X-WiKi: Graph-Based Knowledge Aggregation from Wikipedia Pages i.

This research presents a new method for synthe-sizing knowledge using a graph model linked to Wikipedia articles. It utilizes natural language processing to extract meaningful information from these articles, organizing it into a graph structure with nodes representing article titles and edges indicating their connections (X- WiKi's linkage co-occurrence graph). This structure is analyzed using co-occurrence graph algorithms and Text Representing Centroid (TRC), focusing on significance, clustering, and relational analysis. The primary goal is to distill essential knowledge into an easily understandable and relational format. This includes de-veloping new algorithms for knowledge generation and summa-rization and evaluating their effectiveness compared to existing TR C methods. Applications include developing educational methodologies, automated research tools, and comprehensive knowledge databases. This study marks a significant advancement in data synthe-sis, presenting an efficient framework for integrating knowl-edge from various disciplines. It simplifies complex concepts from interconnected texts, enhancing accessibility and under-standing of large datasets and benefiting the educational and research community.