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            <namePart type="given">Yusuke</namePart>
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    <abstract>Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddings in various tasks (e.g., link prediction), geometric understanding of such embeddings (i.e., arrangement of entity and relation vectors in vector space) is unexplored – we fill this gap in the paper. We initiate a study to analyze the geometry of KG embeddings and correlate it with task performance and other hyperparameters. To the best of our knowledge, this is the first study of its kind. Through extensive experiments on real-world datasets, we discover several insights. For example, we find that there are sharp differences between the geometry of embeddings learnt by different classes of KG embeddings methods. We hope that this initial study will inspire other follow-up research on this important but unexplored problem.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1012</identifier>
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%0 Conference Proceedings
%T Towards Understanding the Geometry of Knowledge Graph Embeddings
%A Sharma, Aditya
%A Talukdar, Partha
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%A Chandrahas
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F chandrahas-etal-2018-towards
%X Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddings in various tasks (e.g., link prediction), geometric understanding of such embeddings (i.e., arrangement of entity and relation vectors in vector space) is unexplored – we fill this gap in the paper. We initiate a study to analyze the geometry of KG embeddings and correlate it with task performance and other hyperparameters. To the best of our knowledge, this is the first study of its kind. Through extensive experiments on real-world datasets, we discover several insights. For example, we find that there are sharp differences between the geometry of embeddings learnt by different classes of KG embeddings methods. We hope that this initial study will inspire other follow-up research on this important but unexplored problem.
%R 10.18653/v1/P18-1012
%U https://aclanthology.org/P18-1012/
%U https://doi.org/10.18653/v1/P18-1012
%P 122-131

Markdown (Informal)

[Towards Understanding the Geometry of Knowledge Graph Embeddings](https://aclanthology.org/P18-1012/) (Chandrahas et al., ACL 2018)

• Towards Understanding the Geometry of Knowledge Graph Embeddings (Chandrahas et al., ACL 2018)

ACL

• Chandrahas, Aditya Sharma, and Partha Talukdar. 2018. Towards Understanding the Geometry of Knowledge Graph Embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 122–131, Melbourne, Australia. Association for Computational Linguistics.