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On the Use of Knowledge Graph Completion Methods for Activity Recommendation in Business Process Modeling

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Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 436)

Abstract

Business process modeling is essential for organisations. However, it is a time-consuming task that requires expert knowledge. In particular, this is the case when modeling domain-specific processes, which often involves the consistent use of technical terminology. Process modelers can be supported through the provision of recommendations on how the model under development can be expanded. Activity recommendation is one such support approach, in which suitable activities to be inserted at a user-defined position are recommended. Recently, it has been suggested to treat activity recommendation as a knowledge graph completion task and to apply methods from this discipline. In this paper, we investigate different approaches to apply embedding- and rule-based knowledge graph completion methods out of the box and evaluate them in an experimental study. Additionally, we compare them to two methods that have specifically been designed for activity recommendation.

Keywords

  • Activity recommendation
  • Knowledge graph completion
  • Rule learning
  • Embeddings

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    Note that we performed the experiments dividedly on two computers: Intel® Xeon® CPU E5-2640 v3@40x2.40 GHz and Intel® Xeon® Silver 4114 CPU@40x2.20 GHz.

  2. 2.

    We also tested other popular KGE models (ComplEx, ConvE) but they yielded comparatively poor results that we do not report here.

  3. 3.

    These parameter settings are specified by MAX_LENGTH_CYCLIC = 5 and MAX_LENGTH_ACYCLIC = 2.

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Sola, D., Meilicke, C., van der Aa, H., Stuckenschmidt, H. (2022). On the Use of Knowledge Graph Completion Methods for Activity Recommendation in Business Process Modeling. In: Marrella, A., Weber, B. (eds) Business Process Management Workshops. BPM 2021. Lecture Notes in Business Information Processing, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-030-94343-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-94343-1_1

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