A Causal Knowledge Graph for Analyzing and Visualizing Programming Language Evolution from Wikipedia Infobox Data
Keywords:
Causal Knowledge Graph, Programming Language Evolution, Wikipedia InfoboxAbstract
Understanding the evolutionary relationships between programming languages is crucial for software development, education, and research. However, current research lacks comprehensive tools for analyzing causal relationships and influence patterns across the diverse programming landscape. This study proposes a method for creating a causal knowledge graph that illustrates the evolution of programming languages by utilizing automated data collection and graph modeling to identify different levels of influence. The system processes structured English and Thai Wikipedia infobox data, analyzing and transforming it into a graph format using Neo4j. Graph visualization is performed using the Cypher Query Language. The knowledge graph presents C-influenced programming languages across three evolutionary stages, supporting both English and Thai. Key features include displaying influence paths and illustrating languages with similar structures. The results demonstrate that graph-based representations effectively capture the evolution of programming languages, enabling learners, educators, and researchers to explore interlanguage relationships.
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