Abstract
Our learning system supports repeatable experiments in which practical problems related to document indexing and retrieval are addressed. Knowledge bases are typically critical to the document indexing and retrieval. In our experiments, one knowledge base is augmented with knowledge from another knowledge base, and at the first level this augmentation is an instance of similarity-based or analogy-based learning. But the overall system is tuned by interaction with a person so that changes in the representation of the knowledge base and in the performance and evaluation components of the learning system can lead to demonstrably improved performance. We provide evidence that small changes in structure or representation should correspond to small changes in function or meaning. Furthermore, all the components of the learning system depend in some way on detecting similarities and exploiting differences, and the move from similarity to analogy in learning exploits a second-order difference.
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© 1989 Springer-Verlag Berlin Heidelberg
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Rada, R., Mili, H. (1989). A knowledge-intensive learning system for document retrieval. In: Morik, K. (eds) Knowledge Representation and Organization in Machine Learning. Lecture Notes in Computer Science, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017217
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DOI: https://doi.org/10.1007/BFb0017217
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