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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 Knowledge-Directed Theory Revision
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    Chapter 2 Towards Clausal Discovery for Stream Mining
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    Chapter 3 On the Relationship between Logical Bayesian Networks and Probabilistic Logic Programming Based on the Distribution Semantics
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    Chapter 4 Induction of Relational Algebra Expressions
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    Chapter 5 A Logic-Based Approach to Relation Extraction from Texts
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    Chapter 6 Discovering Rules by Meta-level Abduction
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    Chapter 7 Inductive Generalization of Analytically Learned Goal Hierarchies
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    Chapter 8 Ideal Downward Refinement in the $\mathcal{EL}$ Description Logic
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    Chapter 9 Nonmonotonic Onto-Relational Learning
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    Chapter 10 CP-Logic Theory Inference with Contextual Variable Elimination and Comparison to BDD Based Inference Methods
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    Chapter 11 Speeding Up Inference in Statistical Relational Learning by Clustering Similar Query Literals
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    Chapter 12 Chess Revision: Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples
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    Chapter 13 ProGolem: A System Based on Relative Minimal Generalisation
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    Chapter 14 An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.
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    Chapter 15 Boosting First-Order Clauses for Large, Skewed Data Sets
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    Chapter 16 Incorporating Linguistic Expertise Using ILP for Named Entity Recognition in Data Hungry Indian Languages
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    Chapter 17 Transfer Learning via Relational Templates
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    Chapter 18 Inductive Logic Programming
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    Chapter 19 Finding Relational Associations in HIV Resistance Mutation Data
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    Chapter 20 ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries
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    Chapter 21 Parameter Screening and Optimisation for ILP Using Designed Experiments
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    Chapter 22 Don’t Fear Optimality: Sampling for Probabilistic-Logic Sequence Models
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    Chapter 23 Policy Transfer via Markov Logic Networks
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    Chapter 24 Can ILP Be Applied to Large Datasets?
Overall attention for this book and its chapters
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
11 Mendeley
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Title
Inductive Logic Programming
Published by
ADS, July 2010
DOI 10.1007/978-3-642-13840-9
ISBNs
978-3-64-213839-3, 978-3-64-213840-9
Editors

Raedt, Luc

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 9%
Unspecified 1 9%
Unknown 9 82%
Readers by discipline Count As %
Unspecified 1 9%
Computer Science 1 9%
Unknown 9 82%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 October 2018.
All research outputs
#7,686,383
of 13,622,595 outputs
Outputs from ADS
#18,480
of 26,518 outputs
Outputs of similar age
#134,391
of 272,365 outputs
Outputs of similar age from ADS
#75
of 194 outputs
Altmetric has tracked 13,622,595 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 26,518 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 272,365 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 194 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.