# Generating Process Models by Interacting with Chatbots--A Literature Review
## 4 Conducting the Systematic Literature Review
### Data Extraction and Synthesis

Table 4: Content of the relevant Studies S9-S18 in this SLRTo answer Research Question RQ1 (how to generate process models from natural language text input), in the following, we present the most frequently used technologies and methodologies of the studies to generate process models from natural language text input:  
* RQ1\(A_{1}\): NLP. NLP involves the computational analysis of human language to extract meaning and structure from unstructured text data. In the context of generating process models from natural language text input, NLP techniques are employed to parse and understand textual descriptions of processes. This may include tasks such as part-of-speech tagging, syntactic parsing, named entity recognition, and semantic analysis. By applying NLP algorithms, systems can identify key process elements, relationships, and activities described in the text, which can then be translated into formal representations such as BPMN diagrams. * RQ1\(A_{2}\): Mapping rules. Mapping rules refer to predefined guidelines or algorithms that are used to translate natural language descriptions into formal representations, such as BPMN process models. These rules define how specific linguistic constructs or patterns in text correspond to elements and relationships in the target model. Mapping rules may be based on syntactic analysis, semantic rules, domain-specific knowledge, or a combination thereof. By applying mapping rules systematically, systems can automate the process of generating process models from textual input, ensuring consistency and accuracy in the resulting models. * RQ1\(A_{3}\): Sentence-level analysis. Sentence-level analysis involves the examination and interpretation of individual sentences within a text to extract relevant information and structure. In the context of generating process models from natural language text as input, sentence-level analysis focuses on identifying process-related statements, actions, and dependencies expressed at the sentence level. This may involve techniques such as sentence parsing, sentiment analysis, and keyword extraction to recognize process activities, participants, and constraints. Sentence-level analysis enables finer-grained understanding of textual descriptions, facilitating the creation of detailed and contextually relevant process models. To answer Research Question RQ2 (how to generate natural language text from process models), we present the most frequently used technology and methodology in the studies and discuss of how they generate natural language text from process models:  
* RQ2\(A_{1}\): NLP. In the context of generating natural language text from process models, NLP techniques are utilized to transform structured representations, such as BPMN diagrams, into human-readable textual descriptions. This process involves analyzing the elements and relationships encoded in the process model, such as activities, events, gateways, and flows, and generating coherent and grammatically correct sentences that convey the semantics of the model. NLP algorithms may employ syntactic and semantic parsing, template-based generation, and linguistic rules to ensure the accuracy and fluency of the generated text. By leveraging NLP, systems can automate the generation of textual documentation, instructions, or explanations from process models, facilitating communication and comprehension among stakeholders. Additionally, NLP-based approaches enable the dynamic generation of text tailored to specific audiences or contexts, enhancing the usability and accessibility of process documentation in diverse organizational settings. To answer Research Question RQ3 (How to get from natural language text input to process models and backwards), the same attribute as in RQ1 and RQ2 were selected and used. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Research Question** & \(A_{1}\) **(NLP)** & \(A_{2}\) **(Mapping Rules)** & \(A_{3}\) **(Sentence Level Analysis)** \\ \hline RQ1 & ✓ & ✓ & ✓ \\ RQ2 & ✓ & & \\ RQ3 & ✓ & & \\ \hline \hline \end{tabular}
\end{table}