Document - How Much Knowledge is in a Knowledge Base? Introducing Knowledge Measures (Preliminary Report)

2020

Conference article Open Access

How Much Knowledge is in a Knowledge Base? Introducing Knowledge Measures (Preliminary Report)

Straccia U.

Knowledge Base Knowledge measure

In this work we address the following question: can we measure how much knowledge a knowledge base represents? We answer to this question (i) by describing properties (axioms) that a knowledge measure we believe should have in measuring the amount of knowledge of a knowledge base (kb); and (ii) provide a concrete example of such a measure, based on the notion of entropy. We also introduce related kb notions such as (i) accuracy; (ii) conciseness; and (iii) Pareto optimality. Informally, they address the following questions: (i) how precise is a kb in describing the actual world? (ii) how succinct is a kb w.r.t. the knowledge it represents? and (iii) can we increase accuracy without decreasing conciseness, or vice-versa?

Source: European Conference on Artificial Intelligence (ECAI-20), pp. 905–912, Santiago de Compostela, SPAIN, 29/08/2020 - 08/09/2020

Publisher: IOS Press, Amsterdam, NLD

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@inproceedings{oai:it.cnr:prodotti:429327,
	title = {How Much Knowledge is in a Knowledge Base? Introducing Knowledge Measures (Preliminary Report)},
	author = {Straccia U.},
	publisher = {IOS Press, Amsterdam, NLD},
	doi = {10.3233/faia200182},
	booktitle = {European Conference on Artificial Intelligence (ECAI-20), pp. 905–912, Santiago de Compostela, SPAIN, 29/08/2020 - 08/09/2020},
	year = {2020}
}