semantica/docs/reference/reasoning.md

title, description, icon

title	description	icon
Reasoning Module	Forward chaining, Rete, deductive, abductive, SPARQL, Datalog, and temporal reasoning with explainable inference paths.	microchip

semantica.reasoning derives new knowledge from existing facts using logical rules:

• Six reasoning engines: forward chaining, Rete, SPARQL, Datalog, temporal, and LLM-powered GraphReasoner
• Every engine produces explainable inference paths: traceable chains of rules and facts
• DatalogReasoner guarantees termination via semi-naive fixpoint evaluation
• TemporalReasoningEngine implements all 13 Allen interval algebra relations
• ExplanationGenerator produces step-by-step natural-language justifications

Exported Classes

Class	Role
Reasoner	Forward-chaining inference: add_fact, add_rule, forward_chain, backward_chain, infer_facts
GraphReasoner	LLM-powered reasoning over a KG dict: answers natural language queries via reason(graph, query)
ReteEngine	Rete pattern matching: build_network, add_fact, match_patterns, execute_matches
SPARQLReasoner	Rule-based SPARQL query expansion via execute_query, expand_query, infer_results
DatalogReasoner	Recursive Horn clause rules with semi-naive fixpoint: add_fact, add_rule, derive_all, query
TemporalReasoningEngine	All 13 Allen interval algebra relations: relation(a, b), overlaps, contains, active_at
ExplanationGenerator	Step-by-step explanations via generate_explanation(inference_result)
Rule	IF/THEN rule: {rule_id, name, conditions, conclusion, rule_type, confidence, priority}
Fact	Working-memory fact: {fact_id, predicate, arguments}
InferenceResult	Single derived conclusion: {conclusion, rule_used, premises, confidence}

Which Engine Should I Use?

• Reasoner — IF/THEN rules, forward and backward chaining. Start here: covers 90% of use cases. No query language required.
• GraphReasoner — Natural language queries over a knowledge graph via LLM. No SPARQL or rules: just ask a question.
• DatalogReasoner — Recursive Horn clause rules with guaranteed termination. Use for complex multi-hop transitive rules.
• ReteEngine — Rete pattern matching for high-frequency inference. Use when you need to match many facts against many rules simultaneously.
• SPARQLReasoner — SPARQL query expansion and rule-based inference. Use when you're working with RDF/OWL data.
• TemporalReasoningEngine — All 13 Allen interval algebra relations. Use for time-aware reasoning: overlaps, before/after, during, contains.

Getting Started

The most common pattern is the Reasoner for IF/THEN forward-chaining:

from semantica.reasoning import Reasoner, Rule, RuleType

reasoner = Reasoner()

# Add facts as strings in predicate(args) form
reasoner.add_fact("Manager(Alice)")
reasoner.add_fact("Employee(Alice)")

# Add an IF-THEN rule using the string form
reasoner.add_rule("IF Manager(?x) THEN HasAuthority(?x)")

# Run forward chaining: returns List[InferenceResult]
results = reasoner.forward_chain()
for r in results:
    print(r.conclusion)         # "HasAuthority(Alice)"
    print(r.confidence)         # 1.0
    if r.rule_used:
        print(r.rule_used.name) # name of the rule applied

Or build rules programmatically using the Rule dataclass:

from semantica.reasoning import Rule, RuleType

rule = Rule(
    rule_id="rule_001",
    name="manager_authority",
    conditions=["Manager(?x)"],
    conclusion="HasAuthority(?x)",
    rule_type=RuleType.IMPLICATION,
    confidence=0.9,
)
reasoner.add_rule(rule)

Reasoner (Forward/Backward Chaining)

Reasoner is the unified entry point for rule-based inference: iterates facts and rules to a fixpoint, then optionally proves a specific goal via backward chaining:

from semantica.reasoning import Reasoner, Rule, RuleType, InferenceResult

reasoner = Reasoner()

# Facts can be strings, KG entity dicts, or KG relationship dicts
reasoner.add_fact("Manager(John)")
reasoner.add_fact("Employee(John)")

# IF-THEN string form
reasoner.add_rule("IF Manager(?x) AND Employee(?x) THEN SeniorStaff(?x)")

# Forward chaining: iterates until fixpoint
results = reasoner.forward_chain()
for r in results:
    print(r.conclusion)   # e.g. "SeniorStaff(John)"
    print(r.premises)     # list of premise strings matched
    print(r.confidence)   # float

# Backward chaining: prove a specific goal
result = reasoner.backward_chain("SeniorStaff(John)", max_depth=10)
if result:
    print(f"Proven: {result.conclusion}")
    print(f"Premises: {result.premises}")

# infer_facts() loads facts and rules in one call, returns conclusion strings
conclusions = reasoner.infer_facts(
    facts=["Manager(Alice)", "Employee(Alice)"],
    rules=["IF Manager(?x) THEN HasAuthority(?x)"],
)
# → ["HasAuthority(Alice)"]

Reasoner Methods

Method	Returns	Description
add_fact(fact)	None	Add a string, entity dict, or relationship dict to working memory
add_rule(rule)	Rule	Add a Rule object or IF-THEN string; rules are sorted by priority descending
forward_chain()	List[InferenceResult]	Derive all possible conclusions iteratively until fixpoint
backward_chain(goal, max_depth)	InferenceResult | None	Prove a specific goal string, returns None if unprovable
infer_facts(facts, rules)	List[str]	Load facts and rules then run forward_chain(), returns conclusion strings
clear()	None	Clear all facts and rules
reset()	None	Alias for clear()

Rule and Fact dataclass fields

from semantica.reasoning import Rule, Fact, RuleType

# Rule: all fields
rule = Rule(
    rule_id="rule_001",            # required: unique identifier
    name="manager_authority",      # required: display name
    conditions=["Manager(?x)"],    # list of condition strings
    conclusion="HasAuthority(?x)", # conclusion string
    rule_type=RuleType.IMPLICATION, # IMPLICATION | EQUIVALENCE | CONSTRAINT | TRANSFORMATION
    confidence=1.0,                 # default 1.0
    priority=0,                     # higher priority rules run first
)

# Fact: for working with the Rete engine directly
from semantica.reasoning import Fact
fact = Fact(
    fact_id="f001",                # required: unique identifier
    predicate="Manager",
    arguments=["John"],
    metadata={},
)

GraphReasoner

GraphReasoner uses an LLM to answer natural language queries over a knowledge graph dict: no SPARQL or rule authoring required:

from semantica.reasoning import GraphReasoner

# Initialize: uses openai by default; override via kwargs
reasoner = GraphReasoner(provider="openai", model="gpt-4o-mini")

kg = {
    "entities": [
        {"id": "alice",   "name": "Alice",   "type": "Person",       "properties": {"role": "CEO"}},
        {"id": "acme",    "name": "Acme Inc", "type": "Organization"},
    ],
    "relationships": [
        {"source": "alice", "target": "acme", "type": "leads"}
    ],
}

answer: str = reasoner.reason(
    graph=kg,
    query="Who leads Acme Inc. and what is their role?"
)
print(answer)

reason() converts the graph to a text context and calls the LLM with a structured prompt. Returns a plain string answer.

ReteEngine

High-performance Rete pattern matching for large rule sets:

from semantica.reasoning import ReteEngine, Rule, Fact, RuleType

engine = ReteEngine()

# Build the Rete network from a list of Rule objects
rules = [
    Rule(
        rule_id="r1",
        name="manager_authority",
        conditions=["Manager(?x)"],
        conclusion="HasAuthority(?x)",
    )
]
engine.build_network(rules)

# Add facts to working memory
engine.add_fact(Fact(fact_id="f1", predicate="Manager", arguments=["Alice"]))

# Match patterns and execute
matches = engine.match_patterns()
results = engine.execute_matches(matches)
# results is a list of conclusion values from matched rules

# Network statistics
stats = engine.get_network_stats()
# → {"total_nodes": N, "alpha_nodes": A, "beta_nodes": B, "terminal_nodes": T, "facts": F}

engine.reset()

ReteEngine Methods

Method	Returns	Description
build_network(rules)	None	Build the Rete network from a list of Rule objects
add_fact(fact)	None	Add a Fact to working memory and propagate through the network
match_patterns(facts)	List[Match]	Match all patterns; optionally add facts before matching
execute_matches(matches)	List[Any]	Execute matched rules and return their conclusion values
reset()	None	Clear facts and all node activation state
get_network_stats()	dict	Return counts of alpha, beta, terminal nodes and facts

SPARQLReasoner

SPARQLReasoner extends SPARQL with inference rule expansion: add IF-THEN rules and they are automatically woven into queries before execution:

from semantica.reasoning import SPARQLReasoner

reasoner = SPARQLReasoner()

# Add an inference rule (IF-THEN string form)
reasoner.add_inference_rule("IF is_a(?x, Manager) THEN has_authority(?x)")

# Execute a query: returns SPARQLQueryResult
result = reasoner.execute_query("""
    PREFIX ex: <http://example.org/>
    SELECT ?person ?company WHERE {
        ?person ex:founded ?company .
        ?company ex:located_in ex:SiliconValley .
    }
""")

for row in result.bindings:
    print(row)   # each row is a dict of variable → {"value": ..., "type": ...}

# Expand a query with inference rules (returns modified query string)
expanded = reasoner.expand_query("SELECT ?x WHERE { ?x a :Manager }")

# Infer additional bindings from existing results
enriched = reasoner.infer_results(result)

SPARQLReasoner Constructor

SPARQLReasoner(
    config=None,         # optional config dict
    triplet_store=None,  # optional TripletStore instance for live query execution
    enable_inference=True,
)

`execute_query()` returns empty bindings when no `triplet_store` is configured. Pass a `TripletStore` instance via the `triplet_store=` kwarg to execute queries against a live backend.

DatalogReasoner

Pure-Python bottom-up semi-naive fixpoint evaluation for recursive Horn clause rules. Termination is guaranteed: the engine detects fixpoint convergence and stops:

from semantica.reasoning import DatalogReasoner, DatalogFact

datalog = DatalogReasoner()

# Add base facts: string form is the simplest
datalog.add_fact("parent(alice, bob)")
datalog.add_fact("parent(bob, charlie)")

# Or use DatalogFact directly (args is a tuple of strings)
datalog.add_fact(DatalogFact(predicate="parent", args=("charlie", "dave")))

# Add recursive rules using Horn clause syntax
datalog.add_rule("ancestor(X, Y) :- parent(X, Y).")
datalog.add_rule("ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z).")

# Evaluate to fixpoint: returns all derived fact strings
all_facts = datalog.derive_all()
# e.g. ["parent(alice, bob)", "parent(bob, charlie)", ..., "ancestor(alice, bob)", ...]

# Query with variable pattern: variables start with uppercase or ?
results = datalog.query("ancestor(alice, ?Z)")
# → [{"Z": "bob"}, {"Z": "charlie"}, {"Z": "dave"}]

# Clear and start over
datalog.clear()

DatalogFact, DatalogRule fields

from semantica.reasoning import DatalogFact, DatalogRule

# DatalogFact: ground fact; args must all be constants (lowercase start)
fact = DatalogFact(predicate="parent", args=("alice", "bob"))

# DatalogRule: parsed from string; head and body are set by the parser
# Use add_rule("head(X, Y) :- body(X, Z), body2(Z, Y)."): do not construct directly

DatalogReasoner Methods

Method	Returns	Description
add_fact(fact)	None	Add string, dict, or DatalogFact; constants must be lowercase-starting
add_rule(rule_str)	None	Parse and add a Horn clause string like "ancestor(X,Y) :- parent(X,Y)."
derive_all()	List[str]	Run semi-naive fixpoint evaluation; returns all facts as strings
query(pattern)	List[dict]	Query derived facts: auto-runs derive_all() if needed
load_from_graph(graph)	int	Load a ContextGraph's nodes/edges as Datalog facts; returns count added
clear()	None	Clear all facts and rules

TemporalReasoningEngine

Pure-Python Allen interval algebra: all 13 relations, no LLM calls:

from datetime import datetime
from semantica.reasoning import TemporalReasoningEngine, TemporalInterval, IntervalRelation

engine = TemporalReasoningEngine()

ceo_tenure   = TemporalInterval(start=datetime(1997, 9, 16), end=datetime(2011, 8, 24))
board_member = TemporalInterval(start=datetime(2000, 1,  1), end=datetime(2012, 6,  1))

# Compute Allen relation: method is relation(), not get_relation()
rel = engine.relation(ceo_tenure, board_member)
# → IntervalRelation.DURING  (ceo_tenure is fully inside board_member)

# Other helpers
engine.overlaps(ceo_tenure, board_member)   # bool
engine.contains(board_member, ceo_tenure)   # bool

# Is a given point in time inside an interval?
engine.active_at(ceo_tenure, datetime(2005, 6, 1))   # True

All 13 Allen interval algebra relations:

Relation	Meaning
BEFORE	A ends before B starts
MEETS	A ends exactly when B starts
OVERLAPS	A starts before B, ends inside B
DURING	A is fully inside B
STARTS	A and B start together, A ends first
FINISHES	A and B end together, A starts later
EQUALS	Identical intervals
AFTER	Inverse of BEFORE
MET_BY	Inverse of MEETS
OVERLAPPED_BY	Inverse of OVERLAPS
CONTAINS	Inverse of DURING
STARTED_BY	Inverse of STARTS
FINISHED_BY	Inverse of FINISHES

`TemporalInterval.start` expects a `datetime` object, not a string. Import `datetime` from the standard library and construct intervals with `datetime(year, month, day)`.

ExplanationGenerator

Generate structured explanations for any InferenceResult:

from semantica.reasoning import ExplanationGenerator, Reasoner, Rule

reasoner = Reasoner()
reasoner.add_fact("Manager(John)")
reasoner.add_rule("IF Manager(?x) THEN HasAuthority(?x)")
results = reasoner.forward_chain()

# ExplanationGenerator takes no positional args
generator = ExplanationGenerator()

# Pass an InferenceResult object: not a dict
explanation = generator.generate_explanation(results[0])

print(f"Type:       {explanation.explanation_type}")   # "inference"
print(f"Conclusion: {explanation.conclusion}")
print(f"NL:         {explanation.natural_language}")

if explanation.reasoning_path:
    for step in explanation.reasoning_path.steps:
        print(f"  Step {step.step_id}: {step.description}")
        if step.rule_applied:
            print(f"    Rule: {step.rule_applied.name}")

# Justify a conclusion with a reasoning path
path = generator.show_reasoning_path(results[0])
justification = generator.justify_conclusion(results[0].conclusion, path)
print(justification.explanation_text)

ExplanationGenerator Methods

Method	Returns	Description
generate_explanation(reasoning)	Explanation	Generate structured explanation for an InferenceResult, Proof, or abductive result
show_reasoning_path(reasoning)	ReasoningPath	Extract and return the reasoning path from any result
justify_conclusion(conclusion, path)	Justification	Build a Justification with evidence and NL text for a conclusion

Key dataclass fields

# Explanation
explanation.explanation_id    # str
explanation.explanation_type  # "inference" | "proof" | "abductive" | "generic"
explanation.conclusion        # conclusion value
explanation.reasoning_path    # ReasoningPath | None
explanation.natural_language  # NL string (when generate_nl=True, the default)

# ReasoningStep
step.step_id        # str
step.description    # str
step.rule_applied   # Rule | None  (NOT rule_name)
step.input_facts    # List[Any]
step.output_fact    # Any
step.confidence     # float

Engine Selection Guide

Engine	Best For	Termination	Key Method
Reasoner	Simple IF/THEN rules	Always (with max_iterations cap)	forward_chain()
GraphReasoner	NL queries over a KG via LLM	Always	reason(graph, query)
ReteEngine	Large rule sets with many facts	Always	match_patterns()
SPARQLReasoner	Rule-augmented SPARQL queries	Always	execute_query()
DatalogReasoner	Recursive rules (ancestry, reachability)	Guaranteed fixpoint	derive_all() / query()
TemporalReasoningEngine	Time interval relationships	Always	relation(a, b)

For recursive rules (e.g. ancestor, reachability, transitivity), use `DatalogReasoner`: it guarantees termination via semi-naive bottom-up fixpoint evaluation. `Reasoner.forward_chain()` has a `max_iterations` cap (default 50) and will silently stop early with deep recursion. `GraphReasoner` requires a configured LLM provider. If the provider fails to initialize, `reason()` returns an error string instead of raising. Check `reasoner.provider is not None` before calling if you need to surface failures explicitly.

• Knowledge Graph — The knowledge graph being reasoned over.
• Ontology — Ontology axioms and SHACL constraints for logical reasoning.
• Triplet Store — RDF backend for SPARQL-based reasoning.
• Context — Reasoning integrated into agent decision intelligence.