论文标题
基于几何代数的嵌入静态和时间知识图完成
Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion
论文作者
论文摘要
近年来，知识图嵌入（KGES）通过将知识图（KG）的实体和关系映射到几何空间中，在链接预测任务上表现出了有希望的性能，因此获得了越来越注意的关注。此外，许多最新的知识图涉及不断发展的数据，例如，事实（\ textit {obama}，\ textit {presencyof}，\ textit {use}）仅在2009年至2017年才有效。这引入了自临时KG自随意时间随时间变化以来知识代表性学习的重要挑战。在这项工作中，我们努力超越KGE的复杂或超复合空间，并提出了一种新型的基于几何代数的嵌入方法Geome，该方法使用多生动器表示和几何产品来模型实体和关系。 Geome涵盖了几种最先进的KGE模型，并能够建模各种关系模式。最重要的是，我们将Geome扩展到TgeOME的时间KGE，该临时KGE进行了时间KG的第四阶张量分解，并设计了新的线性时间正则化以进行时间表示学习。此外，我们研究了时间粒度对TGEOME模型性能的影响。实验结果表明，我们提出的模型在四个常用的静态KG数据集和四个跨各种指标的四个良好的时间KG数据集上实现了最先进的性能。
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.
