?
Integrating an Ontology-Driven Approach to Data Visualization and AI Based Visualization with Plotly
This study introduces an AI-driven assistant prototype that automates the generation of data visualization scripts from natural language queries, eliminating the need for users to have programming skills. The article examines research aimed at developing tools for effective data visualization, compares data visualization systems based on the use of artificial intelligence, and shows the limitations of the existing tools. The proposed approach to data visualization is based on integrating knowledge-driven DSM platform (language toolkits) and generative AI tools. The proposed methodology categorizes tasks of data visualization into two distinct types: standard and non-standard. Standard tasks are solved with a code-generation approach based on prompts within a visual environment. Non-standard tasks are handled by extending existing libraries with user defined packages. The language-oriented approach with DSM tools effectively unifies both categories: for standard tasks, users work with pre-existing DSLs and adjust parameters as necessary, whereas for non-standard tasks, users develop new DSLs with language toolkits automating visual DSL creation and code generation. The core of the language toolkits is multifaceted ontology. By integrating a large language model (LLM) with a knowledge-driven framework and a multifaceted ontology, the system enables dynamic, context-aware visualization workflows that ensure semantic traceability and reproducibility. The ontology not only stores descriptions of data visualization tasks but also facilitates the reuse of generated scripts, thereby enhancing the system’s adaptability and fostering collaborative analytical work among user communities. The dataset, containing entries and variables encompassing different domains, is used to demonstrate the functionality of the prototype. The article provides examples of developing several visualization options, demonstrating the application of the proposed approach. Case studies demonstrate the prototype’s efficacy in creating histograms, scatter plots, and other visualization methods, while reducing technical barriers for users. Future work will extend the assistant’s functionality by incorporating user-defined visualization packages and additional LLM training to address non-standard tasks and complex visualization scenarios.