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
(3)由于BERT和Llama2等LMs是使用来自不同信息源的庞大语料库进行预训练的,因此在用于解决TKG推理任务之前,它们不可避免地已经看到了世界知识。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...
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),...
Learning representations for temporal knowledge graphs (TKGs) is a fundamental task. Most existing methods regard TKG as a sequence of static snapshots and recurrently learn representations by retracing the previous snapshots. However, new knowledge can be continuously accrued to TKGs as streams. These...
These methods limit the accuracy of reasoning paths and ignore multiple explainable reasoning paths in temporal knowledge graphs (TKGs). To overcome this limitation, we propose a multi-hop reasoning model TPath in this paper. It is a reinforcement learning (RL) framework which can learn multi-hop...
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...
Temporal knowledge graphs (TKGs) have become an effective tool for numerous intelligent applications. Due to their incompleteness, TKG embedding methods ha... M Li,Z Sun,WLW Zhang - Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving...
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from time-evolving KGs, is crucial for many applications to provi...
In this regard, there have been many studies focusing on the reasoning and completion of static KGs and temporal knowledge graphs (TKGs). For example, TiRGN (Li, Sun et al., 2022) and CENET (Xu, Ou et al., 2023) consider entities or entity relationship pairs in global history, MLRPN...
Temporal knowledge graphs (TKGs) are a form of knowledge representation constructed based on the evolution of events at different time points. It provides an additional perspective by extending the temporal dimension for a range of downstream tasks. Given the evolving nature of events, it is ...