AFRA.owl an ontology-driven approach is proposed for semantic conflict detection and classification in rule-based expert systems. It focuses on the critical case of anti-fraud rule repositories for the inspection of Card Not Present (CNP) transactions in e-commerce environments. The main motivation is to examine and curate anti-fraud rule datasets to avoid semantic conflicts that could lead the underpinning expert system to incorrectly perform, e. g., by accepting fraudulent transactions and/or by discarding harmless ones. The proposed approach is based on Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) technologies to develop an anti-fraud rule ontology and reasoning tasks, respectively. The three main contributions of this work are: first, the creation of a conceptual knowledge model for describing 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 and validate the proposed model. A real-world use case in the e-commerce (e-Tourism) industry is used to explain the ontological knowledge design and its use.
AFRA.owl contains the following features:
- A semantic approach to represent and consolidate anti-fraud rules is proposed.
- An OWL Ontology and SWRL rules are developed for reasoning tasks in anti-fraud.
- The proposal is validated with a real-world knowledge rule base of e-Tourism.
- Obtained semantized data successfully detect inconsistencies in anti-fraud rules.
- We provide actual e-merchants with tools to enhance their commercial activities.