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Calculating Salience and Breadth of Knowledge
December 1993 (vol. 5 no. 6)
pp. 996-998

An overview is presented of recently completed research investigating three questions: what does it mean for a computer to know what it knows about? How can a computer construct a representation of what it knows about? How can such a representation be used for practical applications that advance the state-of-the-art in understanding the content of large databases?

[1] L. F. Rau, "A computational approach to meta-knowledge: Calculating breadth and salience," Ph.D. dissertation, Comput. Sci. Dep., Univ. Exeter, Exeter, England, submitted Dec. 1992.
[2] E. E. Smith and D. L. Medin,Categories and Concepts. Cambridge, MA: Harvard University press, 1988.
[3] L. F. Rau, "Calculating breadth of knowledge," inProc. 14th Annu. Conf. Cognitive Sci. Soc. Hillsdale, NJ: Lawrence Erlbaum Associates, 1992.
[4] M. Iwayama, T. Tokunaga, and H. Tanaka, "A method of calculating the measure of salience in understanding metaphors," inProc. Amer. Ass. Artificial Intell. Los Altos, CA: Morgan Kaufmann, 1990, pp. 298-303.
[5] L. F. Rau, "Calculating salience of knowledge," inProc. 14th Annu. Conf. Cognitive Sci. Soc. Hillsdale, NJ: Lawrence Erlbaum Associates, 1992.

Index Terms:
salience; breadth of knowledge; state-of-the-art; large database content; underlying computational model; DBMS; database management systems; knowledge based systems; knowledge representation
Citation:
L.F. Rau, "Calculating Salience and Breadth of Knowledge," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 6, pp. 996-998, Dec. 1993, doi:10.1109/69.250087
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