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020 _a9783030715908
_9978-3-030-71590-8
024 7 _a10.1007/978-3-030-71590-8
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.7
_223
245 1 0 _aMDATA: A New Knowledge Representation Model
_h[electronic resource] :
_bTheory, Methods and Applications /
_cedited by Yan Jia, Zhaoquan Gu, Aiping Li.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aX, 255 p. 23 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v12647
505 0 _aIntroduction to the MDATA Model -- The Framework of the MDATA Computing Model -- Spatiotemporal Data Cleaning and Knowledge Fusion -- Chinese Named Entity Recognition: Applications and Challenges -- Joint Extraction of Entities and Relations: An Advanced BERT-based Decomposition Method -- Entity Alignment: Optimization by Seed Selection -- Knowledge Extraction: Automatic Classification of Matching Rules -- Network Embedding Attack: An Euclidean Distance based Method -- Few-Shot Knowledge Reasoning: An Attention Mechanism based Method -- Applications of Knowledge Representation Learning -- Detection and Defense Methods of Cyber Attacks -- A Distributed Framework for APT Attack Analysis -- Social Unrest Events Prediction by Contextual Gated Graph Convolutional Networks -- Information Cascading in Social Networks.
520 _aKnowledge 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.
650 0 _aDatabase management.
650 0 _aArtificial intelligence.
650 0 _aInformation technology
_xManagement.
650 0 _aApplication software.
650 0 _aInformation storage and retrieval systems.
650 1 4 _aDatabase Management System.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer Application in Administrative Data Processing.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aInformation Storage and Retrieval.
700 1 _aJia, Yan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGu, Zhaoquan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLi, Aiping.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030715892
776 0 8 _iPrinted edition:
_z9783030715915
830 0 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v12647
856 4 0 _uhttps://doi.org/10.1007/978-3-030-71590-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cSPRINGER
999 _c185298
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