An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations

Daga, Enrico; d’Aquin, Mathieu; Gangemi, Aldo and Motta, Enrico (2016). An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations. In: Knowledge Engineering and Knowledge Management (Blomqvist, Eva; Ciancarini, Paolo; Poggi, Francesco and Vitali, Fabio eds.), Lecture Notes in Computer Science (LNCS), Springer, pp. 129–144.

DOI: https://doi.org/10.1007/978-3-319-49004-5_9

Abstract

Workflow formalisations are often focused on the representation of a process with the primary objective to support execution. However, there are scenarios where what needs to be represented is the effect of the process on the data artefacts involved, for example when reasoning over the corresponding data policies. This can be achieved by annotating the workflow with the semantic relations that occur between these data artefacts. However, manually producing such annotations is difficult and time consuming. In this paper we introduce a method based on recommendations to support users in this task. Our approach is centred on an incremental rule association mining technique that allows to compensate the cold start problem due to the lack of a training set of annotated workflows. We discuss the implementation of a tool relying on this approach and how its application on an existing repository of workflows effectively enable the generation of such annotations.

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