Rule-based query answering method for a knowledge base of economic crimes

TL;DR

This paper introduces a rule-based query answering approach for economic crime data, utilizing ontological knowledge and two reasoning methods, demonstrated through a prototype system tested on crime-related datasets.

Contribution

It proposes a novel rule-based query answering framework with hybrid and forward chaining reasoning methods for economic crime knowledge bases.

Findings

System successfully answers queries on economic crime data

Hybrid reasoning improves query accuracy and efficiency

Prototype demonstrates practical applicability in economic crime analysis

Abstract

We present a description of the PhD thesis which aims to propose a rule-based query answering method for relational data. In this approach we use an additional knowledge which is represented as a set of rules and describes the source data at concept (ontological) level. Queries are posed in the terms of abstract level. We present two methods. The first one uses hybrid reasoning and the second one exploits only forward chaining. These two methods are demonstrated by the prototypical implementation of the system coupled with the Jess engine. Tests are performed on the knowledge base of the selected economic crimes: fraudulent disbursement and money laundering.