论文笔记:AAAI 2020 Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction 1.前言论文链接:https://arxiv.org/pdf/1911.09419.pdfgithub:https://github.com/MIRALab-USTC/KGE-HAKE...“卡车”。 3.2Hierarchy-AwareKnowledgeGraphEmbedding 为了对以上两个类别进行建模,本文提出了一种层次感知的知...
To address this challenge, we propose a novel knowledge graph embedding model -- namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE) -- which maps entities into the polar coordinate system. HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally ...
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 ...
This is the code of paperLearning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction.Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang.AAAI 2020.arxiv Dependencies Python 3.6+ PyTorch1.0+ Results The results ofHAKEand the baseline modelModEonWN18RR,FB15k-237andYAGO3-10are as foll...
data_modules: Dataset / DataLoader / Collator / Vocab helper: Configure / Hierarchy_Statistic / Logger / Utils models: StructureModel / EmbeddingLayer / TextEncoder / TextPropagation (HiAGM-TP) / Multi-Label Attention (HiAGM-LA) train_modules: Criterions / EvaluationMetrics / TrainerHierarchy...
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...
We propose a novel knowledge graph embedding model called Hierarchy-Aware Paired Relation Vectors Knowledge Graph Embedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge ...
graph embedding is a widely used technique that represents entities and relations in a low-dimensional space to predict missing links in knowledge graphs. However, most existing k...doi:10.1007/s00521-024-09775-yZhangJinglinShenBoZhangYu