Large Language Models (LLMs) have become incredibly powerful tools for various tasks, including processing and understanding web news. But while LLMs excel at generating human-like text, they sometimes struggle with common-sense reasoning and capturing the intricate relationships within the information they process. Imagine an LLM trying to understand a complex news story with multiple actors, events, and timelines—it might miss the subtle connections that give the story its true meaning. This is where knowledge bases come in. Researchers are exploring how structured knowledge bases can enhance LLMs, providing them with a backbone of explicit facts and relationships. Think of it like giving the LLM a cheat sheet with key information already organized. This research introduces a framework called BERTGraph, which combines the power of BERT (a popular LLM) with a graph convolutional network (GCN). The GCN takes both the LLM's output and a knowledge base generated from the news item, allowing it to combine the LLM’s implicit knowledge with the explicit knowledge from the KB. This process involves extracting structured information from news items using a rule-based system called NewsIE, creating relational tuples that represent the connections within the news. This extracted information is then combined with the LLM’s output using the GCN. The results are promising, showing improved performance in news category classification across several datasets. This approach helps LLMs understand the connections and nuances within news stories more effectively, leading to more accurate and insightful analysis. By adding this structured knowledge, LLMs can reason more like humans, making them even more valuable for understanding the complex world of news and information. However, this approach also faces challenges. Creating and maintaining accurate knowledge bases can be complex, especially as news constantly evolves. Future research could explore how to automate this process further and adapt it to different news domains and languages. The integration of structured knowledge bases with LLMs is a significant step towards more intelligent and nuanced AI systems, promising deeper and more accurate information processing across various domains, not just news.