Abstract
We introduce a model of uncertainty where documents are not uniquely identified in a reference network, and some links may be incorrect. It generalizes the probabilistic approach on databases to graphs, and defines subgraphs with a probability distribution. The answer to a relational query is a distribution of documents, and we study how to approximate the ranking of the most likely documents and quantify the quality of the approximation. The answer to a function query is a distribution of values and we consider the size of the interval of Minimum and Maximum values as a measure for the precision of the answer1. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Hess, C., & De Rougemont, M. (2007). A model of uncertainty for near-duplicates in document reference networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4675 LNCS, pp. 449–453). Springer Verlag. https://doi.org/10.1007/978-3-540-74851-9_40
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