Results 71 to 80 of about 6,810,610 (247)

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Noise-augmented contrastive learning with attention for knowledge-aware collaborative recommendation

Scientific Reports
Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) based model has gradually become the theme of Collaborative Knowledge Graph (CKG).
Wanyi Gu, Hua Xu, Xiang Peng, Ning Rao, Huali Zhu   +4 more
doaj   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

Study on Graph Collaborative Filtering Model Based on FeatureNet Contrastive Learning [PDF]

Jisuanji kexue
Graph-based collaborative filtering recommendation techniques have gained significant attention for their ability to efficiently process large-scale interaction data.However,the effectiveness of these techniques is limited by the sparsity of data in real-
WU Pengyuan, FANG Wei
doaj   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Co-augmentation of structure and feature for boosting graph contrastive learning

Graph Contrastive Learning (GCL) learns invariant representation by maximizing the consistency between different augmented graphs that share the same semantics. However, the performance of existing GCL methods is inseparable from varied manually designed
Pan, Shirui, Yan, Rong, Bao, Peng
core   +1 more source

The PRIMA Thesaurus for Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa, Alexey Boubnov, Dario De Angelis, Catriona Eschke, Maximilian Kabbe, Felix Kraus, Mirco Panighel, Richard Thelen, Davide Valentinis   +8 more
wiley   +1 more source

Prototype based contrastive graph clustering network for reducing false negatives

Scientific Reports
Contrastive graph clustering methods significantly enhance the clustering performance of graph data by leveraging multi-view augmentation and contrastive loss. In particular, Self-Supervised Graph Contrastive Learning (SS-GCL) has gained attention due to
Cuihua Ma, Chaosheng Tang, Ziqi Deng, Fuyun Li, Haixia Long, Jieren Cheng   +5 more
doaj   +1 more source

Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels

, 2021
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of semi-supervised node classification. However, most existing GNN models require sufficient labeled data for effective network training.
Wan, S, Zhan, Y, Liu, L, Yu, B, Pan, S, Gong, C   +5 more
core