Results 61 to 70 of about 8,049,750 (250)

QLite: Lightweight Knowledge Graph Embedding Framework With Query Processing

IEEE Access
A vast number of studies on knowledge graph embedding have been conducted. However, most knowledge graph embedding models have high dimensional embedding vectors.
Chun-Hee Lee, Dong-Oh Kang
doaj   +1 more source

GeS as an Emerging OTS Material: Atomic Structure and Modeling of ON/OFF Switching

Advanced Functional Materials, EarlyView.
The structural puzzle of glassy GeS, an advanced memory‐selector material, has finally been disentangled, revealing a patchwork of mixed tetrahedral and trigonal species. Under a high electric field, over‐ and under‐coordinated Ge and S defects, together with three‐membered rings, form an interconnected network associated with localized and extended ...
Andrey Tverjanovich, Jörg C. Neuefeind, Chris J. Benmore, Daniele Fontanari, Anton Sokolov, Maxim Khomenko, Shinji Kohara, Aleksei Bytchkov, Takeshi Usuki, Maria Bokova, Mohammad Kassem, Pascal Masselin, Mariana Milochova, Eugene Bychkov   +13 more
wiley   +1 more source

HFKG-RFE: An algorithm for heterogeneous federated knowledge graph.

PLoS ONE
Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance.
Chunjuan Li, Hong Zheng, Gang Liu
doaj   +1 more source

Bioprinted Stiffness‑Tunable Pancreatic Microniches to Decode Early Tumor Initiation Mechanisms

Advanced Healthcare Materials, EarlyView.
Stiffness‐tunable GelMA microniches generated by microvalve bioprinting preserve wild‐type acinar identity while revealing a nonlinear, Kras‐dependent response to matrix mechanics. Intermediate stiffness promotes acinar‐to‐ductal metaplasia and proliferation, defining a permissive mechanical window.
Aurelien Mazet, Charles Handschin, Theo Desigaux, Nathalie Dusserre, Marie‐Laure Stachowicz, Shane Fennell, Malou Lea, Silvia Arcucci, Benoît Thibault, Jean‐Christophe Fricain, Julie Guillermet‐Guibert, Hugo Oliveira   +11 more
wiley   +1 more source

Interest Capturing Recommendation Based on Knowledge Graph [PDF]

Jisuanji kexue
As a kind of auxiliary information,knowledge graph can provide more context information and semantic association information for the recommendation system,thereby improving the accuracy and interpretability of the recommendation.By mapping items into ...
JIN Yu, CHEN Hongmei, LUO Chuan
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +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

Knowledge Graph Embedding With Interactive Guidance From Entity Descriptions

IEEE Access, 2019
Knowledge Graph (KG) embedding aims to represent both entities and relations into a continuous low-dimensional vector space. Most previous attempts perform the embedding task using only knowledge triples to indicate relations between entities.
Wen'an Zhou, Shirui Wang, Chao Jiang
doaj   +1 more source

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

Advanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng, Chuanyu Liu, Qian Wang, Melissa Jane Meadowcroft, Vlad Badilita, Carlos Miguel Costa, Senentxu Lanceros‐Méndez, Ying Li, Chenyao Ma, Weibo Gong, Surendra Kumar Martha, Aloysius Soon, Piao Ma, Di Zhang, Teguh Ariyanto, Sudaryanto, Hiroshi Kakinuma, Jie Zhao, Jiayu Peng, Pengfei Ou, Jun Lu, Shin‐ichi Orimo, Hao Li   +24 more
wiley   +1 more source

Adversarially regularized graph autoencoder for graph embedding

, 2018
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
Jiang, Jing, Ruiqi Hu, Long, Guodong, Chengqi Zhang, Yao, Lina, Zhang, Chengqi, Hu, Ruiqi, Pan, S, Guodong Long, Jiang, J, Shirui Pan, Lina Yao, Long, G, Zhang, C, Yao, L, Pan, Shirui, Hu, R, Jing Jiang   +17 more
core   +1 more source