MDATA: A New Knowledge Representation Model: Theory, Methods and Applications: 12647 (Lecture Notes in Computer Science, 12647) by Yan Jia,Zhaoquan Gu,Aiping Li: New Paperback (2021)

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Seller Inventory # 6666-IUK-9783030715892

Bibliographic Details

Title: MDATA: A New Knowledge Representation Model: Theory, Methods and Applications: 12647 (Lecture Notes in Computer Science, 12647)
Publisher: Springer 2021-03-07
Publication Date: 2021
Language: English
ISBN 10: 3030715892
ISBN 13: 9783030715892
Binding: Paperback
Condition: New

About this title

Synopsis

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.

From the Back Cover

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.

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