Exposé: An ontology for data mining experiments
Joaquin Vanschoren, Larisa Soldatova · Lirias · 2010
Research in machine learning and data mining can be speeded up tremendously by moving empirical research results out of people’s heads and labs, onto the network and into tools that help us structure and filter the information. This paper presents Exposé, an ontology to describe machine learning experiments in a standardized fashion and support a collaborative approach to the analysis of learning algorithms. Using a common vocabulary, data mining experiments and details of the used algorithms and datasets can be shared between individual researchers, software agents, and the community at large. It enables open repositories that collect and organize experiments by many researchers. As can been learned from recent developments in other sciences, such a free exchange and reuse of experiments requires a clear representation. We therefore focus on the design of an ontology to express and share experiment meta-data with the world.