Data Portal @ linkeddatafragments.org

Matches in ESWC 2020 for { ?s ?p ----------- Strong Points ----------- -Novel approach. -Theoretic proofs and counterexamples are provided. -Strong evaluation. -Implementation and data available online ----------- Weak Points ----------- -Difficult to find any Summary: The paper addresses the important task of link prediction on knowledge bases(KB) i.e. automatic knowledge base completion (KBC). The presented approach adopts hyperbolic geometry to exploit scale-free structures of KBs in order to learn KB embeddings. The authors focus on a specific type of KB embeddings models, i.e., translational models aiming to model vector translation between entities. The proposed model is also shown to be effective in capturing the logical consistency in the facts induced by the KB embeddings. The paper demonstrates how the performance gap between translational and bilinear model families can be closed. The paper provides counterexamples showing the Kazemi and Pool restrictions do not apply to translational models ie TransE when fact validity is based on implausibility scores below a non-zero threshold. Introduction and Related Work: The introduction as the whole paper is well-written and introduces the problem and its specifics for KBs and how they can be exploited through hyperbolic geometry. The authors did a very good job of narrowing down the problem and scope of the paper while putting in the context of the existing body of work. Preliminaries and Proposed Algorithm: The introduced notation and preliminaries are well-explained. Although I am not an expert on hyperbolic geometry and spaces, I was relatively easy to grasp the idea behind it due to the efforts of the authors to provide a clear and easy to follow the narrative. Experiments and Evaluation: A sound evaluation aimed to highlight the specifics of the proposed model. The evaluation addresses the structural properties of the datasets as well as the novel regularisation scheme introduced due to the usage of the Pioncaré-ball model. Critical Appreciation: Overall, I think this paper presents a sound and dense and valuable contribution to the ESWC community and has to be accepted. The paper addresses limitations in a set of KB models i.e. translational and provides strong empirical evidence for that the performance of the TransE model family is not an intrinsic model property but a shortcoming that can be eliminated by the right choice of the geometric space. Moreover, the paper settles an existing disagreement in the recent body of work on KB embeddings as it provides counterexamples showing that Kazemi and Poole restrictions do not apply to TransE models. The authors also provide a theoretical proof that the relation regions captured by the proposed HyperKG are convex and thus can effectively represent QC rules and consequently are reasoning based on HyperKG embeddings would be logically consistent and deductively closed with respect to ontological rules. Minor comments: Page 7: … In our experiments, we noticed a tendency of the “word” vectors to … Page 8: … have shown that the FTransE … =============================== After Rebuttal =============== I keep my original score.". }

• Paper.52_Review.1 hasContent "----------- Strong Points ----------- -Novel approach. -Theoretic proofs and counterexamples are provided. -Strong evaluation. -Implementation and data available online ----------- Weak Points ----------- -Difficult to find any Summary: The paper addresses the important task of link prediction on knowledge bases(KB) i.e. automatic knowledge base completion (KBC). The presented approach adopts hyperbolic geometry to exploit scale-free structures of KBs in order to learn KB embeddings. The authors focus on a specific type of KB embeddings models, i.e., translational models aiming to model vector translation between entities. The proposed model is also shown to be effective in capturing the logical consistency in the facts induced by the KB embeddings. The paper demonstrates how the performance gap between translational and bilinear model families can be closed. The paper provides counterexamples showing the Kazemi and Pool restrictions do not apply to translational models ie TransE when fact validity is based on implausibility scores below a non-zero threshold. Introduction and Related Work: The introduction as the whole paper is well-written and introduces the problem and its specifics for KBs and how they can be exploited through hyperbolic geometry. The authors did a very good job of narrowing down the problem and scope of the paper while putting in the context of the existing body of work. Preliminaries and Proposed Algorithm: The introduced notation and preliminaries are well-explained. Although I am not an expert on hyperbolic geometry and spaces, I was relatively easy to grasp the idea behind it due to the efforts of the authors to provide a clear and easy to follow the narrative. Experiments and Evaluation: A sound evaluation aimed to highlight the specifics of the proposed model. The evaluation addresses the structural properties of the datasets as well as the novel regularisation scheme introduced due to the usage of the Pioncaré-ball model. Critical Appreciation: Overall, I think this paper presents a sound and dense and valuable contribution to the ESWC community and has to be accepted. The paper addresses limitations in a set of KB models i.e. translational and provides strong empirical evidence for that the performance of the TransE model family is not an intrinsic model property but a shortcoming that can be eliminated by the right choice of the geometric space. Moreover, the paper settles an existing disagreement in the recent body of work on KB embeddings as it provides counterexamples showing that Kazemi and Poole restrictions do not apply to TransE models. The authors also provide a theoretical proof that the relation regions captured by the proposed HyperKG are convex and thus can effectively represent QC rules and consequently are reasoning based on HyperKG embeddings would be logically consistent and deductively closed with respect to ontological rules. Minor comments: Page 7: … In our experiments, we noticed a tendency of the “word” vectors to … Page 8: … have shown that the FTransE … =============================== After Rebuttal =============== I keep my original score."".