Our model leverages the advantages of Temporal Knowledge Graphs (TKGs) that can capture both the multi-relations and evolution. We begin by modeling user-item interactions over time by constructing a Temporal Interaction Knowledge Graph (TIKG). We then employ Structure Embedding (SE), Facticity ...
Therefore, the concept of temporal knowledge graphs (TKGs) was proposed by academics. As shown in Fig. 1, there is a sub-graph of a temporal knowledge graph. Similar to static KGs, TKGs also exist incompleteness. Thus, a novel research direction, TKGC (Temporal Knowledge Graph Completion),...
(2022). Additionally, LLMs possess temporal commonsense (Chu et al., 2023), which is often absent in traditional temporal knowledge graphs. This temporal knowledge can complement existing TKGs for TKGQA. 补充知识。语言模型本身可以作为时态知识图谱(TKG)使用,正如Dhingra等人(2022)所展示的那样。此外,...
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
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs Neurips 24 推荐指数: #paper/⭐⭐⭐# 提供了可以刷的时序知识图谱和时序异构图 代码地址:JuliaGast/TGB2: Temporal Graph Benchmark project repo 动机 图结构数据学习在推荐系统、知识库补全及分子学习等领域广泛...
大多数流行的TKGF基准是从2020年之前构建的TKGs中提取的,例如ICEWS14, ICEWS18和ICEWS05-15[15]。其中的事实是基于2019年之前的世界知识,这意味着LMs可能在其训练语料库中遇到它们,这对lm驱动的TKG推理模型构成了信息泄露的威胁。 模型方法: 上图表示了zrllm增强的TKGF模型说明:(a)培训管道。(b)评价管道。
Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future facts based on given history. One of the key challenges for prediction is to learn the evolution of facts. Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for enti...
2024 Elsevier B.V.Temporal knowledge graphs (TKGs) capture relationships and entities evolving over time, making event prediction a challenging task due to the complex temporal and relational dynamics. In this work, we propose BiCoTime, a novel model using bicomplex embeddings to represent entities...
To address this issue, we propose the Graph Hawkes Network to capture the dynamics of evolving graph sequences. Extensive experiments on large-scale temporal relational databases, such as temporal knowledge graphs, demonstrate the effectiveness of our approach. 展开 ...
Knowledge Graph Completion (KGC) is a fundamental problem for temporal knowledge graphs (TKGs), and TKGs embedding methods are one of the essential methods for KGC. However, existing TKG embedding methods encounter a scalability dilemma, i.e., the inconsistency in parameter scalability among differen...