Recurrent event network: Autoregressive structure inference over temporal knowledge graphs 聚合器对全局图结构和局部邻域进行编码,分别捕获全局和局部信息。重现事件编码器使用图形结构的编码表示序列更新其状态。MLP解码器定义当前图形的概率。 Recurrent event network DE-SimplE (AAAI'20) Diachronic embedding for tempo...
文章来源:Zixuan Li, Xiaolong Jin, Saiping Guan, Wei Li, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng:Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs. ACL/IJCN…
Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion knowledge-graphknowledge-graph-completionknowledge-graph-embeddingsknowledge-graph-embeddingtemporal-knowledge-graphtemporal-knowledge-graphs UpdatedAug 22, 2023 Python INK-USC/DArtNet ...
从历史到未来的原因:时间知识图的两阶段推理 Abstract Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts
2.我们设计了一种基于llm的方法zrLLM,并设法在零射击关系推理中增强各种基于嵌入的TKGF模型。 3.实验结果表明,zrLLM有助于大大提高所有考虑的TKGF模型对包含未见零射关系的事实的预测能力,表明它是有效的和高度自适应的。 摘要: 解决的问题:没有先验图上下文的看不见的零射击关系进行建模、 ...
Fig. 2 Framework of spatial-temporal knowledge graph research Full size|PPT slide 如何建立符合时空知识特点的时空认知与知识图谱表达方法,形成多维度的时空知识分类体系和统一的时空本体,发展顾及复杂时空特征及关系的时空知识图谱自适应表达模型,是时空知识组织管理、更新与计算推理、时空知识表示学习的理论基础。
Temporal knowledge graphs (TKGs) can store and model dynamic relational information. Recently, research on generative models has addressed the limitations of embedding methods in terms of generality and scalability. However, generative models still face two challenges in the context of TKGs: (1) Inabi...
Knowledge Graphs are important tools to model multi-relational data that serves as information pool for various applications. Traditionally, these graphs are considered to be static in nature. However, recent availability of large scale event-based interaction data has given rise to dynamically evolving...
3. A Survey on Knowledge Graphs: Representation, Acquisition and Applications 作者:Shaoxiong Ji, et al. 发表时间:2020 发表于:Expert Systems with Applications, 2020 关键词:知识图谱,综述 概述:本文从知识的表示学习、知识获取,时态知识图谱以及知识感知应用等方面做了阐述,内容全面又不失深度,值得一读。
Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. recommendation, knowledge graph completion). GitHub Link:https://github.com/SpaceLearner/Awesome-DynamicGraphLearning ...