Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning

Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern...

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Detalles Bibliográficos
Autores: Almagro Blanco, Pedro, Sancho Caparrini, Fernando, Borrego Díaz, Joaquín
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/170870
Acceso en línea:https://hdl.handle.net/11441/170870
https://doi.org/10.3390/math11224672
Access Level:acceso abierto
Palabra clave:Graph pattern matching
Graph query
Node classification
Relational machine learning
Subgraph classification
Symbolic artificial intelligence
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spelling Logical–Mathematical Foundations of a Graph Query Framework for Relational LearningAlmagro Blanco, PedroSancho Caparrini, FernandoBorrego Díaz, JoaquínGraph pattern matchingGraph queryNode classificationRelational machine learningSubgraph classificationSymbolic artificial intelligenceRelational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern-based approach. However, this type of learning model suffers from two fundamental problems: the computational complexity arising from relational queries and the lack of a robust and general framework to serve as the basis for relational learning methods. In this paper, we propose an efficient graph query framework that allows for cyclic queries in polynomial time and is ready to be used in pattern-based learning methods. This solution uses logical predicates instead of graph isomorphisms for query evaluation, reducing complexity and allowing for query refinement through atomic operations. The main differences between our method and other previous pattern-based graph query approaches are the ability to evaluate arbitrary subgraphs instead of nodes or complete graphs, the fact that it is based on mathematical formalization that allows the study of refinements and their complementarity, and the ability to detect cyclic patterns in polynomial time. Application examples show that the proposed framework allows learning relational classifiers to be efficient in generating data with high expressiveness capacities. Specifically, relational decision trees are learned from sets of tagged subnetworks that provide both classifiers and characteristic patterns for the identified classes.MDPICiencias de la Computación e Inteligencia ArtificialMCIN/AEI/10.13039/5011000110332023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/170870https://doi.org/10.3390/math11224672reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésMathematics, 11 (22), 4672.PID2019-109152Ghttps://www.mdpi.com/2227-7390/11/22/4672info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1708702026-06-17T12:51:07Z
dc.title.none.fl_str_mv Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
title Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
spellingShingle Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
Almagro Blanco, Pedro
Graph pattern matching
Graph query
Node classification
Relational machine learning
Subgraph classification
Symbolic artificial intelligence
title_short Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
title_full Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
title_fullStr Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
title_full_unstemmed Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
title_sort Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
dc.creator.none.fl_str_mv Almagro Blanco, Pedro
Sancho Caparrini, Fernando
Borrego Díaz, Joaquín
author Almagro Blanco, Pedro
author_facet Almagro Blanco, Pedro
Sancho Caparrini, Fernando
Borrego Díaz, Joaquín
author_role author
author2 Sancho Caparrini, Fernando
Borrego Díaz, Joaquín
author2_role author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
MCIN/AEI/10.13039/501100011033
dc.subject.none.fl_str_mv Graph pattern matching
Graph query
Node classification
Relational machine learning
Subgraph classification
Symbolic artificial intelligence
topic Graph pattern matching
Graph query
Node classification
Relational machine learning
Subgraph classification
Symbolic artificial intelligence
description Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern-based approach. However, this type of learning model suffers from two fundamental problems: the computational complexity arising from relational queries and the lack of a robust and general framework to serve as the basis for relational learning methods. In this paper, we propose an efficient graph query framework that allows for cyclic queries in polynomial time and is ready to be used in pattern-based learning methods. This solution uses logical predicates instead of graph isomorphisms for query evaluation, reducing complexity and allowing for query refinement through atomic operations. The main differences between our method and other previous pattern-based graph query approaches are the ability to evaluate arbitrary subgraphs instead of nodes or complete graphs, the fact that it is based on mathematical formalization that allows the study of refinements and their complementarity, and the ability to detect cyclic patterns in polynomial time. Application examples show that the proposed framework allows learning relational classifiers to be efficient in generating data with high expressiveness capacities. Specifically, relational decision trees are learned from sets of tagged subnetworks that provide both classifiers and characteristic patterns for the identified classes.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/170870
https://doi.org/10.3390/math11224672
url https://hdl.handle.net/11441/170870
https://doi.org/10.3390/math11224672
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Mathematics, 11 (22), 4672.
PID2019-109152G
https://www.mdpi.com/2227-7390/11/22/4672
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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