In this paper, we explore the possibility to apply natural language processing in visual model-to-model (M2M) transformations. Therefore, we present our research results on information extraction from text labels in process models modeled using Business Process Modeling Notation (BPMN) and use case models depicted in Unified Modeling Language (UML) using the most recent developments in natural language processing (NLP). In this paper, we focus on three relevant tasks, namely, the extraction of verb/noun phrases that would be used to form relations, parsing of conjunctive/disjunctive statements, and the detection of abbreviations and acronyms. Relation extraction was attempted to solve by implementing techniques that combine state-of-the-art NLP language models with formal regular expressions grammar-based structure detection. In this paper, we perform thorough testing of the most recent state-of-the-art NLP tools (CoreNLP, Stanford Stanza, Flair, Spacy, AllenNLP, BERT, ELECTRA), as well as custom BERT-BiLSTM-CRF and ELMo-BiLSTM-CRF implementations, trained with certain data augmentations to improve performance on the most ambiguous cases; these tools are used as a foundation for building tools to extract noun and verb phrases from short text labels generally used in UML and BPMN models. Furthermore, we describe our attempts to improve these extractors by solving the abbreviation/acronym detection problem using machine learning-based detection, as well as process conjunctive and disjunctive statements, due to their relevance to performing advanced text normalization. The obtained results show that the best phrase extraction and conjunctive phrase processing performance was obtained using Stanza based implementation, yet, our trained BERT-BiLSTM-CRF outperformed it for the verb phrase detection task. Our acronym detection approach resulted in the precision of 0.78 and F1-Score of 0.73 which may also be considered quite positive. While this work was inspired by our ongoing research on partial model-to-model transformations, we believe it to be applicable in other areas requiring similar text processing capabilities as well.

To properly and quickly evaluate an object’s shape, in a manner that is suitable for real-time applications, a set of parameters has been created and the shape parametric description (SPD) has been elaborated. This solution is focused on the classification of amber gemstones according to shape. To improve the results obtained by SPD, the most popular machine learning classification algorithms were applied and tested. The proposed method (i.e., SPD) achieved the fastest classification, requiring the least computational resources, while providing an accuracy of approximately 80%. The best results were achieved when the SPD parameters were used in a feedforward neural network (FFNN), and an accuracy of 91.5% was obtained, while the time required for the computations remained in a range that is acceptable for real-time applications.

Aspects of business process models' integration with structured business vocabularies & rules are analyzed in this paper. Despite the fact that business process (BP) modeling has its long-lasting traditions in various areas of application, this discipline remains in the constant process of improvement and issue-solving. The paper deals with one of such issues, namely, the existing gap between BP modeling and specification of business vocabularies & rules. In not dealt appropriately, this may lead to some issues while developing, reading and interpreting business models themselves, also to miscommunication issues within and among the organizations dependent on such models, and so on. The gap could be diminished by the means of integration of BP models with business vocabularies & rules, the paper presents some argumentation to back such statements. Later, basic principles of the approach for BPMN BP model integration with SBVR business vocabulary & rules are presented and briefly described in this paper.

Despite the fact that business process (BP) modeling has its long-lasting traditions in various areas of application, this discipline remains in the constant process of improvement and issue-solving. The possibilities of synergy among business process models and business vocabularies & rules are analyzed in this paper. The authors emphasize the existing gap between business process modeling and specification of business vocabularies & rules. Such situation may lead to misunderstandings while reading and interpreting business models and also miscommunication issues within among the organizations. Some of these issues could be resolved by realizing the integration of BP modeling standards with business vocabularies & rules. The paper presents some argumentation to back such statements. Later, basic principles of the approach for BPMN Business process model integration with SBVR business vocabulary & rules are presented and briefly described in this paper.DOI: http://dx.doi.org/10.5755/j01.itc.41.4.2013

Approaches for the analysis and specification of business vocabularies and rules are relevant topics in both Business Process Management and Information Systems Development disciplines. However, in common practice of Information Systems Development, the Business modeling activities still are of mostly empiric nature. In this paper, aspects of the approach for semi-automatic extraction of business vocabularies (BV) from business process models (BPM) are presented. The approach is based on novel business modeling-level OMG standards ?Business Process Model and Notation? (BPMN) and ?Semantics for Business Vocabularies and Business Rules? (SBVR), thus contributing to OMG?s vision of Model-Driven Architecture (MDA) and to model-driven development in general. The discussed extraction approach is evaluated against fully-automatic BPMN BPM ? SBVR BV transformation that has been developed in parallel to the presented work.