The bulk of research in these fields concentrates on inducing models from data archived in computer databases. However, for many problem domains, human expertise forms an essential part of the corpus of knowledge needed to construct models of the domain. The discipline of knowledge engineering has focused on encoding the knowledge of experts in a form that can be encoded into computational models of a domain. At present, knowledge engineering and machine learning remain largely separate disciplines. Yet in many fields of endeavor, substantial human expertise exists alongside data archives.
Emphasis is on sound theoretical frameworks rather than ad hoc approaches.
Of particular interest are papers that combine clear theoretical discussion with practical examples, and papers that compare different approaches. Manuscripts must not exceed 30 pages and must conform to the Machine Learning guidelines and style . Example papers can be downloaded from the publishers. The first author will be the primary contact unless otherwise stated. Papers must be submitted electronically.