@article{1434589,
      recid = {1434589},
      author = {Jia, Yan and Gu, Zhaoquan. and Li, Aiping.},
      title = {MDATA : a new knowledge representation model : theory,  methods and applications /},
      publisher = {Springer,},
      address = {Cham :},
      pages = {1 online resource},
      year = {2021},
      note = {Includes author index.},
      abstract = {Knowledge representation is an important task in  understanding how humans think and learn. Although many  representation models or cognitive models have been  proposed, such as expert systems or knowledge graphs, they  cannot represent procedural knowledge, i.e., dynamic  knowledge, in an efficient way. This book introduces a new  knowledge representation model called MDATA  (Multi-dimensional Data Association and inTelligent  Analysis). By modifying the representation of entities and  relations in knowledge graphs, dynamic knowledge can be  efficiently described with temporal and spatial  characteristics. The MDATA model can be regarded as a  high-level temporal and spatial knowledge graph model,  which has strong capabilities for knowledge representation.  This book introduces some key technologies in the MDATA  model, such as entity recognition, relation extraction,  entity alignment, and knowledge reasoning with  spatiotemporal factors. The MDATA model can be applied in  many critical applications and this book introduces some  typical examples, such as network attack detection, social  network analysis, and epidemic assessment. The MDATA model  should be of interest to readers from many research fields  such as database, cyberspace security, and social network,  as the need for the knowledge representation arises  naturally in many practical scenarios.},
      url = {http://library.usi.edu/record/1434589},
      doi = {https://doi.org/10.1007/978-3-030-71590-8},
}