论文标题
知识图嵌入几何代数
Knowledge Graph Embeddings in Geometric Algebras
论文作者
论文摘要
知识图（kg）嵌入旨在将kg中的实体和关系嵌入到低维的潜在空间中。现有的kg嵌入方法通过利用现实价值，复杂值或超复合值（Quaternionor octonion）表示，将kg中的实体模型且关系添加到几何代数中。在这项工作中，我们介绍了一种新型的基于几何代数的KG嵌入框架，Geome，该框架将多活活的代表和几何产品uti大小为模型和关系。我们的Framework涵盖了几种最先进的KG嵌入方法，并且具有对各种关键关系模式进行建模的能力，包括（反）对称性，反式和倒置组成，具有较高自由度的丰富表现力以及良好的通用能力。多个基准知识图上的实验结果表明，该方法的方法优于链接预测的现有最新模型。
Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space. Existing KG embedding approaches model entities andrelations in a KG by utilizing real-valued , complex-valued, or hypercomplex-valued (Quaternionor Octonion) representations, all of which are subsumed into a geometric algebra. In this work,we introduce a novel geometric algebra-based KG embedding framework, GeomE, which uti-lizes multivector representations and the geometric product to model entities and relations. Ourframework subsumes several state-of-the-art KG embedding approaches and is advantageouswith its ability of modeling various key relation patterns, including (anti-)symmetry, inversionand composition, rich expressiveness with higher degree of freedom as well as good general-ization capacity. Experimental results on multiple benchmark knowledge graphs show that theproposed approach outperforms existing state-of-the-art models for link prediction.
