title: Automatic generation of a metamodel from an existing knowledge base to assist the development of a new analogous knowledge base.
abstract: Knowledge acquisition is a key step in the development of knowledge-based systems and methods have been proposed to help elicitating a domain-specific task model from a generic task model. We explored how an existing validated knowledge base (KB) represented by a decision tree could be automatically processed to infer a higher level domain-specific task model. On-codoc is a guideline-based decision support system applied to breast cancer therapy. Assuming task identity and ontological proximity between breast and lung cancer domains, the generalization of the breast can-cer KB should allow to build a metamodel to serve as a guide for the elaboration of a new specific KB on lung cancer. Two types of parametrized generalization methods based on tree structure simplification and ontological abstraction were used. We defined a similarity distance and a generalization coefficient to select the best metamodel identified as the closest to the original decision tree of the most generalized metamodels.
authors.affiliation: Service d'Informatique Medicale/AP-HP, Paris, Cedex 13, France.
authors.forename: J
authors.initials: J
authors.lastname: Bouaud
authors.forename: B
authors.initials: B
authors.lastname: Séroussi
mesh_terms: D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000628:therapy*; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D006801:Humans; D008175:Lung Neoplasms / Q000628:therapy; D017410:Practice Guidelines as Topic; D013813:Therapy, Computer-Assisted