A knowledge based system for the management of a time stamped uncertain observation set with application on preserving mobility

• Published in: International journal of approximate reasoning Vol. 134; pp. 53 - 71
• Main Authors: Delcroix, Véronique, Grislin-Le Strugeon, Emmanuelle, Puisieux, François
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2021; Elsevier
• Subjects: Computer Science; Information aging; Information quality; Knowledge based system; Probabilistic graphical model; Reasoning with uncertainty; Uncertain information; Uncertain information; Probabilistic graphical model; Information aging; Knowledge based system; Reasoning with uncertainty; Information quality
• ISSN: 0888-613X; 1873-4731
• DOI: 10.1016/j.ijar.2021.04.003

The aim of this study is to maintain up-to-date information about the current state of elderly people that are medically followed for risks of fall. Our proposal consists of an individual information database management system that can provide information on-demand on various variables. Such a system has to deal with several sources of uncertainty: lack of information, evolving information and reliability of the information sources. We consider that the features of the person may evolve with time causing uncertainty due to obsolete information. Our context includes new information received bit by bit, with no possibility to collect all required information at once. This paper establishes a first proposal to manage a set of uncertain observations, in order to reduce erroneous and obsolete information while keeping the benefit of previously collected information. We propose an architecture of the system based on a probabilistic knowledge model about the characteristics of interest, a set of decay functions that help to evaluate the confidence degree in previous observations, and a reasoning module to manage new observations, maintain the compatibility and the quality of the observation set. We detail the algorithms of the reasoning module, and the algorithm to update the confidence degree of the observations.