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MDATA: A New Knowledge Representation Model

Theory, Methods and Applications

  • Book
  • © 2021

Overview

Editors:
  1. Yan Jia

    1. Harbin Institute of Technology, Shenzhen, Shenzhen, China

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  2. Zhaoquan Gu

    1. Guangzhou University, Guangzhou, China

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    You can also search for this editor in PubMed   Google Scholar

  3. Aiping Li

    1. National University of Defense Technology, Changsha, China

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  • Introduces a new knowledge representation model called MDATA
  • Explores some key technologies of the MDATA model
  • Written by experts in the field

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12647)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction to the MDATA Model

    • Yan Jia, Zhaoquan Gu, Aiping Li, Weihong Han
    Pages 1-18

  3. The Framework of the MDATA Computing Model

    • Yan Jia, Aiping Li, Zhaoquan Gu, Bin Zhou, Ye Wang, Xiang Wang et al.
    Pages 19-31

  4. Spatiotemporal Data Cleaning and Knowledge Fusion

    • Huchen Zhou, Mohan Li, Zhaoquan Gu, Zhihong Tian
    Pages 32-50

  5. Chinese Named Entity Recognition: Applications and Challenges

    • Qisen Xi, Yizhi Ren, Siyu Yao, Guohua Wu, Gongxun Miao, Zhen Zhang
    Pages 51-81

  6. Joint Extraction of Entities and Relations: An Advanced BERT-based Decomposition Method

    • Changhai Wang, Aiping Li
    Pages 82-98

  7. Entity Alignment: Optimization by Seed Selection

    • Xiaolong Chen, Le Wang, Yunyi Tang, Weihong Han, Zhihong Tian, Zhaoquan Gu
    Pages 99-116

  8. Knowledge Extraction: Automatic Classification of Matching Rules

    • Yunyi Tang, Le Wang, Xiaolong Chen, Zhaoquan Gu, Zhihong Tian
    Pages 117-130

  9. Network Embedding Attack: An Euclidean Distance Based Method

    • Shanqing Yu, Jun Zheng, Yongqi Wang, Jinyin Chen, Qi Xuan, Qingpeng Zhang
    Pages 131-151

  10. Few-Shot Knowledge Reasoning: An Attention Mechanism Based Method

    • Haocheng Xie, Aiping Li, Yan Jia
    Pages 152-164

  11. Applications of Knowledge Representation Learning

    • Chenchen Li, Aiping Li, Ye Wang, Hongkui Tu
    Pages 165-184

  12. Detection and Defense Methods of Cyber Attacks

    • Kai Xing, Aiping Li, Rong Jiang, Yan Jia
    Pages 185-198

  13. A Distributed Framework for APT Attack Analysis

    • Yulu Qi, Rong Jiang, Aiping Li, Zhaoquan Gu, Yan Jia
    Pages 199-219

  14. Social Unrest Events Prediction by Contextual Gated Graph Convolutional Networks

    • Haiyang Wang, Bin Zhou, Zhipin Gu, Yan Jia
    Pages 220-233

  15. Information Cascading in Social Networks

    • Liqun Gao, Bin Zhou, Yan Jia, Hongkui Tu, Ye Wang
    Pages 234-254

  16. Back Matter

    Pages 255-255
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Keywords

About this book

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.

Editors and Affiliations

  • Harbin Institute of Technology, Shenzhen, Shenzhen, China

    Yan Jia

  • Guangzhou University, Guangzhou, China

    Zhaoquan Gu

  • National University of Defense Technology, Changsha, China

    Aiping Li

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