Section: Research Program

From KDD to KDDK

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

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Knowledge discovery in databases is a process for extracting knowledge units from large databases, units that can be interpreted and reused within knowledge-based systems. From an operational point of view, the KDD process is performed within a KDD system including 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 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, and concept lattice design (Formal Concept Analysis and extensions [95] , [108] ).

• Numerical methods are based on second-order Hidden Markov Models (HMM2, designed for pattern recognition [107] ). Hidden Markov Models have good capabilities for locating stationary segments, and are mainly used for mining temporal and spatial data.

The principle summarizing KDDK can be understood as a process going from complex data units to knowledge units being guided by domain knowledge (KDDK or “knowledge with/for knowledge”) [104] . Two original aspects can be underlined: (i) the KDD process is guided by domain knowledge, and (ii) the extracted units are embedded within a knowledge representation formalism to be reused in a knowledge-based system for problem solving purposes.

The various instantiations of the KDDK process in the research work of Orpailleur are mainly based on classification, considered as a polymorphic process involved in tasks such as modeling, mining, representing, and reasoning. Accordingly, the KDDK process may feed knowledge-based systems to be used for problem-solving activities in application domains, e.g. agronomy, astronomy, biology, chemistry, and medicine, and also for semantic web activities involving text mining, information retrieval, and ontology engineering [97] , [81] .