Results 51 to 60 of about 69,930 (266)

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

Semantic Modeling in Materials Science and Engineering With Platform MaterialDigital Core Ontology 3.0

Advanced Engineering Materials, EarlyView.
The community‐driven Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a Basic Formal Ontology‐aligned semantic backbone for the processing–structure–properties paradigm in Materials Science and Engineering. Modular engineering, automated releases, and validation workflows are highlighted and key semantic patterns for materials ...
Markus Schilling, Philipp von Hartrott, Jörg Waitelonis, Thomas Hanke, Henk Birkholz, Hossein Beygi Nasrabadi, Khashayar Razghandi, Kamilla Zaripova, Felix Thonagel, Fabian Neuhaus, Martin Glauer, Lars Vogt, Harald Sack, Lutz Mädler, Bernd Bayerlein, Chris Eberl   +15 more
wiley   +1 more source

Increasing Inter‐Micellar Connectivity Toughens and Imparts Cooling‐Induced Shape Memory in Micellar Hydrogels

Advanced Functional Materials, EarlyView.
Oligomerizing Pluronic triblock copolymers provides a processing strategy for tuning the mechanical properties and stimuli‐responsive behaviors of micellar hydrogels. Varying oligomer fraction produces hydrogels spanning brittle to highly extensible responses; maintaining micellar architectures enables cooling‐induced reverse thermal shape memory and ...
Gourav Kumbhojkar, Eric Yang, Arman Moussavi, EunBi Oh, Haeji Kim, Wei Chen, Junsoo Kim, Sinan Keten, Ryan L. Truby   +8 more
wiley   +1 more source

Temporal knowledge graph reasoning using global and recent history information

Scientific Reports
Since the static Knowledge Graph cannot meet the dynamics of knowledge in the real world, Temporal Knowledge Graph has become a potential method for processing temporal knowledge.
Changlong Wang, Jianlong Cao, Wenzheng Guo, Jie Hu, Yaoyao Hu, Yi Liu, Yawei Li, Huimei Tian, Pengjuan Lu, Wenhao Zhao, Zhixiong Yin   +10 more
doaj   +1 more source

A 3D Human Neuron‐on‐Chip Platform to Monitor Neuronal Injury Responses

Advanced Healthcare Materials, EarlyView.
This study presents a novel 3D Neuron‐on‐Chip model that can maintain human PSC‐derived excitatory prefrontal cortex neurons in 3D hydrogels and can be used to monitor neuronal injury responses over time. Results show injury‐induced acute neuronal excitotoxicity, declining neuronal connectivity, and the activation of a neurodegenerative, SASP‐like ...
Ruiping Tang, Charles‐Francois Latchoumane, Avi Chopra, Md Marzan Sarkar, Chunki Kim, Nathan Gonsalves, Hsueh‐Fu Wu, John C. Sentmanat, Alan Liu, Isha Mhatre‐Winters, Aditya Mishra, Andrei G. Fedorov, Jay M. Patel, Nadja Zeltner, Steven L. Stice, Jason R. Richardson, Lohitash Karumbaiah   +16 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

Timespan-Aware Dynamic Knowledge Graph Embedding by Incorporating Temporal Evolution

IEEE Access, 2020
Recently, Knowledge Graph Embedding (KGE) has attracted considerable research efforts, since it simplifies the manipulation while preserving the inherent structure of the KG.
Xiaoli Tang, Rui Yuan, Qianyu Li, Tengyun Wang, Haizhi Yang, Yundong Cai, Hengjie Song   +6 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   +3 more sources

Mimetic Core‐Sheath Silk Sutures with Superior‐Strength for Pain Management, Anti‐Infection and Wound Healing

Advanced Healthcare Materials, EarlyView.
ABSTRACT Sutures are crucial for tissue approximation and wound healing. Besides their strength limits requirement, they often fail to effectively address complications like infection and pain. Inspired by natural silk's “core‐sheath” structure, our suture features a wet‐spun core of n zinc‐fortified silk fibroin fibers (RSF‐Zn2+) after drawing with ...
Yi Peng, Kai Ma, Xiaoxiao Li, Xiaozhuo Wu, Yuezhou Zhang, Ying Luo, Chenxing Zhang, Wen Zhong, Chunjing Yu, Lin Lu, Malcolm Xing, Guozhong Lyu   +11 more
wiley   +1 more source

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