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Inductive Logic Programming

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

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Inductive Logic Programming
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    Chapter 2 A Refinement Operator for Inducing Threaded-Variable Clauses
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    Chapter 3 Propositionalisation of Continuous Attributes beyond Simple Aggregation
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    Chapter 4 Topic Models with Relational Features for Drug Design
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    Chapter 5 Pairwise Markov Logic
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    Chapter 6 Evaluating Inference Algorithms for the Prolog Factor Language
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    Chapter 7 Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns
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    Chapter 8 Fast Parameter Learning for Markov Logic Networks Using Bayes Nets
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    Chapter 9 Bounded Least General Generalization
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    Chapter 10 Itemset-Based Variable Construction in Multi-relational Supervised Learning
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    Chapter 11 A Declarative Modeling Language for Concept Learning in Description Logics
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    Chapter 12 Identifying Driver’s Cognitive Load Using Inductive Logic Programming
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    Chapter 13 Opening Doors: An Initial SRL Approach
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    Chapter 14 Probing the Space of Optimal Markov Logic Networks for Sequence Labeling
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    Chapter 15 What Kinds of Relational Features Are Useful for Statistical Learning?
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    Chapter 16 Learning Dishonesty
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    Chapter 17 Heuristic Inverse Subsumption in Full-Clausal Theories
  19. Altmetric Badge
    Chapter 18 Learning Unordered Tree Contraction Patterns in Polynomial Time
Attention for Chapter 1: Inductive Logic Programming
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
2 news outlets

Readers on

mendeley
26 Mendeley
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Chapter title
Inductive Logic Programming
Chapter number 1
Book title
Inductive Logic Programming
Published in
Lecture notes in computer science, January 2013
DOI 10.1007/978-3-642-38812-5_1
Book ISBNs
978-3-64-238811-8, 978-3-64-238812-5
Authors

Fabrizio Riguzzi, Filip Železný, Solly Brown, Claude Sammut, Brown, Solly, Sammut, Claude

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Italy 1 4%
Australia 1 4%
Unknown 23 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 38%
Student > Master 4 15%
Researcher 3 12%
Professor 1 4%
Other 1 4%
Other 2 8%
Unknown 5 19%
Readers by discipline Count As %
Computer Science 11 42%
Engineering 8 31%
Psychology 2 8%
Agricultural and Biological Sciences 1 4%
Unknown 4 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 December 2023.
All research outputs
#1,937,737
of 25,018,122 outputs
Outputs from Lecture notes in computer science
#314
of 8,155 outputs
Outputs of similar age
#18,203
of 293,176 outputs
Outputs of similar age from Lecture notes in computer science
#14
of 316 outputs
Altmetric has tracked 25,018,122 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,155 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 293,176 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 316 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.