Previous provenance models have assumed that there is complete certainty in the provenance relationships. But what if this assumption does not hold? In this work, we propose a probabilistic provenance graph (PPG) model to characterize scenarios where provenance relationships are uncertain. We describe two motivating examples. The first example demonstrates the uncertainty associated with the provenance of an email. The second example demonstrates and characterizes the uncertainty associated with the provenance of statements in documents.

Abstract-To go beyond what current provenance systems can capture for natural language text documents, we propose the Lincoln Laboratory Plagiarism for Provenance System (LLPlā) as an approach for capturing linguistic provenance. Linguistic provenance infers the origin of text based on its linguistic structure. We take a plagiarism detection approach to this task as identifying similar sections of text is fundamental to linguistic provenance and central to LLPlā's performance. Thus, to determine the most viable plagiarism detection algorithm for use in LLPlā, we evaluate three state-of-theart plagiarism detection algorithms. Moreover, we propose extensions to the best-performing algorithm that improve its precision with negligible effects on recall.