Defining Business Process Models with Natural Language Processing and Speech Recognition
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Proposed approaches for modeling unstructured business processes include
declarative, constraint-based solutions, which meet halfway between support and flexibility. A
noteworthy example is the Declare framework, which is equipped with a graphical declarative
language whose semantics can be expressed with multiple logic-based formalisms. So far, the
practical use of Declare constraints has been mostly hampered by the difficulty of modeling
them: the formal notation of Declare represents a steep learning curve for users lacking an
understanding of temporal logic, while the graphical notation has proven to be unintuitive. As
such, this work presents and assesses an analysis toolkit which tries to circumvent this issue by
providing the user with a possibility to model Declare constraints by using their own way of
expression.
The toolkit includes a Declare modeler supplied with a speech recognition mechanism, which
accepts a user’s vocal statement as input and converts it into the closest Declare constraint(s) by
combining voice recognition, natural language processing and business rule extraction
technologies. The constraints cover the entire Multi-Perspective extension of Declare
(MP-Declare), complementing control-flow constraints with data and temporal perspectives.
Even though this thesis focuses on Declare, it represents the first attempt at testing the
practicability of speech recognition in business process modeling altogether.
Description
Keywords
Business Process Management, Declarative Process Models, Process mining, Process discovery, Conformance checking, Process analytics tool