INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELS
In this paper, we present a method for comparing business process models with their textual descriptions, using a semantic-based approach based on the SBERT (Sentence-Bidirectional Encoder Representations from Transformers) model. Business process models, especially those created with the BPMN (Busi...
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| Format: | Article |
| Language: | English |
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National Technical University Kharkiv Polytechnic Institute
2024-12-01
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| Series: | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології |
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| Online Access: | http://samit.khpi.edu.ua/article/view/320181 |
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| author | Oleksandr Rudskyi Andrii Kopp Tetiana Goncharenko Igor Gamayun |
| author_facet | Oleksandr Rudskyi Andrii Kopp Tetiana Goncharenko Igor Gamayun |
| author_sort | Oleksandr Rudskyi |
| collection | DOAJ |
| description | In this paper, we present a method for comparing business process models with their textual descriptions, using a semantic-based approach based on the SBERT (Sentence-Bidirectional Encoder Representations from Transformers) model. Business process models, especially those created with the BPMN (Business Process Model and Notation) standard, are crucial for optimizing organizational activities. Ensuring the alignment between these models and their textual descriptions is essential for improving business process accuracy and clarity. Traditional set similarity methods, which rely on tokenization and basic word matching, fail to capture deeper semantic relationships, leading to lower accuracy in comparison. Our approach addresses this issue by leveraging the SBERT model to evaluate the semantic similarity between the text description and the BPMN business process model. The experimental results demonstrate that the SBERT-based method outperforms traditional methods, based on similarity measures, by an average of 31%, offering more reliable and contextually relevant comparisons. The ability of SBERT to capture semantic similarity, including identifying synonyms and contextually relevant terms, provides a significant advantage over simple token-based approaches, which often overlook nuanced language variations. The experimental results demonstrate that the SBERT-based approach, proposed in this study, improves the alignment between textual descriptions and corresponding business process models. This advancement is allowing to improve the overall quality and accuracy of business process documentation, leading to fewer errors, introducing better clarity in business process descriptions, and better communication between all the stakeholders. The overall results obtained in this study contribute to enhancing the quality and consistency of BPMN business process models and related documentation. |
| format | Article |
| id | doaj-art-37356b46f579445eb1912500b730dc0b |
| institution | Kabale University |
| issn | 2079-0023 2410-2857 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | National Technical University Kharkiv Polytechnic Institute |
| record_format | Article |
| series | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології |
| spelling | doaj-art-37356b46f579445eb1912500b730dc0b2025-01-08T14:40:15ZengNational Technical University Kharkiv Polytechnic InstituteВісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології2079-00232410-28572024-12-012 (12)566510.20998/2079-0023.2024.02.09358842INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELSOleksandr Rudskyi0https://orcid.org/0009-0001-1130-9957Andrii Kopp1https://orcid.org/0000-0002-3189-5623Tetiana Goncharenko2https://orcid.org/0000-0001-6630-307XIgor Gamayun3https://orcid.org/0000-0003-2099-4658National Technical University "Kharkiv Polytechnic Institute"National Technical University "Kharkiv Polytechnic Institute"National Technical University "Kharkiv Polytechnic Institute"National Technical University "Kharkiv Polytechnic Institute"In this paper, we present a method for comparing business process models with their textual descriptions, using a semantic-based approach based on the SBERT (Sentence-Bidirectional Encoder Representations from Transformers) model. Business process models, especially those created with the BPMN (Business Process Model and Notation) standard, are crucial for optimizing organizational activities. Ensuring the alignment between these models and their textual descriptions is essential for improving business process accuracy and clarity. Traditional set similarity methods, which rely on tokenization and basic word matching, fail to capture deeper semantic relationships, leading to lower accuracy in comparison. Our approach addresses this issue by leveraging the SBERT model to evaluate the semantic similarity between the text description and the BPMN business process model. The experimental results demonstrate that the SBERT-based method outperforms traditional methods, based on similarity measures, by an average of 31%, offering more reliable and contextually relevant comparisons. The ability of SBERT to capture semantic similarity, including identifying synonyms and contextually relevant terms, provides a significant advantage over simple token-based approaches, which often overlook nuanced language variations. The experimental results demonstrate that the SBERT-based approach, proposed in this study, improves the alignment between textual descriptions and corresponding business process models. This advancement is allowing to improve the overall quality and accuracy of business process documentation, leading to fewer errors, introducing better clarity in business process descriptions, and better communication between all the stakeholders. The overall results obtained in this study contribute to enhancing the quality and consistency of BPMN business process models and related documentation.http://samit.khpi.edu.ua/article/view/320181business process modelingbpmnsemantic similaritysberttext comparisonbusiness process optimizationnatural language processing |
| spellingShingle | Oleksandr Rudskyi Andrii Kopp Tetiana Goncharenko Igor Gamayun INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELS Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології business process modeling bpmn semantic similarity sbert text comparison business process optimization natural language processing |
| title | INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELS |
| title_full | INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELS |
| title_fullStr | INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELS |
| title_full_unstemmed | INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELS |
| title_short | INTELLIGENT TECHNOLOGY FOR SEMANTIC COMPLETENESS ASSESSMENT OF BUSINESS PROCESS MODELS |
| title_sort | intelligent technology for semantic completeness assessment of business process models |
| topic | business process modeling bpmn semantic similarity sbert text comparison business process optimization natural language processing |
| url | http://samit.khpi.edu.ua/article/view/320181 |
| work_keys_str_mv | AT oleksandrrudskyi intelligenttechnologyforsemanticcompletenessassessmentofbusinessprocessmodels AT andriikopp intelligenttechnologyforsemanticcompletenessassessmentofbusinessprocessmodels AT tetianagoncharenko intelligenttechnologyforsemanticcompletenessassessmentofbusinessprocessmodels AT igorgamayun intelligenttechnologyforsemanticcompletenessassessmentofbusinessprocessmodels |