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@ARTICLE{Ihsan:1034332,
      author       = {Ihsan, Ahmad Zainul and Fathalla, Said and Sandfeld,
                      Stefan},
      title        = {{M}odeling dislocation dynamics data using semantic web
                      technologies},
      journal      = {Neural computing $\&$ applications},
      volume       = {37},
      issn         = {0941-0643},
      address      = {London},
      publisher    = {Springer},
      reportid     = {FZJ-2024-07117},
      pages        = {11737-11753},
      year         = {2025},
      abstract     = {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.},
      cin          = {IAS-9},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1007/s00521-024-10674-5},
      url          = {https://juser.fz-juelich.de/record/1034332},
}