AI 2012: AI 2012: Advances in Artificial Intelligence pp 890-901 | Cite as
Probabilistic Model-Based Assessment of Information Quality in Uncertain Domains
Abstract
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.
Keywords
Actual World Information Quality User Query Actual Soundness Bulk CarrierPreview
Unable to display preview. Download preview PDF.
References
- 1.Laskey, K.B.: MEBN: A language for first-order bayesian knowledge bases. Artif. Intell. 172(2-3), 140–178 (2008)MathSciNetMATHCrossRefGoogle Scholar
- 2.Motro, A., Rakov, I.: Estimating the Quality of Databases. In: Andreasen, T., Christiansen, H., Larsen, H.L. (eds.) FQAS 1998. LNCS (LNAI), vol. 1495, pp. 298–307. Springer, Heidelberg (1998)CrossRefGoogle Scholar
- 3.Naumann, F., Leser, U., Freytag, J.C.: Quality-driven integration of heterogenous information systems. In: Proc. of the 25th Int. Conf. on VLDB, pp. 447–458 (1999)Google Scholar
- 4.Nilsson, N.J.: Probabilistic logic. Artif. Intell. 28(1), 71–87 (1986)MathSciNetMATHCrossRefGoogle Scholar
- 5.Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann (1988)Google Scholar
- 6.Peim, M., Franconi, E., Paton, N.W.: Estimating the quality of answers when querying over description logic ontologies. Data & Knowl. Eng. 47(1), 105–129 (2003)CrossRefGoogle Scholar
- 7.Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Communications of the ACM 45(4), 211–218 (2002)CrossRefGoogle Scholar
- 8.Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill (1983)Google Scholar