TY  - GEN
AB  - 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.
AU  - Jia, Yan
AU  - Gu, Zhaoquan.
AU  - Li, Aiping.
CN  - Q387
CY  - Cham :
DA  - 2021.
DO  - 10.1007/978-3-030-71590-8
DO  - doi
ID  - 1434589
KW  - Knowledge representation (Information theory)
KW  - Information visualization.
KW  - Représentation des connaissances.
KW  - Visualisation de l'information.
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-71590-8
N1  - Includes author index.
N2  - 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.
PB  - Springer,
PP  - Cham :
PY  - 2021.
SN  - 9783030715908
SN  - 3030715906
T1  - MDATA :a new knowledge representation model : theory, methods and applications /
TI  - MDATA :a new knowledge representation model : theory, methods and applications /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-71590-8
VL  - 12647
ER  -