Artículo
Enhancing semantic consistency in anti-fraud rule-based expert systems
Autor/es | Roldán García, María del Mar
García Nieto, José Manuel Aldana Montes, José F. |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2017 |
Fecha de depósito | 2021-05-07 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | European Union (UE) Ministerio de Economía y Competitividad (MINECO). España Junta de Andalucía |
Identificador del proyecto | FP7 EU project SME-Ecompass No: 315637
TIN2014-58304 P11-TIC-7529 P12-TIC-1519 |
Cita | Roldán García, M.d.M., García Nieto, J.M. y Aldana Montes, J.F. (2017). Enhancing semantic consistency in anti-fraud rule-based expert systems. Expert Systems with Applications, 90 (December 2017), 332-343. |
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