Augmenting Modelers with Semantic Autocompletion of Processes
- URL: http://arxiv.org/abs/2105.11385v1
- Date: Mon, 24 May 2021 16:23:07 GMT
- Title: Augmenting Modelers with Semantic Autocompletion of Processes
- Authors: Maayan Goldstein and Cecilia Gonzalez-Alvarez
- Abstract summary: Business process modelers need expertise and knowledge of the domain that may not always be available to them.
We present a method for process autocompletion at design time, that is based on the semantic similarity of sub-processes.
- Score: 5.279475826661643
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Business process modelers need to have expertise and knowledge of the domain
that may not always be available to them. Therefore, they may benefit from
tools that mine collections of existing processes and recommend element(s) to
be added to a new process that they are constructing. In this paper, we present
a method for process autocompletion at design time, that is based on the
semantic similarity of sub-processes. By converting sub-processes to textual
paragraphs and encoding them as numerical vectors, we can find semantically
similar ones, and thereafter recommend the next element. To achieve this, we
leverage a state-of-the-art technique for embedding natural language as
vectors. We evaluate our approach on open source and proprietary datasets and
show that our technique is accurate for processes in various domains.
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