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MDATA: A New Knowledge Representation Model

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Cover of 'MDATA: A New Knowledge Representation Model'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to the MDATA Model
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    Chapter 2 The Framework of the MDATA Computing Model
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    Chapter 3 Spatiotemporal Data Cleaning and Knowledge Fusion
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    Chapter 4 Chinese Named Entity Recognition: Applications and Challenges
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    Chapter 5 Joint Extraction of Entities and Relations: An Advanced BERT-based Decomposition Method
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    Chapter 6 Entity Alignment: Optimization by Seed Selection
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    Chapter 7 Knowledge Extraction: Automatic Classification of Matching Rules
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    Chapter 8 Network Embedding Attack: An Euclidean Distance Based Method
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    Chapter 9 Few-Shot Knowledge Reasoning: An Attention Mechanism Based Method
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    Chapter 10 Applications of Knowledge Representation Learning
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    Chapter 11 Detection and Defense Methods of Cyber Attacks
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    Chapter 12 A Distributed Framework for APT Attack Analysis
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    Chapter 13 Social Unrest Events Prediction by Contextual Gated Graph Convolutional Networks
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    Chapter 14 Information Cascading in Social Networks
Attention for Chapter 6: Entity Alignment: Optimization by Seed Selection
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Chapter title
Entity Alignment: Optimization by Seed Selection
Chapter number 6
Book title
MDATA: A New Knowledge Representation Model
Published by
Springer, Cham, January 2021
DOI 10.1007/978-3-030-71590-8_6
Book ISBNs
978-3-03-071589-2, 978-3-03-071590-8
Authors

Chen, Xiaolong, Wang, Le, Tang, Yunyi, Han, Weihong, Tian, Zhihong, Gu, Zhaoquan

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