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