Results 21 to 30 of about 9,156,471 (236)

GC-GAT: An integrated approach combining grid calculation and graph attention network for geographic knowledge graph reasoning [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Geographic knowledge graph (KG) reasoning enables the inference of missing entities and relations, a fundamental step toward constructing comprehensive geospatial knowledge systems. However, prevailing approaches often struggle with the accurate modeling
Y. Liu, L. Gao, Y. Zhang, H. Yin
doaj   +1 more source

Reasoning like human:hierarchical reinforcement learning for knowledge graph reasoning [PDF]

, 2020
Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multi-hop reasoning over the information found along other paths connecting a pair of entities.
Chen Gong, Shirui Pan, Wan, Guojia, Zhou, Chuan, Gong, Chen, Pan, Shirui, Chuan Zhou, Guojia Wan, Haffari, Gholamreza, Gholamreza Haffari   +9 more
core   +1 more source

Survey on Construction Method of Temporal Knowledge Graph [PDF]

Jisuanji kexue yu tansuo
As a bridge connecting data, knowledge, and intelligence, knowledge graph has been widely applied in fields such as search assistance, intelligent recommendation, question-answering systems, and natural language processing. However, with the expansion of
LU Jiamin, ZHANG Jing, FENG Jun, AN Qi
doaj   +1 more source

ERGCN: Enhanced Relational Graph Convolution Network, an Optimization for Entity Prediction Tasks on Temporal Knowledge Graphs

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
doaj   +1 more source

TempoQR: Temporal Question Reasoning over Knowledge Graphs

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG).
Costas Mavromatis, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Adesoji Adeshina, Phillip Ryan Howard, Tetiana Grinberg, Nagib Hakim, George Karypis   +7 more
openaire   +4 more sources

Exploiting a graphplan framework in temporal planning [PDF]

, 2003
Graphplan (Blum and Furst 1995) has proved a popular and successful basis for a succession of extensions. An extension to handle temporal planning is a natural one to consider, because of the seductively time-like structure of the layers in the plan ...
Maria Fox, Long, D., Fox, M., Derek Long
core   +2 more sources

MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022
EMNLP 2022 Full ...
Yuwei Xia, Mengqi Zhang 0002, Qiang Liu 0006, Shu Wu, Xiaoyu Zhang 0002   +4 more
openaire   +4 more sources

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas F. Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +1 more source

Research on SARIMA-based embedding for temporal knowledge graph reasoning

Dianzi Jishu Yingyong
This paper focuses on reasoning for Temporal Knowledge Graph (TKG) in periodic scenarios and proposes the EI-SAR model. The model integrates the temporal decomposition capability of the SARIMA model with Temporal Knowledge Graph Embedding (TKGE ...
Wu Yu, Hu Youde, Feng Tong, Du Yingming, Cao Han, Tang Jing, Li Xinbing   +6 more
doaj   +1 more source

Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data

Remote Sensing, 2022
Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features.
Xingtong Ge, Yi Yang, Ling Peng, Luanjie Chen, Weichao Li, Wenyue Zhang, Jiahui Chen   +6 more
doaj   +1 more source