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Inductive Logic Programming

Overview of attention for book
Cover of 'Inductive Logic Programming'

Table of Contents

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    Book Overview
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    Chapter 1 Estimation-Based Search Space Traversal in PILP Environments
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    Chapter 2 Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks
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    Chapter 3 Online Structure Learning for Traffic Management
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    Chapter 4 Learning Through Advice-Seeking via Transfer
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    Chapter 5 How Does Predicate Invention Affect Human Comprehensibility?
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    Chapter 6 Distributional Learning of Regular Formal Graph System of Bounded Degree
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    Chapter 7 Learning Relational Dependency Networks for Relation Extraction
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    Chapter 8 Towards Nonmonotonic Relational Learning from Knowledge Graphs
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    Chapter 9 Learning Predictive Categories Using Lifted Relational Neural Networks
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    Chapter 10 Generation of Near-Optimal Solutions Using ILP-Guided Sampling
Attention for Chapter 8: Towards Nonmonotonic Relational Learning from Knowledge Graphs
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Chapter title
Towards Nonmonotonic Relational Learning from Knowledge Graphs
Chapter number 8
Book title
Inductive Logic Programming
Published by
Springer, Cham, September 2016
DOI 10.1007/978-3-319-63342-8_8
Book ISBNs
978-3-31-963341-1, 978-3-31-963342-8
Authors

Hai Dang Tran, Daria Stepanova, Mohamed H. Gad-Elrab, Francesca A. Lisi, Gerhard Weikum, Tran, Hai Dang, Stepanova, Daria, Gad-Elrab, Mohamed H., Lisi, Francesca A., Weikum, Gerhard

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 33%
Researcher 4 27%
Student > Master 2 13%
Student > Bachelor 1 7%
Unknown 3 20%
Readers by discipline Count As %
Computer Science 11 73%
Mathematics 1 7%
Unknown 3 20%

This page is provided by Altmetric.