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| 001 | 1034332 | ||
| 005 | 20250625202305.0 | ||
| 024 | 7 | _ | |a 10.1007/s00521-024-10674-5 |2 doi |
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| 100 | 1 | _ | |a Ihsan, Ahmad Zainul |0 P:(DE-Juel1)185910 |b 0 |u fzj |
| 245 | _ | _ | |a Modeling dislocation dynamics data using semantic web technologies |
| 260 | _ | _ | |a London |c 2025 |b Springer |
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| 520 | _ | _ | |a The research in Materials Science and Engineering focuses on the design, synthesis, properties, and performance of materials. An important class of materials that is widely investigated are crystalline materials, including metals and semiconductors. Crystalline material typically contains a specific type of defect called “dislocation”. This defect significantly affects various material properties, including bending strength, fracture toughness, and ductility. Researchers have devoted a significant effort in recent years to understanding dislocation behaviour through experimental characterization techniques and simulations, e.g., dislocation dynamics simulations. This paper presents how data from dislocation dynamics simulations can be modelled using semantic web technologies through annotating data with ontologies. We extend the dislocation ontology by adding missing concepts and aligning it with two other domain-related ontologies (i.e., the Elementary Multi-perspective Material Ontology and the Materials Design Ontology), allowing for efficiently representing the dislocation simulation data. Moreover, we present a real-world use case for representing the discrete dislocation dynamics data as a knowledge graph (DisLocKG) which can depict the relationship between them. We also developed a SPARQL endpoint that brings extensive flexibility for querying DisLocKG. |
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| 700 | 1 | _ | |a Fathalla, Said |0 P:(DE-Juel1)190520 |b 1 |u fzj |
| 700 | 1 | _ | |a Sandfeld, Stefan |0 P:(DE-Juel1)186075 |b 2 |e Corresponding author |
| 773 | _ | _ | |a 10.1007/s00521-024-10674-5 |0 PERI:(DE-600)1480526-1 |p 11737-11753 |t Neural computing & applications |v 37 |y 2025 |x 0941-0643 |
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