14 Ask a KG with natural language
This chapter covers
- Understanding the limitations of RAG in complex scenarios
- Building an advanced question-answering system that mimics domain expertise on KGs
- Transforming query results into meaningful, actionable summaries
In this chapter, we will explore how to build an advanced system that can answer questions effectively. Using a law enforcement example as our guide, we'll compare the RAG approach and our new "expert emulation" method for capturing the expertise of skilled information retrieval. We will walk you through the key concepts and components needed to build this system, giving you a solid foundation for creating reliable question-answering applications.
The framework we'll develop rests on several key pillars:
- Understanding and properly routing different types of user questions
- Extracting and representing domain knowledge in a form that LLMs can effectively utilize
- Implementing expert-like reasoning patterns for query construction
- Ensuring results are presented in meaningful, actionable ways
This framework is specifically designed to integrate with a front-end layer, ensuring that the question-answering system can be effectively presented to end users through a graphical interface. This integration-first approach influences many of our design decisions throughout the chapter, from how we structure query responses to how we handle data visualization.