Semantic Search
Semantic search augments keyword matching with semantic representations (embeddings) and retrieves content by meaning rather than literal terms. It leverages vector similarity, knowledge graphs and ranking signals to improve relevance for documents, chatbots and product search. Successful adoption requires data preparation, model choice and evaluation metrics.
This block bundles baseline information, context, and relations as a neutral reference in the model.
Definition · Framing · Trade-offs · Examples
What is this view?
This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.
Baseline data
Context in the model
Structural placement
Where this block lives in the structure.
No structure path available.
Relations
Connected blocks
Directly linked content elements.