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

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

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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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Notes

  1. 1.

    My experiment: http://www.myexperiment.org/.

  2. 2.

    W3C PROV: https://www.w3.org/TR/prov-overview/.

  3. 3.

    OPMW: http://www.opmw.org/.

  4. 4.

    PWO: http://purl.org/spar/pwo.

  5. 5.

    Wings: http://www.wings-workflows.org/.

  6. 6.

    My experiments: http://www.myexperiment.org/.

  7. 7.

    SHIWA: http://www.shiwa-workflow.eu/wiki/-/wiki/Main/SHIWA+Repository.

  8. 8.

    SCUFL2: https://taverna.incubator.apache.org/documentation/scufl2/.

  9. 9.

    Datanode: http://purl.org/datanode/ns/.

  10. 10.

    In this paper we use the terminology of the SCUFL2 specification. However, the basic structure is a common one. In the W3C PROV-O model this concept maps to the class Activity, in PWO with Step, and in OPMW to WorkflowExecutionProcess, just to mention few examples.

  11. 11.

    “LipidMaps Query” workflow from My experiment: http://www.myexperiment.org/workflows/1052.html.

  12. 12.

    Dinowolf: http://github.com/enridaga/dinowolf.

  13. 13.

    SCUFL2 Specification: https://taverna.incubator.apache.org/documentation/scufl2/.

  14. 14.

    Apache Taverna: https://taverna.incubator.apache.org/.

  15. 15.

    Apache Lucene: https://lucene.apache.org/core/.

  16. 16.

    DBPedia Spotlight: http://spotlight.dbpedia.org/.

  17. 17.

    DBPedia: http://dbpedia.org/.

  18. 18.

    My Experiments: http://www.myexperiments.org.

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Authors and Affiliations

  1. Knowledge Media Institute (KMI) - The Open University, Walton Hall, Milton Keynes, MK76AA, UK

    Enrico Daga, Mathieu d’Aquin & Enrico Motta

  2. National Research Council (CNR), Via Gaifami 18, 95126, Catania, Italy

    Aldo Gangemi

  3. Paris Nord University, Sorbonne Cite CNRS UMR7030, Paris, France

    Aldo Gangemi

Authors
  1. Enrico Daga
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  2. Mathieu d’Aquin
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  3. Aldo Gangemi
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  4. Enrico Motta
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Correspondence to Enrico Daga .

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Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

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Daga, E., d’Aquin, M., Gangemi, A., Motta, E. (2016). An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_9

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

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