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The prevailing focus in contemporary research on temporal knowledge graph reasoning lies in extrapolation knowledge graph reasoning, aimed at forecasting future events in the ground of …

Temporal Knowledge Graphs (TKGs) reflect the dynamic temporal evolution of real-world facts. The extrapolation of TKG reasoning, which predicts future facts based on histories in TKGs, …

Temporal knowledge graph reasoning addresses the challenge of understanding and reasoning over time-varying knowledge, enabling systems to make informed decisions and predictions …

Temporal knowledge graph reasoning addresses the challenge of understanding and reasoning over time-varying knowledge, enabling systems to make informed decisions and predictions …

Nov 1, 2024 · Temporal Knowledge Graphs (TKGs) enable effective modeling of knowledge dynamics and event evolution, facilitating deeper insights and analysis into temporal …

Aug 31, 2024 · 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 …

To this end, we propose a new temporal graph reasoning framework, called the Dual Guiding constrained generative model (DuGen), which primarily consists of a context summarizer and …

Jan 23, 2025 · Temporal knowledge graphs (TKGs) are critical tools for capturing the dynamic nature of facts that evolve over time, making them highly valuable in a broad spectrum of …

Nov 1, 2024 · Temporal knowledge graph reasoning is a technique used to predict unknown entities and relations at specific points in time. It encompasses two main approaches: …

In this paper, we investigate a realistic but underexplored problem, called few-shot temporal knowledge graph reasoning, that aims to predict future facts for newly emerging entities based …

Dec 1, 2024 · Temporal Knowledge Graph (TKG) reasoning aiming at forecasting future events based on historical snapshots distributed over timestamps. Referred to as extrapolation in …

Apr 30, 2023 · Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on historical TKG data. Most of the existing methods are incapable of explicitly modeling the …

Mar 10, 2025 · Therefore, we propose to describe a temporal fact more accurately as an n-tuple, containing not only its predicate and core entities, but also its auxiliary entities, as well as the …

6 days ago · Graph Retrieval-Augmented Generation (GraphRAG) has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external …

Apr 1, 2023 · In this paper, we propose a novel model, TLmod, for temporal knowledge graph reasoning tasks. We define five types of temporal logical rules. Meanwhile, we propose a …

Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized …

We then integrate the modeled knowledge in a Siamese graph neural network encoder through a well-designed contrastive loss. ... When network host logs are abstracted into discrete …

Apr 1, 2024 · Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph (KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial task that aims to …

Sep 5, 2022 · In this paper we propose a subgraph-based model for answering complex questions over temporal knowledge graphs (TKG), inspired by human cognition. Our method, called …