What is knowledge graph?
A knowledge graph is a graph-based database in which knowledge is represented in an organised and semantically rich fashion. This can be produced by extracting entities and relationships from structured or unstructured data, such as text from documents.
How the companies uses LLM for creating Knowledge graph?
Organisations can develop a knowledge graph in a systematic manner by first ingesting a standard ontology (such as insurance risk) and then using a large language model (LLM) such as GPT-3 to generate and populate a graph database. The second stage is to utilise an LLM as an intermediate layer to take natural language text inputs and construct graph queries to return knowledge. The creation and search queries can be tailored to the graph’s storage platform, such as Neo4j, AWS Neptune, or Azure Cosmos DB.
Which kind of approaches that used in knowledge graph through LLM?
1: Investigating ontology and discovering entities and relationships
2: Creating a text prompt for LLM to construct ontology schema and databases.
3: Developing a query to generate data
4: Using the query to generate a knowledge graph
The capacity to update the knowledge graph when new data becomes available is another feature of this technique. The Cypher query can be amended to include additional nodes and edges, and then the updated query can be ingested into the graph database to incorporate the new data. This makes it easy to manage the knowledge graph and keep it current and relevant.