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...
Recent research has proved that multi-source information of entities is conducive to more accurate knowledge embedding tasks. In this paper, we propose a model based on an attention mechanism and integrating the type information of entities, named KTAT. This model is based on the graph a...
由于知识图谱受限于构建技术等因素,它总是不完整的,因此对于知识图谱的一个基本难题是如何去预测缺失的关系边。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...
论文笔记:ROTATE: KNOWLEDGE GRAPH EMBEDDING BY RELATIONAL ROTATION IN COMPLEX SPACE-ICLR2019 WhisperEcho 自然语言处理、对话系统、知识图谱8 人赞同了该文章 目录 收起 ABSTRACT 1 INTRODUCTION 2 RELATED WORK 3 ROTATE:RELATIONAL ROTATION IN COMPLEX VECTOR SPACE 3.1 MODELING AND INFERRING RELATION PATTERNS...
结果,这将推动具有对称关系的实体在嵌入空间中彼此靠近。RotatE解决了这个问题,并且能够建模和推断对称模式。任意向量r满足ri=±1能被用来表示一个在RotatE中的对称关系,因此,可以区分具有对称关系的实体。不同的对称关系也可以用不同的嵌入向量来表示。图1提供了仅具有一维嵌入的TransE和RotatE的图示,并显示了RotatE...
Graph embedding, which aims to learn low-dimensional node representations to preserve original graph structures, has attracted extensive research interests... S Feng,L Chen,Kaiqi ZhaoWei WeiXuemeng SongShuo ShangPanos KalnisLing Shao - 《IEEE Transactions on Knowledge & Data Engineering》 被引量: 0...
In [75], a systematic review of knowledge graph embedding was provided, including the state-of-the-art and the latest trends. In [76], authors reviewed the basic concept of knowledge computing and the methods for computing over knowledge graphs. The computing methods are dissected into three ...
HRotatE: Hybrid Relational Rotation Embedding for Knowledge Graph link in a Knowledge Graph, but the most prominent approaches are based on tensor factorization and Knowledge-Graph embeddings, such as RotatE and SimplE. ... A Shah,B Molokwu,Z Kobti - International Joint Conference on Neural Netw...
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的优化采用了一种新的自对抗负采样(self-adversarial negative sampling)技术,通过当前实体和关系嵌入生成负采样。这个技术通用性好,可用到许多现有的知识图谱嵌入模型上。 3、相关工作 知识图中链接预测的关键是依据观测到的事实推理连接模式,例如,关系模型。依据现有的文献,有三种关系模型是非常重要的并且在知识图谱...