LLM-Driven Summarization and Distinguish Analysis of Multiple Entities in RDF Graphs

This research implements the application of Large Language Models (LLMs) in the summarization and distinguish analysis of multiple entities within Resource Description Framework (RDF) graphs. As the volume of structured data on the web is growing exponentially, the need for efficient and effective methods to interpret and summarize this data becomes increasingly important. This study focuses on utilizing LLMs to generate human-readable summaries from RDF graphs and particularly emphasizing on distinguishing between multiple entities. The study apply SPARQL queries to extract relevant data from DBpedia, subsequently a thorough process of frequency analysis and property unification to refine the dataset. Three LLMs including ChatGPT, DeepSeek, and Mistral have been evaluated for their ability to generate coherent and informative summaries. The evaluation process combines human-based assessments with automated metrics for the thorough analysis of generated texts. Key outcomes include the effectiveness of LLMs in generating summaries that are both informative and contextually relevant. The research also reflects the importance of data preprocessing techniques, such as frequency analysis and property unification in enhancing the quality of the generated summaries. Moreover, the study provides insights into the strengths and limitations of different LLMs in summarizing RDF data that offers a foundation for future research in this area. A framework for evaluating the performance of LLMs in summarization tasks has been designed in this research opens the way for future explorations in the application of advanced AI technologies in data interpretation and knowledge representation.