The process of tagging text or data with references to formal ontologies to make meaning machine-readable.
Semantic annotation is the process of enriching text, data, or media with machine-readable metadata that links content to formal ontologies, knowledge graphs, or controlled vocabularies. By attaching semantic tags — references to canonical URIs in shared ontologies — to words, phrases, or data fields, semantic annotation transforms unstructured or loosely structured content into knowledge that AI systems can reason over with precision.
Semantic annotation has become a critical step in enterprise AI data pipelines. Before documents can be indexed in knowledge graphs, used in RAG systems, or processed by AI agents, their entities and concepts must be linked to canonical representations. Automated semantic annotation — using NER, entity linking, and concept extraction — enables organizations to semantically enrich millions of documents at scale.
Semantic annotation combines several NLP techniques: Named Entity Recognition (identifying entity spans), Entity Linking (connecting entities to knowledge graph URIs), Concept Extraction (identifying domain concepts), and Relation Extraction (identifying relationships between entities). The output is an annotated document where each entity or concept is tagged with its canonical URI, enabling precise retrieval and reasoning.
A legal tech company semantically annotates contract documents. The phrase 'Apple Inc.' is linked to its Wikidata URI, 'force majeure' is linked to a legal ontology concept, and 'January 15, 2026' is tagged as a contract date. An AI agent can then answer 'Which contracts with Apple Inc. contain force majeure clauses expiring in 2026?' by querying the semantic annotations rather than parsing natural language.