Reviewing the Qualifiers of Imperfection in Geographic Information - Archive ouverte HAL Access content directly
Book Sections Year : 2019

Reviewing the Qualifiers of Imperfection in Geographic Information

(1) , (2)
1
2

Abstract

This chapter argues that the qualifiers of imperfection in geographic information can be reviewed in the highly formalized framework of AGM belief revision in knowledge engineering. It tackles the belief revision methods used for imperfect information, especially Bayesian revision (Bayes' theorem and Jeffrey's rule) and the alternatives in non‐probabilistic formalisms (Dempster's rule of combination in evidence theory, possibilistic conditioning in possibility theory). The chapter shows how the theories about the imperfect representation of spatial objects can be implemented from an operational point of view, to solve the issue of belief revision on available information. It also shows how revision operations may create imperfections in the set of beliefs. Afterward, the chapter considers the more general case in which clauses use from the very beginning formalisms employed for uncertain knowledge, and how revision operations may take advantage of these formalisms to reach revised and consistent states of uncertain beliefs.
Not file

Dates and versions

hal-02376225 , version 1 (22-11-2019)

Identifiers

Cite

Giovanni Fusco, Andrea G. B. Tettamanzi. Reviewing the Qualifiers of Imperfection in Geographic Information. Mireille Batton-Hubert; Eric Desjardin; François Pinet. Geographic Data Imperfection 1: From theory to applications, 1, Wiley, 2019, 9781786302977. ⟨10.1002/9781119507284.ch9⟩. ⟨hal-02376225⟩
68 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More