Semantic Query Processing Engines

Updated 10 November 2025

Semantic Query Processing Engines are computational systems that utilize both classical relational methods and LLM-driven semantics to interpret and optimize queries over structured and unstructured data.
They combine ontology-driven mapping, semantic operator extensions, and hybrid execution layers to support natural language interaction with heterogeneous databases.
Evaluation methodologies and adaptive optimizations are employed to mitigate schema hallucination and balance quality, cost, and latency in real-world applications.

A semantic query processing engine is a computational system that augments or replaces conventional data query capabilities by leveraging structured semantics—ontologies, knowledge graphs, embeddings, or LLMs—to interpret, execute, and optimize queries over heterogeneous information sources, both structured (tables, RDF) and unstructured (text, images, audio). Such engines operate across a spectrum, from classical semantic web platforms grounded in ontological reasoning and SPARQL, to modern LLM-powered systems supporting direct natural language interaction with complex, multimodal and distributed databases.

1. Conceptual Foundations and Evolution of Semantic Query Processing

Semantic query engines distinguish themselves by their ability to interpret queries not only at the syntactic level but also at a semantic level, taking into account ontological definitions, entity relationships, and high-level intent (Madhu et al., 2011). Traditional search and database query processors focus predominantly on keyword matching or syntactic SQL constructs; in contrast, semantic engines perform mapping from user input—typically in natural language or semantically-rich query languages—into formal representations over knowledge bases or indexed assets.

Early generations were closely linked to the Semantic Web vision and emphasized ontology-driven data modeling (e.g., leveraging OWL, RDFS) and SPARQL for graph-based querying of RDF data (Ali et al., 2020, 0812.3788). Central features included term-to-ontology mapping, semantic expansion (via subsumption, synonymy, or relational closure), and reasoning for inferring implicit relationships.

The emergence of neural models, pre-trained embeddings, and LLMs has driven a shift toward operators that embed semantic filtering, joining, and aggregation as black-box, prompt-driven function calls—effectively blending statistical and symbolic methods (Lao et al., 3 Nov 2025, Hassini, 20 Oct 2025, Lee et al., 29 Aug 2025).

2. Core Architectural Patterns and Operators

At a high level, modern semantic query processing engines typically exhibit one or more of the following architectural strata:

Ontology-Driven Layer: Maintains class and property hierarchies, enables term disambiguation, reasoning, and mapping from user queries to the schema (e.g., via Map(Q,O) and Infer(O,G,…) (Madhu et al., 2011)).
Relational/Algebraic Layer: Implements classical operators (selection, projection, join, grouping, aggregation) potentially extended for semantic interpretation.
Semantic Operator Layer: Introduces LLM- or embedding-driven versions of classical operations (e.g., sem_filter, sem_join, sem_map, sem_rank, sem_classify (Lao et al., 3 Nov 2025); σ^sem, π^sem, ⋈^sem, γ^sem, δ^sem (Lee et al., 29 Aug 2025)).
Execution Layer: Orchestrates hybrid plans that interleave classical and semantic steps, including cost-based or rule-based optimization of execution order and resource allocation (Mittal et al., 5 Apr 2024, Hassini, 20 Oct 2025).
Caching and Middleware: Enables feature- and intent-aware semantic caches, ANN indexes for embeddings, and cross-session/intermediate result caches (Mahendru, 6 Jun 2024, Lao et al., 3 Nov 2025).

Core semantic operators generalize the relational algebra by allowing predicates and transformations to be expressed in natural language and executed via LLM inference, embeddings, or domain-specific models. For instance, a semantic join may answer "are these two records about the same topic" via prompt-based neural comparison rather than string- or key-based equality (Lee et al., 29 Aug 2025, Lao et al., 3 Nov 2025).

3. Query Language Support and Operator Semantics

A unifying feature of next-generation semantic engines is their extension of languages like SQL or SPARQL to encode semantic operators directly and natively (Lao et al., 3 Nov 2025, Lee et al., 29 Aug 2025, Mittal et al., 5 Apr 2024). For instance, SABER formalizes an algebra of semantic selection (σ^sem), projection (π^sem), join (⋈^sem), grouping (γ^sem), aggregation (ξ^sem), and deduplication (δ^sem) (Lee et al., 29 Aug 2025):

Semantic Selection: For predicate P (a prompt), σ^sem_P(r) selects tuples t in ρ(r) for which P(t) is true.
Semantic Join: For join predicate Q, r ⋈^sem_Q s forms the tuple pairs (t_r, t_s) such that Q(t_r, t_s) is deemed true by a predicate model (typically LLM- or embedding-based).
Semantic Grouping and Aggregation: γ^sem and ξ^sem enable clustering/grouping by demonstrative semantic similarity and aggregate computations (e.g., LLM-generated summary sentences).

In SQL-embedded systems such as SSQL (Mittal et al., 5 Apr 2024), dedicated keywords (e.g., SEMANTIC='q') query for embedding-based similarity; in SABER (Lee et al., 29 Aug 2025), UDFs such as SEM_WHERE, SEM_JOIN, and SEM_GROUP_BY can be dropped into syntactically valid queries and mapped to well-defined algebraic operators. In UQE (Dai et al., 23 Jun 2024), UQL allows any SELECT, WHERE, or GROUP BY clause to contain free-form NL predicates, which are interpreted by LLMs.

Table: Example Semantic Operators and Their Formalization

Operator	Formalization	Backend Task
sem_filter	sem_filter(ℓ: X→Bool): { t∈T	M(ℓ)(t)=1 }
sem_join	sem_join(ℓ: (X,Y)→Bool): { (t_i,t_j) : ... }	Pairwise LLM/embedding predicate
SEM_WHERE	WHERE SEM_WHERE('NL predicate', backend)	σ^sem with LLM/embedding as P
SEM_SELECT	SELECT ..., SEM_SELECT('NL expr', backend) AS ...	π^sem for LLM prompt-driven extract
SEM_GROUP_BY	GROUP BY SEM_GROUP_BY(attribute, k)	γ^sem, clustering with NL descriptors

Operators must be efficiently orchestrated to minimize LLM invocations and support pipeline compositionality. Fusion strategies, batching, and early LIMIT pushdown are used to amortize LLM or embedding costs (Lao et al., 3 Nov 2025, Hassini, 20 Oct 2025).

4. System Components: Planning, Optimization, and Failure Modes

Semantic query engines typically perform the following system-level coordination:

Schema Introspection and Linking: Automated extraction of table/column metadata and schema relationships, coupled with schema-to-query linking via embedding-based or LLM-driven planners (e.g., SILE in DynaQuery (Hassini, 20 Oct 2025)).
Cost- and Rule-Based Planning: Selection among execution strategies (e.g., applying relational predicates vs. semantic filtering first, or candidate pruning via embeddings before full LLM evaluation) to balance accuracy, cost, and latency (Mittal et al., 5 Apr 2024, Dai et al., 23 Jun 2024, Lao et al., 3 Nov 2025).
Failure Analysis: Key robustness metrics include schema hallucination rates (in which LLMs reference unavailable fields/tables), join mismatches, and select-column mismatches. For instance, DynaQuery's SILE architecture reduces schema hallucination from ~50.7% (RAG) to 6.76% on the BIRD benchmark (Hassini, 20 Oct 2025).
Human-in-the-Loop Calibration: Threshold selection for semantic queries is often performed via adaptive binary search and human feedback to set similarity levels that balance recall and precision in ambiguous contexts (Mittal et al., 5 Apr 2024).
Caching and Cross-Query Optimization: Semantic caches index query/intent/feature vectors and use ANN or clustering to minimize LLM usage for repeated or similar queries (Mahendru, 6 Jun 2024, Lao et al., 3 Nov 2025).

5. Evaluation Methodologies and Benchmarks

Benchmarking semantic engines requires measurement along multiple axes: correctness/accuracy, latency, LLM invocation cost, and coverage of query types and data modalities.

SemBench (Lao et al., 3 Nov 2025) introduces structured scenarios (movies, wildlife, e-commerce, MMQA, medical) and multi-modal operators (semantic filter, join, map, rank, classify), evaluating engines such as LOTUS, Palimpzest, ThalamusDB, and BigQuery using metrics such as F₁, Spearman’s correlation, and Adjusted Rand Index for classification/grouping.

Empirical findings include:

Scenario	Leading System(s)	Quality (max)	Cost (min)	Latency (min)
Movies	BigQuery/Thalamus	0.82	\$0.02	42s
E-commerce	LOTUS	0.75	\$0.22	103s
Wildlife	LOTUS/Image-only	0.95 (4/10)	-	-
MMQA	Palimpzest/LOTUS	0.88	\$1.41-1.62	218-243s

Cost-quality swings can be extreme (100x) depending on pushdown, operator fusion, and target modalities (Lao et al., 3 Nov 2025). Prompt design, operator fusion, caching, and adaptive optimization are necessary avenues for further increasing efficiency and quality.

Additional benchmarks address record-level quality (DCG, nDCG), aggregation accuracy (relative error), clustering fit for semantic caches (e.g., Silhouette on EK-OPTICS (Mahendru, 6 Jun 2024)), and entailment alignment (e.g., frequency and purity scores in scholarly KGs (Jia et al., 24 May 2024)).

6. Design Principles, Robustness and Future Directions

Semantic query engines must balance declarative expressiveness (e.g., full natural language support) and transparency with architectural robustness, cost control, and auditability. Current best practices include:

Hierarchy of Awareness: Progression from schema-awareness (robust schema linking), to semantics-awareness (data dictionaries, NL enrichment), to data-awareness (value-level alignment). DynaQuery demonstrates that moving up this hierarchy yields measurable increases in accuracy and reduces hallucination (Hassini, 20 Oct 2025).
Operator Compositionality and Reasoning: The algebraic framework in SABER and similar systems formally guarantees closure and compositionality under semantic operators, facilitating logical rewrites and extending cost-based optimization developed in classical relational engines (Lee et al., 29 Aug 2025).
Failure Mode Mitigation: Robustness against schema hallucination, context pruning, or cross-modal ambiguity is best achieved with dedicated schema-aware context construction, deterministic linking, and prompt auditing (Hassini, 20 Oct 2025, Lao et al., 3 Nov 2025).
Extensibility and Modularity: Community-defined semantic operators (as in SABER) and plug-and-play UDFs enable the architecture to evolve alongside advances in NLP/ML modeling (Lee et al., 29 Aug 2025).

Notable open challenges include scaling LLM-inference to very large datasets and query workloads, developing formal probabilistic models for prompt-based semantic operators, and automating prompt optimization and plan selection based on observed cost/quality tradeoffs (Lao et al., 3 Nov 2025, Lee et al., 29 Aug 2025).

Projected future work targets:

Cross-modal, multi-hop semantic reasoning over heterogeneous graphs (Lin, 8 Apr 2025)
Integration of mid-sized (3–7B parameter) LLMs for cost-accuracy tradeoffs in resource-constrained settings (Lin, 8 Apr 2025)
Enhanced query optimization using semantic selectivities and cost estimation (Mittal et al., 5 Apr 2024, Hassini, 20 Oct 2025)
Human-in-the-loop calibration and active learning for ambiguous semantic queries (Mittal et al., 5 Apr 2024, Lao et al., 3 Nov 2025)
Automated benchmark expansion and meta-optimization over prompt and operator design (Lao et al., 3 Nov 2025)

7. Comparative Summary

Semantic query processing engines represent a convergence of formal, algebraic database traditions and modern probabilistic, neural, and language-centric paradigms. Their key contributions are:

Unified Query Interface: Allowing expressive queries over arbitrarily heterogeneous and multimodal databases, often blending natural language predicates, structured constraints, and flexible grouping/aggregation (Hassini, 20 Oct 2025, Lee et al., 29 Aug 2025).
Robust Schema and Semantics Linking: Large, multi-relational and cross-modal databases require deterministic, robust schema-aware pipelines to prevent or nearly eliminate catastrophic contextual failures (e.g., schema hallucination) (Hassini, 20 Oct 2025).
Compositional Algebra: SABER demonstrates that a formally sound extension of relational algebra with semantic predicates and operators yields predictable, optimizable, and correct pipeline behavior (Lee et al., 29 Aug 2025).
Benchmark-Driven Progress: Evaluation frameworks such as SemBench drive empirical clarity on system trade-offs, highlight current weaknesses (cost, modality coverage, operator diversity), and orient development priorities (Lao et al., 3 Nov 2025).

In sum, the field is rapidly converging on architectures that treat LLM-driven semantics as first-class citizens within historically well-understood database operator frameworks, achieving new levels of expressive, robust, and explainable data access across the structured–unstructured divide.