How to Query Knowledge Graphs Using SPARQLReasoner with RDF Backends

Instantiate SPARQLReasoner with an RDF triplet store (e.g., BlazegraphStore), register inference rules via add_inference_rule, and call execute_query to run SPARQL with automatic query expansion and result inference.

The semantica-agi/semantica repository provides a high-level interface for semantic reasoning over RDF data. The SPARQLReasoner class serves as the primary entry point for querying knowledge graphs using SPARQL while integrating rule-based inference capabilities across various RDF backends.

Architecture Overview

SPARQLReasoner orchestrates query execution through three main components:

• SPARQLReasoner: The high-level orchestrator that manages query expansion, execution, inference, and caching. Implemented in [semantica/reasoning/sparql_reasoner.py](https://github.com/semantica-agi/semantica/blob/main/semantica/reasoning/sparql_reasoner.py#L51).
• Reasoner: Handles inference rules and provides forward/backward chaining capabilities. Located in [semantica/reasoning/reasoner.py](https://github.com/semantica-agi/semantica/blob/main/semantica/reasoning/reasoner.py#L57).
• Triplet Store: The concrete RDF backend (Blazegraph, Jena, RDF4J) that executes SPARQL queries. Injected via the triplet_store parameter.

Initializing the RDF Backend

Before creating a SPARQLReasoner, you must instantiate a compatible RDF triplet store. The repository supports multiple backends through a common TripletStore interface.

For example, to connect to Blazegraph:

from semantica.triplet_store import BlazegraphStore

store = BlazegraphStore(
    endpoint="http://localhost:9999/blazegraph/sparql",
    namespace="mykg"
)

Other supported implementations include JenaStore, RDF4JStore, and OxigraphStore, all located in the semantica/triplet_store/ directory.

Configuring SPARQLReasoner

Create the reasoner instance by injecting the store and enabling inference:

from semantica.reasoning import SPARQLReasoner

reasoner = SPARQLReasoner(
    triplet_store=store,
    enable_inference=True
)

The enable_inference flag activates rule-based query expansion and result enrichment.

Adding Inference Rules

Register IF-THEN rules to expand the knowledge graph during query execution:

rule_def = "IF ?x is_a Person AND ?x is_a Human THEN ?x is_a Mammal"
reasoner.add_inference_rule(rule_def)

Internally, Reasoner._parse_rule_definition processes these strings and stores them in self.reasoner.rules.

Executing Queries

The execute_query method follows a sophisticated pipeline:

1. Cache Check: Verifies self.query_cache for existing results.
2. Query Expansion: Calls expand_query (lines 89-146 in sparql_reasoner.py) to append inference patterns.
3. Backend Execution: Dispatches SPARQL to the triplet store.
4. Result Inference: Applies infer_results (lines 88-124) to enrich bindings using the rule set.
5. Caching: Stores the final SPARQLQueryResult dataclass (defined at lines 42-48) before returning.

raw_query = """
    SELECT ?s ?p ?o
    WHERE { ?s ?p ?o }
    LIMIT 10
"""

result = reasoner.execute_query(raw_query)

print("Variables:", result.variables)
for binding in result.bindings:
    print(binding)

Complete Implementation Example

Here is a full workflow from initialization to result inspection:

from semantica.triplet_store import BlazegraphStore
from semantica.reasoning import SPARQLReasoner

# 1. Initialize RDF backend

store = BlazegraphStore(
    endpoint="http://localhost:9999/blazegraph/sparql",
    namespace="mykg"
)

# 2. Create reasoner with inference enabled

reasoner = SPARQLReasoner(
    triplet_store=store,
    enable_inference=True
)

# 3. Add inference rule

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

# 4. Execute SPARQL query

query = "SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10"
result = reasoner.execute_query(query)

# 5. Inspect results

for binding in result.bindings:
    print(binding)

# 6. Clear cache if needed

reasoner.clear_cache()

Summary

• SPARQLReasoner in semantica/reasoning/sparql_reasoner.py provides the main interface for SPARQL-based reasoning.
• Any RDF backend implementing the TripletStore interface can be injected via the triplet_store parameter.
• Query execution involves caching, expansion via expand_query, backend execution, and inference via infer_results.
• Results are returned as SPARQLQueryResult dataclasses containing variables, bindings, and metadata.
• Progress tracking events are emitted throughout the pipeline (lines 100-110 and 150-158) for observability.

Frequently Asked Questions

What RDF backends are supported by SPARQLReasoner?

The system supports any store implementing the TripletStore interface. Reference implementations include BlazegraphStore, JenaStore, RDF4JStore, and OxigraphStore. You can swap backends by changing the import and instantiation while keeping the same SPARQLReasoner configuration.

How does query caching work in SPARQLReasoner?

The reasoner maintains an in-memory query_cache dictionary. Before executing a query, execute_query checks this cache. If results exist, they are returned immediately. Otherwise, the query proceeds through expansion and execution, with the final SPARQLQueryResult stored in the cache before return. Use clear_cache() to invalidate entries.

Can I use SPARQLReasoner without inference?

Yes. Set enable_inference=False when instantiating SPARQLReasoner. In this mode, the reasoner skips the expand_query and infer_results steps, passing SPARQL directly to the triplet store without rule-based modification.

How are IF-THEN inference rules parsed?

Rules are parsed by Reasoner._parse_rule_definition in semantica/reasoning/reasoner.py. The parser converts IF-THEN strings into internal rule objects stored in self.reasoner.rules. These rules drive both query expansion (modifying the SPARQL pattern) and result inference (enriching returned bindings).

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