRole.
• Author.109.1 isHeldBy Laura_Hollink.
• b0_g282 first Author.109.2.
• b0_g282 rest b0_g283.
• Author.109.2 type RoleDuringEvent.
• Author.109.2 label "Aysenur Bilgin, 2nd Author for Paper 109".
• Author.109.2 withRole PublishingRole.
• Author.109.2 isHeldBy Aysenur_Bilgin.
• b0_g283 first Author.109.3.
• b0_g283 rest nil.
• Author.109.3 type RoleDuringEvent.
• Author.109.3 label "Jacco van Ossenbruggen, 3rd Author for Paper 109".
• Author.109.3 withRole PublishingRole.
• Author.109.3 isHeldBy Jacco_van_Ossenbruggen.
• Laura_Hollink type Person.
• Laura_Hollink name "Laura Hollink".
• Laura_Hollink label "Laura Hollink".
• Laura_Hollink holdsRole Author.109.1.
• Aysenur_Bilgin type Person.
• Aysenur_Bilgin name "Aysenur Bilgin".
• Aysenur_Bilgin label "Aysenur Bilgin".
• Aysenur_Bilgin holdsRole Author.109.2.
• Jacco_van_Ossenbruggen type Person.
• Jacco_van_Ossenbruggen name "Jacco van Ossenbruggen".
• Jacco_van_Ossenbruggen label "Jacco van Ossenbruggen".
• Jacco_van_Ossenbruggen holdsRole Author.109.3.
• Author.110.1 type RoleDuringEvent.
• Author.110.1 label "Thomas Pellissier Tanon, 1st Author for Paper 110".
• Author.110.1 withRole PublishingRole.
• Author.110.1 isHeldBy Thomas_Pellissier_Tanon.
• b0_g285 first Author.110.2.
• b0_g285 rest b0_g286.

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