Text2Simulate: A Scientific Knowledge Visualization Technique for Generating Visual Simulations from Textual Knowledge

Recent research has developed knowledge visualization techniques for generating interactive visualizations from textual knowledge. However, when applied, these do not generate precise semantic visual representations, which is imperative domains that require an accurate representation of spatial attributes and relationships between objects discourse in explicit Therefore, this work presents a Text-to-Simulation Knowledge Visualization (TSKV) technique simulations domain by developing rule-based classifier to improve natural language processing, Spatial Ordering (SO) algorithm solve the identified challenge. A system architecture was structurally model components TSKV implemented using application called ‘Text2Simulate’. quantitative evaluation carried out test accuracy modified existing information criteria. Object Inclusion (OI), Object-Attribute Visibility (OAV), Relative Positioning (RP), Exact Visual Representation (EVR) criteria were include Object’s Motion (OM) metric generated simulations. Evaluation on simulation results 90.1, 84.0, 90.0, 96.0% OI, OAV, OM, RP, EVR respectively. User conducted measure effectiveness user satisfaction showed all participants satisfied well above average. These improved quality visualized due classification This could be adopted during development electronic learning applications understanding desirable actions.