Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction ElEvEn 孤独患者2 人赞同了该文章 目录 收起 1、摘要 2、简介 3、超关系事实 4、HINGE 从三元组中学习 从键值对中学习 合并特征向量进行预测 5、HINGE和其实现的目标 6、模型训练 1、摘要 知识图(KG)嵌入是预测KG中缺失链...
Knowledge Graph Embedding for Link Prediction: A Comparative Analysis 作者:Andrea Rossi、Donatella Firmani、Antonio Matinata、Paolo Merialdo、Denilson Barbosa 论文链接:https://arxiv.org/pdf/2002.00819.pdf 摘要:知识图谱(Knowledge graph, KGs)在工业和学术领域有很多应用,这反过来又推动了从各种来源大规模提...
(3)Graphlets and path-based methods 最后,正如我们对节点级特性的讨论一样,在图上定义特性的一个有效而强大的策略是简单地计算不同小子图结构(在此上下文中通常称为graphlets)的出现次数。在形式上,graphlet内核涉及枚举特定大小的所有可能的图形结构,并计算它们在整个图形中出现的次数。这种方法的挑战在于,尽管已...
ManifoldE—论文《From One Point to A Manifold:Knowledge Graph Embedding For Precise Link Prediction》阅读笔记 ElEvEn 孤独患者1 人赞同了该文章 目录 收起 1、简介 2、模型方法 ManifoldE: 一个基于流形的模型 Sphere Hyperplane 3、实验 提出了一种基于流形的嵌入原理 1、简介 尽管以前的方法取得了成功...
现在的知识图谱可以包含数以亿计的事实(fact),但是知识图谱不可能包含所有实际中存在的事实。因此,链路预测(link prediction)/知识图谱补全(knowledge base completion)成为了研究的一个方向。即如何根据已有的事实,预测可能存在的事实。 受到词嵌入的启发,知识图谱嵌入(knowgraph graph embedding)——将知识图谱映射到离...
Knowledge Graph Embedding for Link Prediction:A Comparative Analysis 原因: 知识图谱(KGs)已在工业和学术界找到了许多应用,这反过来又促使了相当多的研究努力,从各种来源大规模提取信息,即使是最先进的KG也会出现不完整的情况。链接预测(LP)旨在解决KG不完整的问题,链接预测的不断发展,使得越来越多的模型出现,但是...
Link predictionGraph memory networksKnowledge graphs68T30Knowledge graph embedding (KGE) aims to represent entities and relations in a low-dimensional continuous vector space. Recent KGE works focus on incorporating additional information, such as local neighbors and textual descriptions, to learn valuable...
embedding methods in a single view are limited in application due to weakening the hierarchical structure representing the affiliation between entities. To break this limitation, we propose a dual-view hyper-relational KG (DH-KG) structure which contains a hyper-relational instance view for entities ...
Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation ...
Convolutional 2D Knowledge Graph Embeddings https://ojs.aaai.org/index.php/AAAI/article/view/11573 AbstractLink prediction for knowledge graphs is the task of predicting missing relationships between entities.Previous workon link prediction has focused onshallow, fast modelswhich can scale to large ...