Matches in ESWC 2020 for { ?s ?p The paper investigates the impact of the mathematical nature of the embedding space on the performance of lower dimensional embedding approachs to link prediction. Further, they investigate the possibility to represent certain types of logical rules into hyperbolic space. Knowledge-base embedding is a very active area of research with a high potential impact on semantic web technologies. The paper is therefore potentially relevant for ESWC. Concerning the two contributions claimed by the authors (see above), I however only consider the second one (rule embedding) to be valid. The first contribution: the investigation of the impact of the nature of the embedding space on the performance imho has been superseded by recent work by Ruffinelli et al* that shows that the differences between embedding models proposed so far was mainly due to differences in the training and evaluation protocol and that the older translational approaches do not perform worse than more recent ones if the same training and evaluation protocol is applied. Further, the results reported in this paper are systematically worse than the ones achieved by Ruffinelli et al even with the most outdated models. The ability to include rule-like structures into the embedding space is indeed an interesting result. In its present form, however, the paper only discusses this in a very brief and incomplete way. I therefore vote for rejection *Daniel Ruffinelli, Samuel Broscheit, Rainer Gemulla: You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings. Proceedings of the International Conference on Learning Representations ICLR 2020.". }
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- Paper.52_Review.2 hasContent "The paper investigates the impact of the mathematical nature of the embedding space on the performance of lower dimensional embedding approachs to link prediction. Further, they investigate the possibility to represent certain types of logical rules into hyperbolic space. Knowledge-base embedding is a very active area of research with a high potential impact on semantic web technologies. The paper is therefore potentially relevant for ESWC. Concerning the two contributions claimed by the authors (see above), I however only consider the second one (rule embedding) to be valid. The first contribution: the investigation of the impact of the nature of the embedding space on the performance imho has been superseded by recent work by Ruffinelli et al* that shows that the differences between embedding models proposed so far was mainly due to differences in the training and evaluation protocol and that the older translational approaches do not perform worse than more recent ones if the same training and evaluation protocol is applied. Further, the results reported in this paper are systematically worse than the ones achieved by Ruffinelli et al even with the most outdated models. The ability to include rule-like structures into the embedding space is indeed an interesting result. In its present form, however, the paper only discusses this in a very brief and incomplete way. I therefore vote for rejection *Daniel Ruffinelli, Samuel Broscheit, Rainer Gemulla: You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings. Proceedings of the International Conference on Learning Representations ICLR 2020."".