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008			210306s2021 gw a o |||| 0|eng d
020			_a9783030715908
024	7		_a10.1007/978-3-030-71590-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ335 _b2021 EB
245	0	0	_aMDATA : _ba new knowledge representation model : theory, methods and applications _cedited by Yan Jia, Zhaoquan Gu, Aiping Li
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (X, 255 páginas) _b23 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aInformation Systems and Applications incl. Internet/Web and HCI _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	3		_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.
988			_aSpringer_Computer_2021
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aJia, Yan _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aGu, Zhaoquan _eeditor literario _0(orcid)0000-0001-7546-852X _1https://orcid.org/0000-0001-7546-852X _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLi, Aiping _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-71590-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _eb _zSI