Data Portal @ linkeddatafragments.org

Matches in ESWC 2020 for { ?s ?p In this paper, the author exploited the importance of the geometrical space, hyperbolic space, for the Knowledge Base Completion. They showed that the lagging performance of translational models compared to the bilinear ones is not an intrinsic characteristic of them but a restriction that can be lifted in the hyperbolic space. Experimental results validated that the right choice of geometrical space is a critical decision for KBC. Positive points: 1) The motivation is clear and the related work is sufficient for their research topic. The authors mainly focus shallow embedding. They discuss the shortcoming of current techniques, e.g., RESCAL and TransE and proposed their model based on the benefit of hyperbolic space. The motivation is clear. 2) The paper is well written and happy to follow. The background of some concepts i.e., Hyperbolic Space, are well introduced in the paper. Some Lemmas are also shown to strength their model. And the problem is clear and experimental results do answer their research question. 3) The code and its software document is available for reproducibility. Negative points: 1) The time/space complexity is not provided, which is very important for large-scale dataset. Current bilinear model, like RESCAL, actually scale very well for large dataset. The proposed HyperKG involve some Riemannian gradient. It is unclear how efficiency of the HyperKG. Some complexity will convince the readers. 2) The dataset of WD and WD++ is quite small. Not sure if such small datasets have statistical significance. Some large dataset, e.g., dataset in recommender system area, can be introduced in the experiments. In summary, the overall idea of introducing geometrical spaces for Knowledge Base Completion. The code and its software document is available for reproducibility. I vote for acceptance at this time. =============================== After Rebuttal =============== The authors answer my question about the time and space complexity of HyperKG, which is important. I suggest the authors also add one subsection to discuss time and space complexity in the original paper. The dataset is still an issue by using randomization tests. The auhtors point out that "recent studies have notated many KB relations have very few facts". I keep my original score since the authors answer my questions.". }

• Paper.52_Review.0 hasContent "In this paper, the author exploited the importance of the geometrical space, hyperbolic space, for the Knowledge Base Completion. They showed that the lagging performance of translational models compared to the bilinear ones is not an intrinsic characteristic of them but a restriction that can be lifted in the hyperbolic space. Experimental results validated that the right choice of geometrical space is a critical decision for KBC. Positive points: 1) The motivation is clear and the related work is sufficient for their research topic. The authors mainly focus shallow embedding. They discuss the shortcoming of current techniques, e.g., RESCAL and TransE and proposed their model based on the benefit of hyperbolic space. The motivation is clear. 2) The paper is well written and happy to follow. The background of some concepts i.e., Hyperbolic Space, are well introduced in the paper. Some Lemmas are also shown to strength their model. And the problem is clear and experimental results do answer their research question. 3) The code and its software document is available for reproducibility. Negative points: 1) The time/space complexity is not provided, which is very important for large-scale dataset. Current bilinear model, like RESCAL, actually scale very well for large dataset. The proposed HyperKG involve some Riemannian gradient. It is unclear how efficiency of the HyperKG. Some complexity will convince the readers. 2) The dataset of WD and WD++ is quite small. Not sure if such small datasets have statistical significance. Some large dataset, e.g., dataset in recommender system area, can be introduced in the experiments. In summary, the overall idea of introducing geometrical spaces for Knowledge Base Completion. The code and its software document is available for reproducibility. I vote for acceptance at this time. =============================== After Rebuttal =============== The authors answer my question about the time and space complexity of HyperKG, which is important. I suggest the authors also add one subsection to discuss time and space complexity in the original paper. The dataset is still an issue by using randomization tests. The auhtors point out that "recent studies have notated many KB relations have very few facts". I keep my original score since the authors answer my questions."".