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IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs [PDF]
Entropy, 2023 Temporal knowledge graphs (KGs) have recently attracted increasing attention. The temporal KG forecasting task, which plays a crucial role in such applications as event prediction, predicts future links based on historical facts. However, current studies
Zhenyu Du +5 more
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MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs [PDF]
Future Internet, 2023 Temporal knowledge graphs play an increasingly prominent role in scenarios such as social networks, finance, and smart cities. As such, research on temporal knowledge graphs continues to deepen.
Yong Yu +5 more
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ISPRS International Journal of Geo-Information, 2021 This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena.
Géraldine Del Mondo +4 more
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Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs [PDF]
Frontiers in Research Metrics and Analytics, 2023 The COVID-19 pandemic highlighted two critical barriers hindering rapid response to novel pathogens. These include inefficient use of existing biological knowledge about treatments, compounds, gene interactions, proteins, etc.
Alessandro Negro +6 more
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Approximate Query on Temporal Knowledge Graphs via Two-Level Embeddings [PDF]
EntropyApproximate query on knowledge graphs (KGs) is an important and common task in real-world applications, where the goal is to return more results on KGs that match the query criteria. Previous approximate query methods have focused on static KGs. However,
Jiaxuan Liu, Xinyi Duan, Luyi Bai
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Future Internet, 2022 Reasoning on temporal knowledge graphs, which aims to infer new facts from existing knowledge, has attracted extensive attention and in-depth research recently.
Yinglin Wang, Xinyu Xu
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Extrapolation Reasoning on Temporal Knowledge Graphs via Temporal Dependencies Learning
CAAI Transactions on Intelligence TechnologyExtrapolation on Temporal Knowledge Graphs (TKGs) aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order. The temporally adjacent facts in TKGs naturally form event sequences, called event evolution patterns ...
Ye Wang +5 more
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SkelFormer: An adaptive hierarchical transformer-based approach on skeleton graphs for human action recognition in video sequences. [PDF]
PLoS ONEHuman skeleton-based action recognition represents a pivotal field of study, capturing the intricate interplay between physical dynamics and intentional actions.
Jiexing Yan +3 more
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TempReasoner: neural temporal graph networks for event timeline construction [PDF]
Scientific ReportsConstructing event timelines from unstructured temporal data is a fundamental challenge for knowledge extraction and reasoning systems. Existing temporal reasoning methods face challenges in jointly modelling fine-grained temporal dependencies, sparse ...
Mohammed Aldawsari
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Temporal Knowledge Graph Representation Learning [PDF]
Jisuanji kexue, 2022 As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the ...
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai
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