How to Use SPARQL for Reasoning in Semantica: A Complete Guide

Semantica provides a dedicated SPARQLReasoner class that expands standard SPARQL queries with inference rules and post-processes results to return both explicit and inferred knowledge.

The semantica-agi/semantica repository implements a unified triplet-store architecture with a reasoning layer that sits between your SPARQL queries and the underlying RDF backend. This guide demonstrates how to leverage the SPARQLReasoner to perform rule-based inference using actual source implementations from the codebase.

Understanding the SPARQL Reasoning Architecture

Semantica’s reasoning pipeline consists of three primary components that work together to enable SPARQL-based inference.

Core Components

• SPARQLReasoner – Defined in semantica/reasoning/sparql_reasoner.py (class starts at line 51), this high-level engine handles query expansion and result inference.
• TripletStore – Located in semantica/triplet_store/triplet_store.py (line 40), this unified interface abstracts concrete RDF stores and provides the execute_query method (line 50) that forwards SPARQL to backends like Blazegraph, Jena, or Oxigraph.
• Reasoner – Implemented in semantica/reasoning/reasoner.py (lines 57-73), this generic rule engine handles forward/backward chaining and converts rules to SPARQL patterns.

The SPARQLReasoner maintains an internal instance of the generic Reasoner class for rule handling, while delegating actual query execution to the TripletStore.

Setting Up the Triplet Store for SPARQL Queries

Before reasoning can occur, you must initialize a TripletStore with a supported backend. The store validates the backend against SUPPORTED_BACKENDS and creates the concrete implementation via _initialize_store_backend.

from semantica.triplet_store import TripletStore

# Initialize a Blazegraph store

store = TripletStore(backend="blazegraph")

# Load data using store methods

kg = {
    "entities": [
        {"id": "Alice", "type": "Person"},
        {"id": "Bob", "type": "Person"},
    ],
    "relationships": [
        {"source": "Alice", "target": "Bob", "type": "knows"}
    ]
}
ontology = {"classes": [{"name": "Person"}], "properties": []}
store.store(knowledge_graph=kg, ontology=ontology)

The TripletStore.execute_query method (lines 50-55) serves as the gateway that sends SPARQL strings to the selected backend’s QueryEngine.

Configuring the SPARQLReasoner

Once the store contains data, create a SPARQLReasoner instance and optionally pass the store via the triplet_store configuration key.

Initializing the Reasoner

from semantica.reasoning.sparql_reasoner import SPARQLReasoner

reasoner = SPARQLReasoner(triplet_store=store)

The reasoner initializes with an empty query_cache dictionary (line 87) reserved for future optimization and creates an internal Reasoner instance for rule handling.

Defining Inference Rules

Add rules using the add_inference_rule method (internally delegated to reasoner.reasoner.add_rule). Rules follow an "IF ... THEN ..." syntax and are stored as Rule objects:

# Add a transitivity rule: Every Person is also an Agent

reasoner.add_inference_rule("IF ?x is_a Person THEN ?x is_a Agent")

Expanding SPARQL Queries with Inference Rules

The expand_query method (starting at line 92 in sparql_reasoner.py) parses your SPARQL query, converts each registered rule to a SPARQL pattern via _rule_to_sparql (lines 55-84), and appends these patterns as comments to the original query.

# Original query seeks Agents (none exist explicitly)

query = "SELECT ?x WHERE { ?x a :Agent }"

# Expand the query with inference patterns

expanded = reasoner.expand_query(query)
print(expanded)

# Output contains commented inference blocks:

#   # Inference: rule_1

#   ?x a :Agent .   # generated from the rule

This expansion encodes inference logic directly into the SPARQL string without modifying the original query semantics.

Executing Queries and Processing Results

With the expanded query, you execute against the store and optionally apply additional inference to the result set.

Running Expanded Queries Against the Store

The TripletStore.execute_query method forwards the SPARQL string to the backend’s QueryEngine, which handles parsing, optimization, and result materialization:

# Execute the expanded query

raw_res = store.execute_query(expanded)

# Results contain bindings for both explicit and inferred matches

print(raw_res.bindings)

Post-Processing with infer_results

For additional reasoning on the result set itself, use infer_results (line 91). This method applies the same rule set directly to the bindings, yielding additional inferred rows:

from semantica.reasoning.sparql_reasoner import SPARQLQueryResult

# Wrap raw results in SPARQLQueryResult dataclass (lines 42-48)

result_obj = SPARQLQueryResult(
    bindings=raw_res.bindings,
    variables=raw_res.variables,
    metadata=raw_res.metadata
)

# Apply rules to the result set

final_res = reasoner.infer_results(result_obj)
print(f"Inferred rows added: {final_res.metadata['inferred_count']}")

Complete End-to-End Example

This workflow demonstrates the full pipeline using an in-process Oxigraph backend:

from semantica.triplet_store import TripletStore
from semantica.reasoning.sparql_reasoner import SPARQLReasoner, SPARQLQueryResult

# 1. Initialize store with Oxigraph (no external server required)

store = TripletStore(backend="oxigraph")

# 2. Load knowledge graph

kg = {
    "entities": [
        {"id": "Alice", "type": "Person"},
        {"id": "Bob", "type": "Person"}
    ],
    "relationships": []
}
store.store(knowledge_graph=kg, ontology={"classes": [{"name": "Person"}]})

# 3. Create reasoner and add inference rule

reasoner = SPARQLReasoner(triplet_store=store)
reasoner.add_inference_rule("IF ?x is_a Person THEN ?x is_a Agent")

# 4. Expand query to include inference

query = "SELECT ?x WHERE { ?x a :Agent }"
expanded = reasoner.expand_query(query)

# 5. Execute and post-process

raw = store.execute_query(expanded)
result = reasoner.infer_results(
    SPARQLQueryResult(bindings=raw.bindings, variables=raw.variables)
)

# 6. Access inferred results

for binding in result.bindings:
    print(binding['?x']['value'])  # Outputs Alice and Bob URIs

Summary

• SPARQLReasoner in semantica/reasoning/sparql_reasoner.py provides the main interface for query expansion and result inference, starting at line 51.
• Use expand_query (line 92) to encode inference rules as SPARQL patterns before execution.
• The TripletStore.execute_query method (line 50) forwards queries to backends including Blazegraph, Jena, RDF4J, Anzo, and Oxigraph.
• Apply infer_results (line 91) to post-process result sets with the same rule set used during expansion.
• Rules follow "IF ... THEN ..." syntax and are managed by the internal Reasoner class defined in semantica/reasoning/reasoner.py.

Frequently Asked Questions

What backends does Semantica support for SPARQL reasoning?

Semantica supports Blazegraph, Apache Jena, RDF4J, Anzo, and Oxigraph through the TripletStore abstraction. The store validates against SUPPORTED_BACKENDS and dispatches queries via the backend-specific QueryEngine implementation.

How does the expand_query method work internally?

The expand_query method parses the incoming SPARQL query, iterates through registered rules, and calls _rule_to_sparql (lines 55-84) to convert each rule into a SPARQL graph pattern. These patterns are appended as commented blocks to the original query string, allowing the backend to execute both explicit and inferred patterns in a single operation.

Can I add multiple inference rules to a single query?

Yes. The SPARQLReasoner maintains a rule registry within its internal Reasoner instance. Multiple calls to add_inference_rule accumulate rules, and expand_query incorporates all registered rules into the expanded SPARQL output. Each rule generates a separate commented pattern block in the final query.

What is the difference between expand_query and infer_results?

expand_query modifies the SPARQL string itself to include rule patterns before execution, allowing the RDF backend to compute inferences during query processing. infer_results operates on the already-executed SPARQLQueryResult object, applying rules to the binding set to derive additional conclusions that may not have been materialized by the backend.

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