Integrating Knowledge Engineering with Knowledge Discovery in Database: TOM4D and TOM4L

A Knowledge Based System (KBS) carries out a set of knowledge intensive tasks for the purpose of putting in practice problem-solving capabilities, comparable to those of a domain expert, from an input data flow produced by a process. In particular, a knowledge intensive task requires, by construction, a Knowledge Model in order to interpret the input data flow according to the task to be achieved, to identify an eventual problem to be solved and to produce a solution to this one. The Knowledge Engineering (KE) discipline provides methods, techniques and tools which facilitate and improve the modelling task of expert knowledge. In this field of study, most approaches model separately expert knowledge regarding the expert's reasoning mechanisms from expert knowledge specific to the domain of interest. Thus, a model of the expert's knowledge, called Expert Model (or Knowledge Model), obtained through this discipline will be generally made up of a model describing how the expert reasons about the process (a conceptual model of the expert's reasoning tasks) and of a representation of the knowledge used in the involved reasoning (a conceptual model of the domain knowledge). This latter is derived from the Process Model utilized by the expert in order to formulate his own knowledge. Knowledge Engineering allows then to establish a back and forth way between the expert's knowledge and the built Expert Model where the validity of this latter can be evaluated. However, two of the main drawbacks with the KE approaches are (i) the cost of knowledge acquisition and modelling process, which is too long for economic domains that use technologies with short life cycles and (ii) the validation of the Expert Model which is mainly oriented to " case-based ". An interesting alternative to deal with these problems is to resort to the process of Knowledge Discovery in Database (KDD) which uses Data Mining techniques in order to obtain knowledge from data. In this approach, the process data flow is recorded by a program in a database where the data contained in such a database are analysed by means of Data Mining techniques in a KDD process with the purpose of discovering``patterns'' of data. An n-ary relation among data can be con