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155 lines
3.9 KiB
Markdown
155 lines
3.9 KiB
Markdown
# Reasoning Module
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Perform logical inference and reasoning on knowledge graphs using rule-based and deductive reasoning engines with support for forward/backward chaining.
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## Overview
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- **Rule-Based Reasoning**: Apply logical rules to derive new facts
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- **Deductive Reasoning**: Infer conclusions from premises
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- **Forward Chaining**: Data-driven reasoning
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- **Backward Chaining**: Goal-driven reasoning
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- **SWRL Support**: Semantic Web Rule Language
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---
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## Algorithms Used
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### Inference Algorithms
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- **Forward Chaining (Rete Algorithm)**: Efficient pattern matching, O(RFP) complexity where R=rules, F=facts, P=patterns
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- **Backward Chaining**: Goal-directed reasoning with SLD resolution
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- **Tableau Algorithm**: Description Logic reasoning
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- **Resolution**: First-order logic inference
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### Rule Matching
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- **Rete Network**: Compiled rule network for efficient matching
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- **Pattern Matching**: Unification algorithm for variable binding
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- **Conflict Resolution**: Priority-based rule selection
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---
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## Main Classes
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### InferenceEngine
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**Methods:**
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| Method | Description | Algorithm |
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|--------|-------------|-----------|
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| `forward_chain(kg, rules)` | Forward chaining inference | Rete algorithm |
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| `backward_chain(kg, goal)` | Backward chaining | SLD resolution |
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| `infer(kg, rules)` | General inference | Auto-select forward/backward |
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| `apply_rules(facts, rules)` | Apply rule set | Pattern matching + unification |
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| `explain_inference(fact)` | Explain derivation | Proof tree generation |
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**Example:**
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```python
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from semantica.reasoning import InferenceEngine, RuleManager
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engine = InferenceEngine(
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strategy="forward", # forward, backward, hybrid
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max_iterations=100,
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explain_inferences=True
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)
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rule_manager = RuleManager()
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rule_manager.add_rule(
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"IF ?x foundedBy ?y THEN ?y founder_of ?x"
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)
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# Forward chaining
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new_facts = engine.forward_chain(kg, rule_manager)
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print(f"Inferred {len(new_facts)} new facts")
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# Explain inference
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for fact in new_facts[:5]:
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explanation = engine.explain_inference(fact)
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print(f"{fact}: {explanation}")
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```
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---
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### RuleManager
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**Methods:**
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| Method | Description | Algorithm |
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|--------|-------------|-----------|
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| `add_rule(rule)` | Add inference rule | Rule parsing + validation |
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| `remove_rule(rule_id)` | Remove rule | Rule deletion |
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| `load_rules(filename)` | Load rules from file | SWRL/custom format parsing |
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| `validate_rules()` | Validate rule set | Consistency checking |
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| `compile_rules()` | Compile to Rete network | Rete compilation |
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**Rule Syntax:**
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```
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IF <condition> THEN <conclusion>
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Examples:
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IF ?x type Person AND ?x worksFor ?y THEN ?y employs ?x
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IF ?x foundedBy ?y AND ?y type Person THEN ?x type Organization
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```
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**Example:**
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```python
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from semantica.reasoning import RuleManager
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rules = RuleManager()
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# Add rules
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rules.add_rule("IF ?x type Company AND ?x foundedBy ?y THEN ?y founder_of ?x")
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rules.add_rule("IF ?x founder_of ?y AND ?y type Company THEN ?x type Entrepreneur")
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# Load from file
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rules.load_rules("rules.swrl")
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# Validate
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is_valid, errors = rules.validate_rules()
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```
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---
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### DeductiveReasoner
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**Methods:**
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| Method | Description | Algorithm |
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|--------|-------------|-----------|
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| `reason(kg, axioms)` | Perform deductive reasoning | Tableau algorithm |
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| `check_entailment(kg, statement)` | Check if statement is entailed | Subsumption testing |
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| `find_inconsistencies(kg)` | Find logical contradictions | Consistency checking |
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| `classify(kg)` | Compute class hierarchy | Classification algorithm |
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---
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## Configuration
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```yaml
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# config.yaml - Reasoning Configuration
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reasoning:
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inference:
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strategy: forward # forward, backward, hybrid
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max_iterations: 100
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explain_inferences: true
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rules:
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format: swrl # swrl, custom
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validate_on_load: true
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deductive:
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reasoner: hermit # hermit, pellet
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check_consistency: true
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```
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---
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## See Also
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- [Knowledge Graph Module](kg.md)
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- [Ontology Module](ontology.md)
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