Challenges and future directions
The integration of GraphML and LLMs opens up a lot of possibilities, but it also presents significant challenges that must be addressed for widespread adoption. One of the foremost concerns is scalability since dealing with large-scale graphs alongside computationally intensive LLMs requires expensive resources in terms of memory, processing power, and efficient data pipelines.
As with many other deep learning-based approaches, another major challenge is in interpretability. While knowledge graphs provide a structured and transparent way to store relationships, LLMs operate as a black box, making it difficult to understand how specific outputs are generated. For example, considering the presented GraphRAG approach, it is not guaranteed that the generated query will be semantically correct, and correcting the result is not an easy task.
Data alignment is also a key issue, as structured knowledge graphs and unstructured text data must be carefully...
