A machine learning tool designed for a model-based knowledge acquisition approach

SEWEBAR-CMS: semantic analytical report authoring for data mining results

“…First attempts to exploit prior conceptual 7 knowledge in propositional 8 machine learning (as research field predating present-day mainstream KDD) were often restricted to intra-attribute value (typically, taxonomical) structuring [5,8,31,43]. More sophisticated and abstract knowledge models were however sometimes also used to constrain the search and structure the learning workflow; examples are qualitative models by Clark & Matwin [14] or problem-solving methods [17,46]. This effort naturally intensified with the rise of semantic web technologies, providing standard, web-oriented languages and reasoning tools for ontological knowledge (in particular, in OWL [1] and Topic Maps [19]).…”

Ontology-Enhanced Association Mining

“…Although not explicitly talking about ontologies, the work by Clark & Matwin [8] is also relevant; they used qualitative models as bias for inductive learning. Finally, Thomas et al [21] and van Dompseler & van Someren [22] used problem-solving method descriptions (a kind of 'method ontologies') for the same purpose. There have also been several efforts to employ taxonomies over domains of individual attributes [3,4,13,19] to guide inductive learning.…”

Knowledge-based approach to domain modeling: organizational process modeling application

“…Case-based learning has been used [18] for solving problems by generating and transforming solutions of similar previously encountered ones. The ENIGME system [19] is a machine learning system that learns operative domain knowledge by exploiting a model of expertise defined in the KADS methodology. Liang et al [20] have developed a novel approach that integrates computer simulation, semiMarkov decision processes, and artificial neural networks for automated acquisition of real-time scheduling knowledge.…”