Learning logical definitions from relations

By J. Ross Quinlan. 1990

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Citations in the corpus

Integrating Learning and Reasoning with Deep Logic Models (2019, background): “DLMs also open up the possibility to iteratively integrate rule induction mechanisms like the ones proposed by the Inductive Logic Programming community [17, 25].”
End-to-end Differentiable Proving (2017, methodology): “We can use NTPs for ILP [9] by gradient descent instead of a combinatorial search over the space of rules as, for example, done by the First Order Inductive Learner (FOIL) [30].”
A Review of Relational Machine Learning for Knowledge Graphs (2015, methodology): “Relational learning results: PRA has been shown to outperform the deterministic relational learning method FOIL [102] for link prediction in NELL [101].”
A flexible learning system for wrapping tables and lists in HTML documents (2002, methodology): “There are several di.erences between this learning algorithm and FOIL.”
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs (2019, background): “g, FOIL [33], MDIE [30] and Inspire [37].”
An efficient algorithm for discovering frequent subgraphs (2004, background): “A number of approaches for finding commonly occurring subgraphs have been developed in the context of inductive logic programming (ILP) systems [38], [33], [44], [34], [19], as graphs can be easily expressed using first-order logic.”
Multi-relational data mining: an introduction (2003, background): “This classical ILP task is addressed, for instance, by the seminal MIS system [46] (rightfully considered as one of the most influential ancestors of ILP) and one of the best known ILP systems FOIL [42].”
Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding (1992, background): “It is an open empirical question whether and where our neural-based approach generalizes better than inductive logic programming approaches (Muggleton, 1992; Quinlan, 1990), which learn a larger class of languages.”
Knowledge Infusion (2006, background): “Then, in effect, a three-valued logic will be realized using two binary variables for each relation.”
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).”
On the Advance of Making Language Models Better Reasoners (2022, background): “training set of objects whose classes are known, find a rule for predicting the class of an unseen object as a function of its attribute values (Quinlan, 1990).”
How Does Predicate Invention Affect Human Comprehensibility? (2016, background): “Failure to address these issues has, until recently, led to limited progress being made in this important topic and many well-known ILP systems such as ALEPH [16] and FOIL [14] have no predicate invention.”
Machine Learning and Software Engineering (2002, unknown): “The following list of publications and web sites offers a good starting point for the interested reader to be acquainted with the state-of-the-practice in ML applications (Aha; Bergadano and Gunetti, 1995; Bratko and Muggleton, 1995; Cristianini and Shawe-Taylor, 2000; Dietterich, 1997; Langley and ”
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).”
An experimental evaluation of simplicity in rule learning (2008, methodology): “To realize this, we compare well-known rule learners, such as CN2, RIPPER, PART, FOIL and C5.”
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text (2019, methodology): “of inductive logic programming (Quinlan, 1990)— as well as the recently developed CLEVR dataset for visual reasoning (Johnson et al.”
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, methodology): “The AI community uses inductive logic programming methods to infer logic programs from databases of positive and negative examples [48].”
Statistical fraud detection: A review (2002, methodology): “Examples of such algorithms include BAYES (Clark and Niblett, 1989), FOIL (Quinlan, 1990) and RIPPER (Cohen, 1995).”
Learning Generalized Policies from Planning Examples Using Concept Languages (2004, unknown): “manner [22].”