Enhancing semantic consistency in anti-fraud rule-based expert systems

In this study, an ontology-driven approach is proposed for semantic conflict detection and classification inrule-based expert systems. It focuses on the critical case of anti-fraud rule repositories for the inspectionof Card Not Present (CNP) transactions in e-commerce environments. The main motivat...

Descripción completa

Detalles Bibliográficos
Autores: Roldán García, María del Mar, García Nieto, José Manuel, Aldana Montes, José F.
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2017
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/108686
Acceso en línea:https://hdl.handle.net/11441/108686
https://doi.org/10.1016/j.eswa.2017.08.036
Access Level:acceso abierto
Palabra clave:Semantic model
Ontology reasoning
Rule-based expert system
Fraud detection expert systems
id ES_effa33771c8a8c8294a79a7d3f842d0d
oai_identifier_str oai:idus.us.es:11441/108686
network_acronym_str ES
network_name_str España
repository_id_str
spelling Enhancing semantic consistency in anti-fraud rule-based expert systemsRoldán García, María del MarGarcía Nieto, José ManuelAldana Montes, José F.Semantic modelOntology reasoningRule-based expert systemFraud detection expert systemsIn this study, an ontology-driven approach is proposed for semantic conflict detection and classification inrule-based expert systems. It focuses on the critical case of anti-fraud rule repositories for the inspectionof Card Not Present (CNP) transactions in e-commerce environments. The main motivation is to examineand curate anti-fraud rule datasets to avoid semantic conflicts that could lead the underpinning expertsystem to incorrectly perform, e. g., by accepting fraudulent transactions and/or by discarding harmlessones. The proposed approach is based on Web Ontology Language (OWL) and Semantic Web Rule Lan- guage (SWRL) technologies to develop an anti-fraud rule ontology and reasoning tasks, respectively. Thethree main contributions of this work are: first, the creation of a conceptual knowledge model for de- scribing anti-fraud rules and their relationships; second, the development of semantic rules as conflict- resolution methods for anti-fraud expert systems; third, experimental facts are gathered to evaluate andvalidate the proposed model. A real-world use case in the e-commerce (e-Tourism) industry is used toexplain the ontological knowledge design and its use. The experiments show that ontological approachescan effectively discover and classify conflicts in rule-based expert systems in the field of anti-fraud ap- plications. The proposal is also applicable to other domains where knowledge rule bases are involved.European Union FP7 EU project SME-Ecompass No: 315637Ministerio de Economía y Competitividad TIN2014-58304Junta de Andalucía P11-TIC-7529Junta de Andalucía P12-TIC-1519ElsevierCiencias de la Computación e Inteligencia ArtificialEuropean Union (UE)Ministerio de Economía y Competitividad (MINECO). EspañaJunta de Andalucía2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/108686https://doi.org/10.1016/j.eswa.2017.08.036reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésExpert Systems with Applications, 90 (December 2017), 332-343.FP7 EU project SME-Ecompass No: 315637TIN2014-58304P11-TIC-7529P12-TIC-1519https://www.sciencedirect.com/science/article/pii/S0957417417305821info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1086862026-06-17T12:51:07Z
dc.title.none.fl_str_mv Enhancing semantic consistency in anti-fraud rule-based expert systems
title Enhancing semantic consistency in anti-fraud rule-based expert systems
spellingShingle Enhancing semantic consistency in anti-fraud rule-based expert systems
Roldán García, María del Mar
Semantic model
Ontology reasoning
Rule-based expert system
Fraud detection expert systems
title_short Enhancing semantic consistency in anti-fraud rule-based expert systems
title_full Enhancing semantic consistency in anti-fraud rule-based expert systems
title_fullStr Enhancing semantic consistency in anti-fraud rule-based expert systems
title_full_unstemmed Enhancing semantic consistency in anti-fraud rule-based expert systems
title_sort Enhancing semantic consistency in anti-fraud rule-based expert systems
dc.creator.none.fl_str_mv Roldán García, María del Mar
García Nieto, José Manuel
Aldana Montes, José F.
author Roldán García, María del Mar
author_facet Roldán García, María del Mar
García Nieto, José Manuel
Aldana Montes, José F.
author_role author
author2 García Nieto, José Manuel
Aldana Montes, José F.
author2_role author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
European Union (UE)
Ministerio de Economía y Competitividad (MINECO). España
Junta de Andalucía
dc.subject.none.fl_str_mv Semantic model
Ontology reasoning
Rule-based expert system
Fraud detection expert systems
topic Semantic model
Ontology reasoning
Rule-based expert system
Fraud detection expert systems
description In this study, an ontology-driven approach is proposed for semantic conflict detection and classification inrule-based expert systems. It focuses on the critical case of anti-fraud rule repositories for the inspectionof Card Not Present (CNP) transactions in e-commerce environments. The main motivation is to examineand curate anti-fraud rule datasets to avoid semantic conflicts that could lead the underpinning expertsystem to incorrectly perform, e. g., by accepting fraudulent transactions and/or by discarding harmlessones. The proposed approach is based on Web Ontology Language (OWL) and Semantic Web Rule Lan- guage (SWRL) technologies to develop an anti-fraud rule ontology and reasoning tasks, respectively. Thethree main contributions of this work are: first, the creation of a conceptual knowledge model for de- scribing anti-fraud rules and their relationships; second, the development of semantic rules as conflict- resolution methods for anti-fraud expert systems; third, experimental facts are gathered to evaluate andvalidate the proposed model. A real-world use case in the e-commerce (e-Tourism) industry is used toexplain the ontological knowledge design and its use. The experiments show that ontological approachescan effectively discover and classify conflicts in rule-based expert systems in the field of anti-fraud ap- plications. The proposal is also applicable to other domains where knowledge rule bases are involved.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/108686
https://doi.org/10.1016/j.eswa.2017.08.036
url https://hdl.handle.net/11441/108686
https://doi.org/10.1016/j.eswa.2017.08.036
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Expert Systems with Applications, 90 (December 2017), 332-343.
FP7 EU project SME-Ecompass No: 315637
TIN2014-58304
P11-TIC-7529
P12-TIC-1519
https://www.sciencedirect.com/science/article/pii/S0957417417305821
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 Elsevier
publisher.none.fl_str_mv Elsevier
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
_version_ 1869423913833857024
score 15,300724

Similares en LA Referencia