Web: http://arxiv.org/abs/2201.09919

Jan. 26, 2022, 2:10 a.m. | Bo Xiong, Nico Potyka, Trung-Kien Tran, Mojtaba Nayyeri, Steffen Staab

cs.LG updates on arXiv.org arxiv.org

Recently, various methods for representation learning on Knowledge Bases
(KBs) have been developed. However, these approaches either only focus on
learning the embeddings of the data-level knowledge (ABox) or exhibit inherent
limitations when dealing with the concept-level knowledge (TBox), e.g., not
properly modelling the structure of the logical knowledge. We present BoxEL, a
geometric KB embedding approach that allows for better capturing logical
structure expressed in the theories of Description Logic EL++. BoxEL models
concepts in a KB as axis-parallel …

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