Results 81 to 90 of about 8,905,265 (199)
Offline and online coupled tensor factorization with knowledge graph.
How can we accurately decompose a temporal irregular tensor along while incorporating a related knowledge graph tensor in both offline and online streaming settings? PARAFAC2 decomposition is widely applied to the analysis of irregular tensors consisting
SeungJoo Lee, Yong-Chan Park, U Kang
doaj +1 more source
Towards Temporal Knowledge Graph Alignment in the Wild
Temporal Knowledge Graph Alignment (TKGA) seeks to identify equivalent entities across heterogeneous temporal knowledge graphs (TKGs) for fusion to improve their completeness. Although some approaches have been proposed to tackle this task, most assume unified temporal element standards and simplified temporal structures across different TKGs.
Runhao Zhao +5 more
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QLGAN: a quantum-lineage graph attention network for temporal knowledge graph entity alignment
Temporal entity alignment, the task of identifying equivalent entities across evolving knowledge graphs (KGs), is a critical yet challenging problem. Existing methods often struggle to holistically model the complex interplay between structural topology ...
Jia Li, Yuxi Ma, Lingzhong Meng
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Terrorism is one of the most important threats to today’ s civilization. Terrorism not only disturbs the social order but also affects the quality of life.
GAO Hongliang +5 more
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Electric Power Terrorism Event Prediction Based on Temporal Knowledge Graph Embedding
It is crucial to predict power terrorism events for ensuring people life quality and social stability. Existing methods use the global terrorism database ( GTD) to build a two-layer static knowledge graph for predicting power terrorism events.
CHEN Hongshan +5 more
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Dual graph architecture with temporal dynamics for enhanced knowledge tracing
Online education demands precise knowledge state tracking for personalized learning. Current methods struggle to model both knowledge dependencies and dynamic learning patterns.
Yan Zhang, Qianjun Tang
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Traditional temporal knowledge graph completion (TKGC) methods often rely on random encoding or pre‐trained small‐scale language models to initialize entity and relation embeddings. Despite the remarkable reasoning and understanding capabilities of large
Lan Zhao +5 more
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capjamesg/knowledge-graph-language: v0.1.2
<p><strong>Full Changelog</strong>: https://github.com/capjamesg/knowledge-graph-language/commits/V0.1.2</p ...
James
core +1 more source
Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph
In the last few years, the solution to Knowledge Graph (KG) completion via learning embeddings of entities and relations has attracted a surge of interest.
Xu, Chengjin +4 more
core
A Survey on Spatio-Temporal Knowledge Graph Models
Many complex real-world systems exhibit inherently intertwined temporal and spatial characteristics. Spatio-temporal knowledge graphs (STKGs) have therefore emerged as a powerful representation paradigm, as they integrate entities, relationships, time and space within a unified graph structure.
Philipp Plamper +2 more
openaire +3 more sources

