知识图谱嵌入(Knowledge Graph Embedding, KGE)技术旨在将图中的实体和关系映射到低维向量空间中,使得相似的实体和关系在向量空间中接近。ComplEx模型是近年来广受欢迎的知识图谱嵌入方法之一,能够有效捕捉复杂的关系模式。 ComplEx模型的基本原理 1 ComplEx简介 ComplEx模型于2016年提出,主要通过复数空间中的运算来表示知识...
Knowledge graph (KG) embedding models map nodes and edges to fixed-length vectors and obtain the similarity of nodes as the output of a scoring function to predict missing links between nodes. KG embedding methods based on graph convolutional networks (GCNs) have recently gained significant ...
当时已经有大量的研究去学习实体和关系的低维表示来预测缺失边,这些研究可以统称为knowledge graph embedding,如TransE、ComplEx 和ConvE。这些模型已经被公认为具有可大规模扩展、高效的优点。直觉上,这些方法根据观察到已有的知识图谱事实去建模和推断知识图谱中的连接模式。 例如,一些关系是对称的(如婚姻),相对地一些...
Knowledge graph embedding methods aim to learn low-dimensional vector representations of entities and relations in knowledge graphs. The models take input in the format of triples (h, t, r) denoting head entity, tail entity, and relation, respectively, and output their embedding vectors as well ...
为了有效地优化RotatE,我们进一步提出了一个新自我对抗负采样技术,其能更具现有的实体和关系嵌入生成负样本。我们的模型达到了SOTA。此外,RotatE也在Countries数据集上优于其他模型,其是一个专门为组合关系推理建模的基准。据我们所知,RotatE是第一个在所有基准上达到SOTA表现的模型。
Embedding of complex networks in the latent geometry allows for a better understanding of their features. The authors propose a framework for mapping complex networks into high-dimensional hyperbolic space to capture their intrinsic dimensionality, navigability and community structure. ...
【KGE】RotatE Knowledge Graph Embedding by Relational Rotation in Complex Space,程序员大本营,技术文章内容聚合第一站。
Rce-KGQA (Jin et al., 2021) use knowledge graph embedding techniques to capture the implicit relation chains in the KB to overcome the missing implied relations between the topic entities and answers. Feng et al. (2021) pretrain a transformer model before the graph reasoner to enable ...
RotatE——论文《ROTATE: KNOWLEDGE GRAPH EMBEDDING BY RELATIONAL ROTATION IN COMPLEX SPACE-ICLR2019》阅读笔记 2 年前 ElEvEn 孤独患者关注本文提出了一种在复空间表示知识图谱实体和关系的模型TransE,并且能很好的表示三种关系模型(对称/反对称、逆和合成)。同时我们提出了一种新的自对抗负采样方法,能有效训练...
Huang X, Zhang J, Li D, Li P (2019) Knowledge graph embedding based question answering. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. pp. 105–113 Yang B, Yih W-t, He X, Gao J, Deng L (2014) Embedding entities and relations for learning...