Section: Research Program

From KDD to KDDK

Keywords:

knowledge discovery in databases, knowledge discovery in databases guided by domain knowledge, data mining

Knowledge discovery in databases is a process for extracting from large databases knowledge units that can be interpreted and reused. From an operational point of view, a KDD system includes databases, data mining modules, and interfaces for interactions, e.g. editing and visualization. The KDD process is based on three main operations: selection and preparation of the data, data mining, and finally interpretation of the extracted units.

The process of “knowledge discovery in databases guided by domain knowledge” extends the KDD cycle with a fourth step, where extracted units are represented within a knowledge base to be reused. The KDDK process –as implemented in the research work of the Orpailleur team– is based on data mining methods that are either symbolic or numerical:

• Symbolic methods are based on frequent itemsets search, association rule extraction, Formal Concept Analysis and extensions [113] .

• Numerical methods are based on higher order stochastic models, namely second-order Hidden Markov Models (HMM2) and Hidden Markov fields (HMRF), which are especially designed for an efficient modeling of space and time [12] .

The principle summarizing KDDK can be understood as a process going from complex data to knowledge units being guided by domain knowledge. Two original aspects can be underlined: (i) the knowledge discovery process is guided by domain knowledge at each step of the process, and (ii) the extracted units are embedded within knowledge-based systems for problem solving purposes.

One main operation in the research work of Orpailleur on KDDK is classification, which is a polymorphic process involved in modeling, mining, representing, and reasoning tasks. Moreover, the KDDK process is intended to feed knowledge-based systems working in application domains, e.g. agronomy, biology, chemistry, cooking and medicine, and also in the context of semantic web, text mining, information retrieval, and ontology engineering.