## 2 Related Work
MOLA [32] and VIATRA [33] use custom graphical modeling languages to imperatively define full-scale M2M transformations. Yet, we were not able to identify any extension points required to implement NLP functionality for these languages. Design patterns in M2M transformations is also a relevant subject of discussion in the modeling community. Starting with the previous version [14], our solution relies heavily on transformation pattern construction. Design pattern-driven development of M2M transformations is supported in [34]. The authors of [35] introduced a taxonomy of design patterns for model transformation formalizing typical cases that arise during transformation specifications; they also identified the use of these patterns in other related research [10]. SPLIT and CONCAT patterns, further introduced in Section 5.1, might be classified under such taxonomy as entity splitting and entity merging patterns, respectively. Section 6 also introduces a set of transformation patterns that are not expected to meet any categorization yet, although similar taxonomies can be created in the future, after NLP integration with M2M transformations becomes more mature. NLP-extended model transformations are directly influenced by the quality of automated processing and interpretation of textual labels in visual models. Most of the research in this field was carried out specifically in business process modeling [36; 37]. Many aspects of this research, however, can be transferred to other models as well, provided those models apply similar naming and modeling practices. While in our research we presented an exemplary transformation pattern to transform UML activities defined using different naming conventions, this task could also be undertaken by using techniques for activity labeling style identification [37] or by extensively utilizing relevant text corpora, lexical resources like WordNet, and linguistic knowledge [38; 39]. [PERSON] et al. [40] proposed a set of activity naming anti-patterns, which were addressed in [41] by introducing the notion of canonicity to define the so-called atomic business process consisting of exactly one action, one business object, and no more than one addition. While our approach tries to apply NLP technology for processing labels, a different route was taken in [41] that, instead of trying to detect naming patterns and preprocess labels automatically, allows us to define conditional transformations for different types of process names. An extensive study of preprocessing textual labels is presented by [PERSON] in [39]. This includes algorithms for detecting homonyms and synonyms in the context of process models by combining semantic vectors with information obtained from external resources. Arguably, such an approach could be more suitable for model processing than deep learning approaches; however, this assumption requires additional verification. Matching elements from multiple models can also be solved using several model matching techniques, particularly text-based or similarity-based matching, which is one of the four categories proposed in a recent survey of model matching techniques [42]. Finally, solving semantic interoperability issues should also be considered to ensure proper transfer of concepts from the source model to the target model while taking into account their contextual discrepancies. Again, there are multiple ways to solve these issues, as suggested in the relevant literature [43]. Finally, one must acknowledge the many recent developments in the area of NLP itself. Some of the core and most recent works are discussed in Section 5.2. However, we reviewed only a small part of the most relevant state-of-the-art works in this area.