A Visualized Framework for Representing Uncertain and Incomplete Temporal Knowledge
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A Visualized Framework for Representing Uncertain and Incomplete Temporal Knowledge

Authors: Yue Wang, Jixin Ma, Brian Knight

Abstract:

This paper presents a visualized computer aided case tool for non-expert, called Visual Time, for representing and reasoning about incomplete and uncertain temporal information. It is both expressive and versatile, allowing logical conjunctions and disjunctions of both absolute and relative temporal relations, such as “Before”, “Meets”, “Overlaps”, “Starts”, “During”, and “Finishes”, etc. In terms of a visualized framework, Visual Time provides a user-friendly environment for describing scenarios with rich temporal structure in natural language, which can be formatted as structured temporal phrases and modeled in terms of Temporal Relationship Diagrams (TRD). A TRD can be automatically and visually transformed into a corresponding Time Graph, supported by automatic consistency checker that derives a verdict to confirm if a given scenario is temporally consistent or inconsistent.

Keywords: Time Visualization, Uncertainty, Incompleteness, Consistency Checking.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1088890

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[1] Tomasz Imielinski, WitoldLipski. Incomplete Information in Relational Databases.Journal of the ACM,1994, 31(4),pp. 761-791.
[2] Todd Mansell. A method for planning given uncertain and incomplete information, In Proceedings of the Ninth international conference on Uncertainty in artificial intelligence,1993, pp. 350-358.
[3] Mohamed Soliman, IhabIlyas, Shalev Ben-David. Supporting ranking queries on uncertain and incomplete data. The International Journal on Very Large Data Bases Archive. 2010, 19(4), pp. 477-501.
[4] Jianbing Ma, Weiru Liu and Paul Miller. Event modelling and reasoning with uncertain information for distributed sensor networks, In Proceedings of the 4th international conference on Scalable uncertainty management,2010, pp.236-249.
[5] James Allen. Maintaining knowledge about temporal intervals. Communications of the ACM, 1983, 26(11),pp. 832-843.
[6] James Allen. Towards a General Theory of Action and Time. Artificial Intelligence, 1984, 23, pp. 123-154.
[7] J. van Benthem, The Logic of Time. Kluwer Academic, Dordrech,1983.
[8] Brian Knight and Jixin Ma. Representing the Dividing Instant, the Computer Journal, 2003.46(2),pp.213-222.
[9] A. Galton. Critical Examination of Allen's Theory of Action and Time, Artificial Intelligence, 1990, 42,pp.159-188.
[10] J. Jensen, J. Clifford, S. Gadia, A. Segev and R. Snodgrass, A Glossary of Temporal Database Concepts. SIGMOD RECORD, 1992, 21(3),pp.35-43.
[11] James Allen and Pat Hayes, Moments and Points in an Interval-based Temporal-based Logic, Computational Intelligence, 1989, 5,pp. 225-238.
[12] J. McCarthy and Hayes. Some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B. And Michie, D. (eds), Machine Intelligence, 4:463-502. Edinburgh University Press, 1969, Edinburgh.
[13] B. Bruce. A model for temporal references and application in a question answering program. Artificial Intelligence,1972,3, pp.1-25.
[14] Jixin Ma and Brian Knight, A General Temporal Theory. The Computer Journal, 1994, 37(2):114-123.