Building Knowledge Graphs: Traditional NER vs. LLMs
Knowledge graphs have become increasingly important for structuring information and enabling advanced querying and reasoning capabilities. But what’s the best way to construct them? In this post, we’ll compare traditional named entity recognition (NER) approaches with newer large language model (LLM) methods for knowledge graph construction.
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The Basics of Knowledge Graphs
A knowledge graph is a network of entities and their interconnections or relationships. They allow us to create structured, machine-readable information from unstructured text that can be used for reasoning and querying. Knowledge graphs power many applications including search engines, recommendation systems, and question answering.
Constructing a knowledge graph typically involves several steps:
- Processing and cleaning text data
- Extracting entities from the text
- Identifying relationships between entities
- Building the graph structure
- Curating and refining the graph
