Aligning Textual and Graphical Descriptions of Processes Through ILP Techniques

Sànchez-Ferreres, Josep; Carmona, Josep; Padró, Lluís

doi:10.1007/978-3-319-59536-8_26

Cited by 20 publications

(18 citation statements)

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“…Therefore, existing approaches cannot be applied to detect inconsistencies between process models and textual process descriptions. Work by Sanchez et al [245] forms an exception to this, although it should be noted that their approach represents a follow-up to ours.As an interesting extension, the approach from [245] also considers the resource and data perspectives when establishing alignments between model and text.…”

Section: Related Workmentioning

confidence: 86%

Comparing and Aligning Process Representations

2018

Lecture Notes in Business Information Processing

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Section: Related Workmentioning

confidence: 86%

Comparing and Aligning Process Representations

2018

Lecture Notes in Business Information Processing

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“…The goal of aligning a process model and a textual process description is to identify correspondences between the elements of the process model and parts of the textual process description [12], [33], [34]. In particular, all elements that Type Text Fragment ↵ 1 action "an examination and follow-up treatment order is placed by the physician" ↵ 2 action "is then responsible for taking a sample to be analysed in the lab" ↵ 3 action "After receiving the sample, a physician of the lab validates its state" ↵ 4 role "an examination and follow-up treatment order is placed by the physician" ↵ 5 role "the female patient is examined by an outpatient physician" ↵ 6 object "an examination and follow-up treatment order is placed by the physician" ↵ 7…”

Section: B Alignment Of Models and Annotated Textsmentioning

confidence: 99%

Supporting the Process of Learning and Teaching Process Models

Sànchez-Ferreres

Delicado

Andaloussi

et al. 2020

IEEE Trans. Learning Technol.

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The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity which accurately reflects the semantics of reality, and is understandable to the model reader. This paper proposes a framework called Model Judge, focused towards the two main actors in the process of learning process model creation: novice modelers and instructors. For modelers, the platform enables the automatic validation of the process models created from a textual description, providing explanations about quality issues in the model. Model Judge can provide diagnostics regarding model structure, writing style, and semantics by aligning annotated textual descriptions to models. For instructors, the platform facilitates the creation of modeling exercises by providing an editor to annotate the main parts of a textual description, that is empowered with natural language processing (NLP) capabilities so that the annotation effort is minimized. So far around 300 students, in process modeling courses of five different universities around the world have used the platform. The feedback gathered from some of these courses shows good potential in helping students to improve their learning experience, which might, in turn, impact process model quality and understandability. Moreover, our results show that instructors can benefit from getting insights into the evolution of modeling processes including arising quality issues of single students, but also discovering tendencies in groups of students. Although the framework has been applied to process model creation, it could be extrapolated to other contexts where the creation of models based on a textual description plays an important role. Index Terms-Natural language processing (NLP), process of process modeling, textual annotation, Business Process Model and Notation (BPMN). I. INTRODUCTION AND MOTIVATION P ROCESS models play an important role in the analysis and improvement of business processes [1]. Moreover, they are often used as basis for process execution. Due to their Manuscript

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“…In the middle of the spectrum, we have approaches that automatically process the natural language text but they generate an intermediate artifact, useful to support the manual modeling by providing intermediate diagnostics [8,20]. The problem of having a single interpretation for ambiguities is a bit mitigated in this case since a human modeler is still in charge of the actual modeling.…”

Section: Related Workmentioning

confidence: 99%

Formal Reasoning on Natural Language Descriptions of Processes

Sànchez-Ferreres

Burattin

Carmona

et al. 2019

Lecture Notes in Computer Science

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The existence of unstructured information that describes processes represents a challenge in organizations, mainly because this data cannot be directly referred into process-aware ecosystems due to ambiguities. Still, this information is important, since it encompasses aspects of a process that are left out when formalizing it on a particular modelling notation. This paper picks up this challenge and faces the problem of ambiguities by acknowledging its existence and mitigating it. Specifically, we propose a framework to partially automate the elicitation of a formal representation of a textual process description, via text annotation techniques on top of natural language processing. The result is the ATDP language, whose syntax and semantics are described in this paper. ATDP allows to explicitly cope with several interpretations of the same textual description of a process model. Moreover, we link the ATDP language to a formal reasoning engine and show several use cases. A prototype tool enabling the complete methodology has been implemented, and several examples using the tool are provided.

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Aligning Textual and Graphical Descriptions of Processes Through ILP Techniques

Cited by 20 publications

References 18 publications

Comparing and Aligning Process Representations

Comparing and Aligning Process Representations

Supporting the Process of Learning and Teaching Process Models

Formal Reasoning on Natural Language Descriptions of Processes

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