Inverse entailment and progol

By Stephen H. Muggleton. 1995

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, methodology): “Representation of knowledge in ﬁrst-order logic in neural networks has been an on-going challenge, but it can beneﬁt from studies of propositional logic representation 3.1 using propositionalisation techniques [30].”
PREDICTIVE LEARNING VIA RULE ENSEMBLES (2008, methodology): “Actual ILP algorithms such as CN2 [Clark and Niblett (1989)], RIPPER [Cohen (1995)], and PRO-GOL [Muggleton (1995)] diﬀer with respect to the detailed techniques that implement the generic paradigm.”
Learning Explanatory Rules from Noisy Data (2017, background): “This is the same solution found in Muggleton et al. (2014).”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “For instance, ALEPH is an inductive logic programming (ILP) system that attempts to learn rules from relational data via inverse entailment [104] (For more information on ILP, see, e.”
Reading to Learn: Constructing Features from Semantic Abstracts (2009, methodology): “We perform relational learning using Inductive Logic Programming (ILP), which constructs generalized rules by assembling smaller logical formulae to explain observed propositional examples (Muggleton, 1995).”
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps (2020, methodology): “Compositional Knowledge Base Inference Extracting Horn rules from the KB has been studied extensively in the Inductive Logic Programming literature (Quinlan, 1990; Muggleton, 1995).”
Learning Games from Videos Guided by Descriptive Complexity (2012, methodology): “As the problem is an instance of inductive logic programming (ILP), in most cases the systems relied on the ILP program Progol (Muggleton 1995).”
Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu (2015, methodology): “ALEPH(3) is a general purpose ILP system that implements Muggleton’s Inverse Entailment algorithm [30] in Prolog.”
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs (2019, background): “Mining Horn clauses has been previously studied in the Inductive Logic Programming (ILP) ﬁeld, e.g, FOIL [33], MDIE [30] and Inspire [37].”
AMIE: association rule mining under incomplete evidence in ontological knowledge bases (2013, methodology): “ALEPH(4) is a general purpose ILP system, which implements Muggleton’s Inverse Entailment algorithm [25] in Prolog.”
Fast relational learning using bottom clause propositionalization with artificial neural networks (2013, unknown): “Keywords: Relational Learning, Inductive Logic Programming, Propositionalization, Neural-Symbolic Integration, Artificial Neural Networks”
Structure learning for relational logistic regression: an ensemble approach (2018, methodology): “More specifically, we use PROGOL (Muggleton 1995; Muggleton 1997) for structure learning, followed by relational logistic regression for parameter learning.”
Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, methodology): “There are also systems such as WARMR [106], ALEPH [107], and AMIE [108], [109] which can extract logical rules automatically from KGs. Recently, there has been growing interest in combining logical rules with KG embedding models.”
Machine Learning - The Art and Science of Algorithms that Make Sense of Data (2012, background): “gave the kernel perceptron as a simple example of a kernelised algorithm; in the next chapter we will see another example. Shawe-Taylor and Cristianini (2004) provide an excellent reference bringing together a wealth of material on the use of kernels in machine learning, and Gärtner (2009) discusses”
Inductive Programming: A Survey of Program Synthesis Techniques (2009, background): “The three well-known systems Foil [16] (top-down), Golem [17] (bottom-up), and Progol [18] (mixed) are instantiations of the covering algorithm.”
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning (2017, background): “Early work in ILP such as FOIL [30], PROGOL [23] are either rulebased or require negative examples which is often hard to find in KBs (by design, KBs store true facts).”