Interval rough mereology for approximating hierarchical knowledge

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Abstract

The paper proposes an approach based on Rough Mereology to approximating hierarchical relationships between imprecise concepts in knowledge representation systems. The approach employs Interval Analysis to capture the imprecision caused by the granularity of knowledge. Interval rough inclusion functions are defined. It is demonstrated that they can be effectively used to compute the IS-A relationships by measuring the inclusion of one approximated concept into another. It is shown that the functions are superior to the previously suggested in literature. © Springer-Verlag Berlin Heidelberg 2007.

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APA

Klinov, P., & Mazlack, L. J. (2007). Interval rough mereology for approximating hierarchical knowledge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4482 LNAI, pp. 557–564). Springer Verlag. https://doi.org/10.1007/978-3-540-72530-5_67

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