Geometric Algebra based Embeddings for Staticand Temporal Knowledge Graph Completion

• URL: http://arxiv.org/abs/2202.09464v1
• Date: Fri, 18 Feb 2022 22:52:46 GMT
• Title: Geometric Algebra based Embeddings for Staticand Temporal Knowledge Graph Completion
• Authors: Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, and Jens Lehmann
• Abstract summary: We propose a novel geometric algebra based embedding approach, GeomE, for Knowledge Graph Embeddings (KGEs) GeomE subsumes several state-of-the-art KGE models and is able to model diverse relations patterns. We extend GeomE to TGeomE for temporal KGE, which performs 4th-order tensor factorization of a temporal KG and devises a new linear temporal regularization for time representation learning.
• Score: 13.062516271299831
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent years, Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a Knowledge Graph (KG) into a geometric space and thus have gained increasing attentions. In addition, many recent Knowledge Graphs involve evolving data, e.g., the fact (\textit{Obama}, \textit{PresidentOf}, \textit{USA}) is valid only from 2009 to 2017. This introduces important challenges for knowledge representation learning since such temporal KGs change over time. In this work, we strive to move beyond the complex or hypercomplex space for KGE and propose a novel geometric algebra based embedding approach, GeomE, which uses multivector representations and the geometric product to model entities and relations. GeomE subsumes several state-of-the-art KGE models and is able to model diverse relations patterns. On top of this, we extend GeomE to TGeomE for temporal KGE, which performs 4th-order tensor factorization of a temporal KG and devises a new linear temporal regularization for time representation learning. Moreover, we study the effect of time granularity on the performance of TGeomE models. Experimental results show that our proposed models achieve the state-of-the-art performances on link prediction over four commonly-used static KG datasets and four well-established temporal KG datasets across various metrics.

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