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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.

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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.

Item Type: Conference or Workshop Item
Copyright Holders: 2016 Springer International Publishing AG
ISBN: 3-319-49003-6, 978-3-319-49003-8
ISSN: 0302-9743
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 47829
Depositing User: Enrico Daga
Date Deposited: 16 Nov 2016 13:53
Last Modified: 09 May 2019 09:56
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