Results 101 to 110 of about 7,645,086 (295)

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

A Hierarchical Knowledge Graph Embedding Framework for Link Prediction

IEEE Access
Knowledge graph embedding maps the semantics of entities and relations to a low-dimensional space by optimizing the vector distance between positive and negative triples.
Shuang Liu, Chengwang Hou, Jiana Meng, Peng Chen, Simon Kolmanic   +4 more
doaj   +1 more source

Scalable Robust Graph Embedding with Spark

, 2022
Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties.
Weidlich, Matthias, Nguyen, Quoc Viet Hung, Yin, Hongzhi, Aberer, Karl, Hoang, Trung Dung, Duong, Chi Thang   +5 more
core   +1 more source

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source

Convolutional 2D Knowledge Graph Embeddings

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models — which potentially limits performance ...
Dettmers, Tim, Minervini, Pasquale, Stenetorp, Pontus, Riedel, Sebastian   +3 more
openaire   +6 more sources

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source

Fast Variational Knowledge Graph Embedding

2024 IEEE International Conference on Quantum Computing and Engineering (QCE)
Embedding of a knowledge graph(KG) entities and relations in the form of vectors is an important aspect for the manipulation of the KG database for several downstream tasks, such as link prediction, knowledge graph completion, and recommendation.
Pulak Ranjan Giri, Mori Kurokawa, Kazuhiro Saito   +2 more
openaire   +2 more sources

Network Embedding Learning in Knowledge Graph [PDF]

, 2019
University of Technology Sydney. Faculty of Engineering and Information Technology.Knowledge Graph stores a large number of human knowledge facts in form of multi-relational network structure, is widely used as a core technique in real-world applications
Zhou, Zili
core  

OntOMat: Toward Ontology‐Based Product and Process Design Engineering and Optimization Solutions Fueling Circular Value Chains

Advanced Engineering Materials, EarlyView.
The OntOMat ontology establishes a structured framework for polymer matrix fiber reinforced composite materials, integrating manufacturing processes, characterization methods, and multiscale design through the VDI/VDE 3682 formalized process description standard.
Nicolas Christ, Jörg Hohe, Martin Kördel, Michael Giese, Niklas Toffert, Christian Seidel, Olaf Rehme, Patrik Schneider, Oliver Focke, Elie Abi Aoun, Claas Falldorf, Beñat Gutierrez, Alexandra Kühn, Sven Torstrick-von der Lieth, Christoph Herden, Erik Johannsen, Jan‐Uwe R. Schmidt, Felix Winkelmann, Andreas Schott, Daniel Schott   +19 more
wiley   +1 more source

3D Bioprinted Glioblastoma Multiforme Models: How the Extracellular Matrix Glycosignature Influences Drug Response

Advanced Functional Materials, EarlyView.
Aberrant glycosylation in the glioblastoma tumor microenvironment drives therapeutic resistance. Here, a 3D bioprinted model was engineered by incorporating α‐NeuNAc‐(2→3)‐β‐D‐Gal‐ and chondroitin sulfate. Combined multiplex immunofluorescence and synchrotron‐based nanoCT analysis revealed that glycan‐matrix interactions dictate specific drug‐escape ...
Francesca Cadamuro, Marco Piazzoni, Ilaria Borghi, Alberto Baeri, Alessandro Fumagalli, Federica Perillo, Hafiz Muhammad Fahad, Martina Ghizzi, Luca Crippa, Gabriella Nicolini, Angela Bentivegna, Daniele Ramazzotti, Valentina Zuliani, Mirko Rivara, Carlo Giorgio Giussani, Andrea Di Cristofori, Guido Cavaletti, Federica Facciotti, Sobhan Sheikhi, Claire Seydoux, Murielle Salome, Peter Cloetens, Alberto Bravin, Paola Coan, Francesco Nicotra, Laura Russo   +25 more
wiley   +1 more source