MATEC Web Conf.
Volume 210, 201822nd International Conference on Circuits, Systems, Communications and Computers (CSCC 2018)
|Number of page(s)||9|
|Published online||05 October 2018|
Hidden information retrieval and evaluation method and tools utilising ontology reasoning applied for financial fraud analysis.
Military University of Technology, Cybernetics Faculty, gen. W. Urbanowicz Street 2, Warsaw, Poland
The paper summarizes a semantic association evaluation and reasoning method, utilising domain and problem solving ontologies. The method combines algorithms for data aggregation and logic reasoning utilising concrete financial data. As an outcome method supports suspicious behaviour recognition of money loundering schemes. These scenarios and schemes are implemented by the analysts using ontology-based constructs. Provided tools cover all stages of data processing starting from structural data extraction and migration, aggregation to reasoning using logic (DL and FOL) constructs. Advances in automatic reasoning and the availability of semantic processing tools encourage analysts to extend existing link analysis methods towards contextual data processing. To demonstrate presented method, a proof of concept environment IAFEC Ontology Toolkit has been described. It delivers initial financial fraud identification schemes (rules) based on set of problem solving ontologies. The novelty in such approach comes from incorporating heterogeneous types of data, which usually are processed by graph methods. The semantic tool, extend capabilities of graph-based (homogeneous) approach by delivering context-aware indirect association identification, and inference path explanation and inspection capabilities. Presented material describes the method and analytical algorithms, which demonstrate description logic reasoning and graph-based semantic association identification and ranking. Developed method has been implemented, as a Protégé OWL 5.0 environment extension, supplemented with web-services delivering distributed data processing, aggregation (not available in ontology languages). The environment provides declarative processing capabilities enabling analysts to design configurable processing flow, and new financial fraud identification schemes.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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