Results 51 to 60 of about 8,905,265 (199)

Spatio-temporal graph data storage and calculation based on grid graph database

International Journal of Digital Earth
How to store and calculate knowledge graph data is an important research direction in database management. As the fundamental elements of spatio-temporal knowledge graph (STKG), spatio-temporal graph data are characterized by large amounts of data ...
Bing Han, Tengteng Qu, Yuhao Huo, Guoyi Sun, Ruoyi Feng, Xiaochong Tong   +5 more
doaj   +1 more source

Temporal Knowledge Graph Forecasting with Neural ODE [PDF]

, 2022
There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a
Tresp, Volker, Han, Zhen, Ding, Zifeng, Gu, Yujia, Ma, Yunpu   +4 more
core   +1 more source

A geographic knowledge integrated computation framework based on grid graph modelling [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Managing dynamic geographic knowledge effectively is hindered by fragmented tools lacking holistic integration, particularly when handling the heterogeneous and evolving nature of real world spatio-temporal data.
B. Han, B. Han, T. Qu
doaj   +1 more source

A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion

CoRR, 2021
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this context, automatic temporal knowledge graph completion (TKGC) has gained great interest.
Zifeng Ding, Yunpu Ma, Bailan He, Volker Tresp   +3 more
openaire   +2 more sources

Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph Completion

Mathematics
Knowledge graphs (KGs) have become a cornerstone for structuring vast amounts of information, enabling sophisticated AI applications across domains. The progression to temporal knowledge graphs (TKGs) introduces time as an essential dimension, allowing ...
Xuechen Zhao, Jinfeng Miao, Fuqiang Yang, Shengnan Pang   +3 more
doaj   +1 more source

Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Applied Sciences, 2023
Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications.
Minwei Wen, Hongyan Mei, Wei Wang, Xing Zhang   +3 more
doaj   +1 more source

A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion

Applied Sciences
Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. In recent years, various TKGC methods have emerged, among which deep learning-based methods have achieved state-of-the-
Ningning Jia, Cuiyou Yao
doaj   +1 more source

FedMDKGE: Multi-granularity Dynamic Knowledge Graph Embedding in Federated Learning

International Journal of Computational Intelligence Systems
As knowledge is time-sensitive, some researchers have started to focus on dynamic knowledge graphs to provide time-dimensioned knowledge content thus reflecting richer information.
Wei Huang, Junling Chen, Dexian Wang, Pengfei Zhang, Jia Liu, Tianrui Li   +5 more
doaj   +1 more source

Bridging graph structure and knowledge-guided editing for interpretable temporal knowledge graph reasoning

Neural Networks
Temporal knowledge graph reasoning (TKGR) aims to predict future events by inferring missing entities with dynamic knowledge structures. Existing LLM-based reasoning methods prioritize contextual over structural relations, struggling to extract relevant subgraphs from dynamic graphs.
Shiqi Fan, Quanming Yao, Hongyi Nie, Wentao Ma, Zhen Wang, Wen Hua   +5 more
openaire   +4 more sources

Anomalous behavior detection based on optimized graph embedding representation in social networks

Journal of King Saud University: Computer and Information Sciences
Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs
Ling Xing, Shiyu Li, Qi Zhang, Honghai Wu, Huahong Ma, Xiaohui Zhang   +5 more
doaj   +1 more source