How I used the GraphRAG paper to enhance structured product data for televisions.
When looking at the GraphRAG paper, different approaches are presented to create a knowledge graph from a large amount of text. This graph encapsulates knowledge and relationships derived from the text data. During the indexing phase, GraphRAG transforms source documents into a structured and semantically rich graph. By iteratively calling a Large Language Model (LLM), GraphRAG extracts entities, relationships, and statements while summarizing them. This is achieved through multi-level “gleaning” (iterative extraction) and domain-specific adaptations.
However, what if you already have structured data, such as product data? Simply inputting the raw JSON into GraphRAG will not create useful insights because, in its raw form, it is treated as unstructured text. However, product data in JSON format is already well-defined, with established entities like products, attributes, and categories, along with clear relationships and structured metadata. Instead, GraphRAG focuses on extracting insights from unstructured text data, leveraging its capabilities to identify complex relationships and enhance the understanding of information from diverse textual sources.
What Can We Still Learn from GraphRAG for Structured Data?
The following points, inspired by the indexing phase of the GraphRAG paper, have been adapted to structured product data of television products:
- Hierarchical Structuring: GraphRAG organizes data into hierarchical communities, allowing information to be structured at different levels of abstraction. To build the knowledge graph from television data, I used this hierarchical community structure to define categories like type, model, and features. Additionally, I introduced a level for screen sizes to specifically respond to user requests for compact, standard, large, or extra-large televisions. This clear categorization improves usability and makes it easier to locate relevant information.
- Community Summaries: To enhance understanding of product categories or collections, I summarized structured data similarly to GraphRAG, highlighting important attributes and relationships. These community summaries provide concise overviews that improve the understanding of product categories or collections.
- Semantic Relationships: Conceptual understanding is enriched through semantic relationships between entities. By linking similar models such as screen sizes in televisions, an assistant can deliver more relevant and context-aware results for user queries.
These adapted points have enhanced the Knowledge Graph’s value by adding dynamic and insightful data, making it useful for both internal analysis and customer-facing applications.
Summary
GraphRAG principles enhance structured product data by applying hierarchical structuring and community summaries, improving data organization and usability. Semantic relationships enrich the graph, enabling more context-aware responses to user queries. While primarily designed for unstructured text, GraphRAG’s methodologies can also significantly enhance structured datasets.
