sqpeTHE FIELD GUIDE / FIRST EDITION · OCT 2026Query data.
Query data.
Understand meaning.
A field guide to Semantic Query Processing Engines.
Explore the ideas, systems, and research bringing AI reasoning into the data stack.
↳ Primary sources, clearly cited↳ Academia + industry
INSIDE A SEMANTIC QUERYFIG. 01
“Find papers that challenge
the conventional approach.”
the conventional approach.”
σ
Describe the intentDeclarative semantic operators
QUERY↓
⋈
Find a better planBalance quality · cost · latency
OPTIMIZER↓
▤ SQL engine
⌕ Retrieval
✧ Models
EXECUTE → EVALUATE → REFINECONCEPTUAL VIEW
A GROWING MAP OF THE FIELD
22research records
3industry platforms
06core operator concepts
01 / FIND YOUR STARTING POINT
One field. Many ways in.
From the first semantic filter to the next query optimizer.
01 / THE FOUNDATIONS↗
Understand the ideas.
What makes a query semantic? Learn the operators, architecture, and trade-offs that define an SQPE.
Read the field guide →
02 / THE LANDSCAPE↗
Explore the systems.
Navigate research engines and industry platforms. Compare interfaces, approaches, and evidence.
Browse the systems →
03 / THE LITERATURE↗
Follow the research.
A searchable library of foundational papers and emerging work, with context and primary sources.
Open the library →
02 / ON THE READING DESK
All research ↗The ideas shaping the field.
FOUNDATIONAL READING / 2024What if meaning were
Explore LOTUS →What if meaning were
a database operator?
LOTUS makes natural-language operations part of a declarative data model—and asks how to execute them efficiently.
03 / EVIDENCE BEFORE HYPE
A useful map needs a clear legend.
This is a curated first edition, not an exhaustive survey or a performance leaderboard. How we curate →
- Research claims link to the original publication.
- Vendor documentation is labeled separately.
- Record review does not mean independent reproduction.
- Corrections and additions are welcome on GitHub.