concept

temporal knowledge graphs

Also known as: temporal knowledge graphs (TKGs), temporal knowledge graph, Temporal Knowledge Graph, TKGs

Facts (21)

Sources

Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org Frontiers 6 facts

claimTemporal knowledge graphs are rarely combined with large language models due to scalability concerns and complex modeling requirements, as noted by Wang et al. (2023b).

referenceThe paper 'Two-stage generative question answering on temporal knowledge graph using large language models' (arXiv:2402.16568) proposes a two-stage generative approach for question answering over temporal knowledge graphs using large language models.

referenceR. Liao, X. Jia, Y. Li, Y. Ma, and V. Tresp published 'Gentkg: Generative forecasting on temporal knowledge graph with large language models' as an arXiv preprint in 2023.

referenceR. Liao, X. Jia, Y. Li, Y. Ma, and V. Tresp published 'Gentkg: generative forecasting on temporal knowledge graph with large language models' in the Findings of the Association for Computational Linguistics: NAACL 2024.

referenceCurrent research addresses the gap between temporal knowledge graphs and large language models through retrieval-augmented generation frameworks, such as GenTKG (Liao et al., 2024), and by integrating few-shot learning and instruction tuning to reduce computational costs.

referenceThe paper 'ZRLLM: zero-shot relational learning on temporal knowledge graphs with large language models' (Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics) presents a zero-shot approach for relational learning on temporal knowledge graphs using large language models.

Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org arXiv Jul 9, 2024 4 facts

referenceLee et al. demonstrated that LLMs can learn patterns from historical data in Temporal Knowledge Graphs using in-context learning (ICL) without requiring special architectures or modules.

claimLLMs can perform forecasting using Temporal Knowledge Graphs (TKGs), which are a subset of Knowledge Graphs containing directions and timestamps.

referenceThe Chain-of-History (CoH) reasoning method proposed by Xia et al. uses an LLM to understand the semantic meaning of entities, relationships, and timestamps in a Temporal Knowledge Graph by exploring high-order history chains to reason about query answers.

claimData in Knowledge Graphs is typically represented as a (subject, object, predicate) triple, which can be extended to a (subject, object, predicate, timestamp) quadruple in temporal knowledge graphs to capture facts over time.

Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv Feb 16, 2025 3 facts

referenceIshaan Singh, Navdeep Kaur, Garima Gaur, et al. authored 'Neustip: A novel neuro-symbolic model for link and time prediction in temporal knowledge graphs', published as an arXiv preprint (arXiv:2305.11301) in 2023.

referenceNeuSTIP combines Graph Neural Network (GNN)-based neural processing with symbolic reasoning to tackle link prediction and time interval prediction in temporal knowledge graphs (TKGs).

procedureNeuSTIP employs temporal logic rules, extracted via 'all-walks' on temporal knowledge graphs, to enforce consistency and strengthen reasoning within the neural framework.

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 2 facts

claimDeveloping temporal knowledge graphs that maintain both historical and current data is a promising direction compared to the common practice of using sequences of static knowledge graph snapshot versions.

claimTemporal knowledge graphs can be implemented using temporal annotations to record the validity time interval, which is the period during which a fact was valid, and the transaction time, which is the time when a fact was added or changed.

How NebulaGraph Fusion GraphRAG Bridges the Gap Between ... nebula-graph.io NebulaGraph Jan 27, 2026 2 facts

accountA major telecommunications provider used a temporal knowledge graph to connect work orders and alerts, allowing the system to visually trace relationships and automatically identify the root cause of network incidents.

claimThe methodology of using temporal knowledge graphs for root cause analysis is applicable to domains with complex event logs, including finance, IT, and manufacturing.

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv Sep 22, 2025 2 facts

claimTemporal Question Answering refers to questions with temporal intent that are answered using temporal Knowledge Graphs containing entities, relations, and associated temporal conditions.

referenceGao et al. (2024) developed a two-stage generative question answering method on temporal knowledge graphs using large language models, published in the ACL Findings proceedings.

Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv Nov 7, 2024 1 fact

referenceSingh et al. (2023) introduced NeuSTIP, a neuro-symbolic model designed for link and time prediction in temporal knowledge graphs.

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv Oct 23, 2025 1 fact

referenceThe Zep framework, proposed by Rasmussen et al. in 2025, employs a temporal knowledge graph to manage fact validity and support time-aware reasoning and updates.