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Article type: Research Article
Authors: Masolo, Claudioa; * | Botti Benevides, Alessanderb | Porello, Danielec
Affiliations: [a] Laboratory for Applied Ontology, ISTC-CNR, Italy. E-mail: masolo@loa.istc.cnr.it | [b] NEMO, Computer Science Department, Federal University of Espírito Santo, Brazil. E-mail: abbenevides@inf.ufes.br | [c] Free University of Bozen/Bolzano, Italy. E-mail: danieleporello@gmail.com
Correspondence: [*] Corresponding author. E-mail: masolo@loa.istc.cnr.it.
Note: [] Accepted by: Roberta Ferrario
Abstract: We propose a formal framework to examine the relationship between models and observations. To make our analysis precise, models are reduced to first-order theories that represent both terminological knowledge – e.g., the laws that are supposed to regulate the domain under analysis and that allow for explanations, predictions, and simulations – and assertional knowledge – e.g., information about specific entities in the domain of interest. Observations are introduced into the domain of quantification of a distinct first-order theory that describes their nature and their organization and takes track of the way they are experimentally acquired or intentionally elaborated. A model mainly represents the theoretical knowledge or hypotheses on a domain, while the theory of observations mainly represents the empirical knowledge and the given experimental practices. We propose a precise identity criterion for observations and we explore different links between models and observations by assuming a degree of independence between them. By exploiting some techniques developed in the field of social choice theory and judgment aggregation, we sketch some strategies to solve inconsistencies between a given set of observations and the assumed theoretical hypotheses. The solutions of these inconsistencies can impact both the observations – e.g., the theoretical knowledge and the analysis of the way observations are collected or produced may highlight some unreliable sources – and the models – e.g., empirical evidences may invalidate some theoretical laws.
Keywords: Ontology, epistemology, scientific theories, observations, provenance, data aggregation
DOI: 10.3233/AO-180193
Journal: Applied Ontology, vol. 13, no. 1, pp. 41-71, 2018
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