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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
  3. Altmetric Badge
    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
  5. Altmetric Badge
    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
  14. Altmetric Badge
    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.
  16. Altmetric Badge
    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
  18. Altmetric Badge
    Chapter 17 Transfer Learning via Relational Templates
  19. Altmetric Badge
    Chapter 18 Inductive Logic Programming
  20. Altmetric Badge
    Chapter 19 Finding Relational Associations in HIV Resistance Mutation Data
  21. Altmetric Badge
    Chapter 20 ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries
  22. Altmetric Badge
    Chapter 21 Parameter Screening and Optimisation for ILP Using Designed Experiments
  23. Altmetric Badge
    Chapter 22 Don’t Fear Optimality: Sampling for Probabilistic-Logic Sequence Models
  24. Altmetric Badge
    Chapter 23 Policy Transfer via Markov Logic Networks
  25. Altmetric Badge
    Chapter 24 Can ILP Be Applied to Large Datasets?
Attention for Chapter 14: An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.
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Chapter title
An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.
Chapter number 14
Book title
Inductive Logic Programming
Published in
Lecture notes in computer science, January 2010
DOI 10.1007/978-3-642-13840-9_14
Pubmed ID
Book ISBNs
978-3-64-213839-3, 978-3-64-213840-9
Authors

Nassif H, Al-Ali H, Khuri S, Keirouz W, Page D, Houssam Nassif, Hassan Al-Ali, Sawsan Khuri, Walid Keirouz, David Page

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 8%
United States 1 8%
Unknown 10 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 25%
Student > Master 2 17%
Student > Ph. D. Student 1 8%
Student > Doctoral Student 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 4 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 17%
Engineering 2 17%
Computer Science 2 17%
Mathematics 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Other 0 0%
Unknown 4 33%
Attention Score in Context

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
#13,749,545
of 23,310,485 outputs
Outputs from Lecture notes in computer science
#4,031
of 8,160 outputs
Outputs of similar age
#131,665
of 166,014 outputs
Outputs of similar age from Lecture notes in computer science
#89
of 188 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,160 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 48th percentile – i.e., 48% 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 166,014 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 188 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 52% of its contemporaries.