scholarly journals Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs

2002 ◽  
Vol 16 ◽  
pp. 135-166 ◽  
Author(s):  
H. Blockeel ◽  
L. Dehaspe ◽  
B. Demoen ◽  
G. Janssens ◽  
J. Ramon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Complexity Analysis ◽  
Relational Learning ◽  
Inductive Logic ◽  
Learning Systems ◽  
Empirical Results

Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.

Discovering and organizing noun-verb collocations in specialized corpora using inductive logic programming

2006 ◽  
Vol 11 (2) ◽  
pp. 209-243 ◽  
Author(s):  
Vincent Claveau ◽  
Marie-Claude L'Homme
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Success Rates ◽  
Machine Learning Technique ◽  
Acquisition Process ◽  
Semantic Relationships ◽  
Learning Technique ◽  
French Corpus

This article presents a method for discovering and organizing noun-verb (N-V) combinations found in a French corpus on computing. Our aim is to find N-V combinations in which verbs convey a “realization meaning” as defined in the framework of lexical functions (Mel’čuk 1996, 1998). Our approach, chiefly corpus-based, uses a machine learning technique, namely Inductive Logic Programming (ILP). The whole acquisition process is divided into three steps: (1) isolating contexts in which specific N-V pairs occur; (2) inferring linguistically-motivated rules that reflect the behaviour of realization N-V pairs; (3) projecting these rules on corpora to find other valid N-V pairs. This technique is evaluated in terms of the relevance of the rules inferred and in terms of the quality (recall and precision) of the results. Results obtained show that our approach is able to find these very specific semantic relationships (the realization N-V pairs) with very good success rates.

Graph-Based Concept Discovery

2019 ◽  
pp. 577-588
Author(s):  
Alev Mutlu ◽  
Pinar Karagoz ◽  
Yusuf Kavurucu
Keyword(s):  
Data Mining ◽  
Logic Programming ◽  
Relational Database ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Data Representation ◽  
Search Method ◽  
Specific Relation ◽  
Concept Discovery ◽  
Evaluation Mechanism

Multi-relational data mining (MRDM) is concerned with discovering hidden patterns from multiple tables in a relational database. One of the most commonly addressed tasks in MRDM is concept discovery in which the problem is inducing logical definitions of a specific relation, called target relation, in terms of other relations, called background knowledge. Inductive logic programming-based and graph-based approaches are two main competitors in this research. In this chapter, the authors aim to introduce concept discovery problem and compare state-of-the-art methods in graph-based concept discovery by means of data representation, search method, and concept descriptor evaluation mechanism.

Inductive logic programming applied for knowledge representation in computer music/ Programação lógica indutiva aplicada para representação do conhecimento em música computacional

2021 ◽  
Vol 5 (4) ◽  
pp. 1840-1857
Author(s):  
Clenio B. Gonçalves Junior ◽  
Murillo Rodrigo Petrucelli Homem
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Computer Music ◽  
Inductive Logic ◽  
Essential Element ◽  
Research Field ◽  
Musical Knowledge

 In Computer Music, the knowledge representation process is an essential element for the development of systems. Methods have been applied to provide the computer with the ability to generate conclusions based on previously established experience and definitions. In this sense, Inductive Logic Programming presents itself as a research field that incorporates concepts of Logic Programming and Machine Learning, its declarative character allows musical knowledge to be presented to non-specialist users in a naturally understandable way. The present work performs a systematic review based on approaches that use Inductive Logic Programming in the representation of musical knowledge. Questions that these studies seek to address were raised, as well as identifying characteristic aspects related to their application.

scholarly journals Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning

2010 ◽  
Vol 83 (2) ◽  
pp. 133-135
Author(s):  
Hendrik Blockeel ◽  
Karsten Borgwardt ◽  
Luc De Raedt ◽  
Pedro Domingos ◽  
Kristian Kersting ◽  
...  
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Guest Editorial ◽  
Relational Learning ◽  
Inductive Logic ◽  
Statistical Relational Learning ◽  
Special Issue
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