Relation Extraction

Relation extraction is the task of automatically identifying relationships between entities in unstructured text. Given a sentence like "Alice Smith manages the data team at Acme Corp," relation extraction identifies the relationships (Alice Smith, MANAGES, data team) and (Alice Smith, WORKS_AT, Acme Corp).

Relation extraction is essential for building knowledge graphs from text. There are three main approaches: rule-based extraction using dependency parsing (fast but limited, ~60% accuracy), supervised machine learning models trained on labeled relationship data, and LLM-based extraction using prompts that instruct the model to output structured triples (most flexible, 85-90% accuracy but slower and more expensive).

In production knowledge graph pipelines, relation extraction typically works in concert with named entity recognition (NER) and entity linking. First NER identifies entities, then relation extraction identifies how they connect, and finally entity linking resolves different mentions to canonical entities. The quality of relation extraction directly determines the quality and completeness of the resulting knowledge graph.