Abstract
In this paper we explore the relationships between symbolic and connectionist models of medical knowledge in the diagnosis task. First we reassume the motivations of the ground-work stages of connectionism. Then a relational network is obtained from the natural language description of the diagnosis task and subsequently this network is transformed into a connectionist one via the dual graph. Finally we comment on the symbiosis between symbolic and neural computation. The aim of the paper is to explore some of the similarities and differences between the two basic approach to artificial intelligence. © Springer-Verlag Berlin Heidelberg 2001.
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CITATION STYLE
Mira, J., Martínez, R., Álvarez, J. R., & Delgado, A. E. (2001). DIAGEN-WebDB: A connectionist approach to medical knowledge representation and inference. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2084 LNCS, pp. 772–782). Springer Verlag. https://doi.org/10.1007/3-540-45720-8_93
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