Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Communications of the ACM - Supporting community and building social capital
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Quality-driven Integration of Heterogenous Information Systems
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Estimating the Quality of Databases
FQAS '98 Proceedings of the Third International Conference on Flexible Query Answering Systems
Estimating the quality of answers when querying over description logic ontologies
Data & Knowledge Engineering
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
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In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the information they are based on. In this paper, we propose a novel method for assessing the quality of information taking into account uncertainty. Two properties --- soundness and completeness --- of the information are used to define the notion of information quality and their expected values are defined using a probabilistic model output. Simulation experiments with data from a maritime scenario demonstrates the usage of the proposed method and its potential for decision support in complex tasks such as surveillance.