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Trends and Applications in Knowledge Discovery and Data Mining

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Table of Contents

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    Book Overview
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    Chapter 1 Matching Biomedical Ontologies with Compact Evolutionary Algorithm
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    Chapter 2 Towards Expert Preference on Academic Article Recommendation Using Bibliometric Networks
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    Chapter 3 Research in Progress: Explaining the Moderating Effect of Massively Multiplayer Online (MMO) Games on the Relationship Between Flow and Game Addiction Using Literature-Based Discovery (LBD)
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    Chapter 4 A Network Approach for Mapping and Classifying Shared Terminologies Between Disparate Literatures in the Social Sciences
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    Chapter 5 Towards Creating a New Triple Store for Literature-Based Discovery
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    Chapter 6 Who Is Who in Literature-Based Discovery: Preliminary Analysis
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    Chapter 7 Adversarial Deep Factorization for Recommender Systems
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    Chapter 8 Detection of Spammers Using Modified Diffusion Convolution Neural Network
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    Chapter 9 Dynamics of Online Toxicity in the Asia-Pacific Region
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    Chapter 10 Predictive K-means with Local Models
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    Chapter 11 Automatic Detection of Sexist Statements Commonly Used at the Workplace
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    Chapter 12 Hybrid Dimensionality Reduction Technique for Hyperspectral Images Using Random Projection and Manifold Learning
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    Chapter 13 Clustering Spatial Data via Mixture Models with Dynamic Weights
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    Chapter 14 Temporal Convolutional Network Based Transfer Learning for Structural Health Monitoring of Composites
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    Chapter 15 Dilated Convolutional Recurrent Deep Network with Transfer Learning for Remaining Useful Life Prediction
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    Chapter 16 Generative Modeling for Synthesis of Cellular Imaging Data for Low-Cost Drug Repurposing Application
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    Chapter 17 A Study of Game Payment Data Mining: Predicting High-Value Users for MMORPGs
Attention for Chapter 5: Towards Creating a New Triple Store for Literature-Based Discovery
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Chapter title
Towards Creating a New Triple Store for Literature-Based Discovery
Chapter number 5
Book title
Trends and Applications in Knowledge Discovery and Data Mining
Published by
Springer, Cham, May 2020
DOI 10.1007/978-3-030-60470-7_5
Book ISBNs
978-3-03-060469-1, 978-3-03-060470-7
Authors

Anna Koroleva, Maria Anisimova, Manuel Gil, Koroleva, Anna, Anisimova, Maria, Gil, Manuel

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 30%
Student > Doctoral Student 1 10%
Lecturer > Senior Lecturer 1 10%
Student > Master 1 10%
Researcher 1 10%
Other 0 0%
Unknown 3 30%
Readers by discipline Count As %
Computer Science 5 50%
Pharmacology, Toxicology and Pharmaceutical Science 1 10%
Unknown 4 40%