Network analysis of terms in the natural sciences insights from Wikipedia through natural language processing and network analysis

Scientists use specific terms to denote concepts, objects, phenomena, etc. The terms are then connected with each other in sentences that are used in science-specific language. Representing these connections through term networks can yield valuable insights into central terms and properties of the interconnections between them. Furthermore, understanding term networks can enhance assessment and diagnostics in science education. Computational means such as natural language processing and network analysis provide tools to analyze term networks in a principled way. This study utilizes natural language processing and network analysis to analyze linguistic properties of terms in the natural science disciplines (biology, chemistry, and physics). The language samples comprised German and English Wikipedia articles that are labelled according to the respective discipline. The different languages were used as contrasting cases. Natural language processing capabilities allowed us to extract term networks from the Wikipedia articles. The network analysis approach enabled us to gain insights into linguistic properties of science terms and interconnections among them. Our findings indicate that in German and English Wikipedia terms such as theory, time, energy, or system emerge as most central in physics. Moreover, the science-term networks display typical scale-free, complex systems behavior. These findings can enhance assessment of science learner's language use. The tools of natural language processing and network analysis more generally can facilitate information extraction from language corpora in the education fields.