How to use large language models and knowledge graphs to manage enterprise data
In recent years, knowledge graphs have become an important tool for organizing and accessing large volumes of enterprise data in diverse industries — from healthcare to industrial, to banking and insurance, to retail and more.
A knowledge graph is a graph-based database that represents knowledge in a structured and semantically rich format. This could be generated by extracting entities and relationships from structured or unstructured data, such as text from documents. A key requirement for maintaining data quality in a knowledge graph is to base it on standard ontology. Having a standardized ontology often involves the cost of incorporating this ontology in the software development cycle.
Organizations can take a systematic approach to generating a knowledge graph by first ingesting a standard ontology (like insurance risk) and using a large language model (LLM) like GPT-3 to create a script to generate and populate a graph database.
The second step is to use an LLM as an intermediate layer to take natural language text inputs and create queries on the graph to return knowledge. The creation and search queries can be customized to the platform in which the graph is stored — such as Neo4j, AWS Neptune or Azure Cosmos DB.