Modeling and Representing Translation-based Classic Transmission Knowledge

Translating classical texts plays a pivotal role in cultural transmission, significantly facilitating the exchange and dissemination of ideas worldwide and fostering the collaborative development of diverse cultures. To facilitate a better understanding of classic transmission based on translations and promote the integration of fragmented knowledge on classic transmission, this study employed ontology and knowledge graphs for modeling and representing translation-based classic transmission knowledge from a multi-disciplinary perspective by integrating history, culture, geography, and communication knowledge. Centering on 'events', the proposed translation-based classic transmission model (TCTM) provides a detailed representation of various entities, attributes, and complex interrelationships and interactions. Using <i>Tao Te Ching</i> as an example, this study conducted the instantiation based on TCTM with multiple data sources to generate a knowledge graph and introduced a method for evaluating the effects of classic transmission for validating the model. The results indicate that the approach is able to dissect the process of the <i>Tao Te Ching</i>'s effect on various countries. The findings suggest that the proposed multi-disciplinary model offers valuable insights into the dynamics of classic transmission and is effective in uncovering latent knowledge within these processes.