Orpailleur: Knowledge Discovery and Knowledge Engineering
The Orpailleur Team is depending both on Inria Nancy Grand Est as a so-called “équipe-projet Inria” and a research team of the LORIA Lab (CNRS — Université de Lorraine).
The Orpailleur Team is mainly interested in Knowledge Discovery in Databases (KDD) and in Knowledge Engineering (KE). KDD consists in processing large volumes of data for discovering patterns that are significant and reusable. Considering patterns as gold nuggets and databases as locations to be explored, KDD can be likened to the process of searching for gold. This explains the name of the research team, as, in French, “orpailleur” denotes a gold miner.
KDD is based on three main operations: data preparation, data mining and interpretation of the extracted patterns. Moreover, KDD is iterative, interactive, and controlled by an analyst (expert of the domain of data). Domain knowledge can be used for improving and guiding the KDD process, leading to Knowledge Discovery guided by Domain Knowledge (KDDK). The discovered patterns can be represented as knowledge units using a knowledge representation formalism and integrated within a knowledge base for problem-solving needs, leading to production of actionable knowledge. In this way, knowledge discovery and knowledge engineering are two complementary processes. The study and implementation of KDD and its variations are guiding research lines within the Orpailleur Team.