How to Implement Change Management and Conflict Resolution Workflows for Knowledge Graphs in Semantica

Semantica provides a built-in pipeline for building, versioning, and reconciling knowledge graphs through GraphBuilder, ConflictDetector, and SourceTracker components.

Knowledge graphs derived from multiple data sources inevitably face schema drift, entity duplication, and conflicting property values. The Semantica framework treats change management and conflict resolution as first-class architectural concerns, enabling teams to track graph evolution, detect inconsistencies automatically, and resolve disputes with full audit trails.

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Core Components for Graph Change Management

Semantica's workflow orchestration centers on four interconnected modules:

Component	Primary Role	Source Path
GraphBuilder	Orchestrates extraction, entity resolution, temporal graph construction, and snapshotting	semantica/kg/graph_builder.py
ConflictDetector	Analyzes entities and relationships for four conflict categories	semantica/conflicts/conflict_detector.py
SourceTracker	Maintains provenance for every property value contributing to conflicts	semantica/provenance/manager.py
ProgressTracker	Emits fine-grained progress events for monitoring long-running jobs	semantica/utils/progress_tracker.py

These modules enable end-to-end change management workflows that preserve historical states while surfacing data quality issues for resolution.

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Configuring Change Management in GraphBuilder

The GraphBuilder class in semantica/kg/graph_builder.py centralizes all change-management capabilities. Enable temporal support, versioning, and history tracking through constructor parameters:

from semantica.kg import GraphBuilder

builder = GraphBuilder(
    merge_entities=True,
    resolve_conflicts=True,
    enable_temporal=True,
    track_history=True,
    version_snapshots=True,
    temporal_granularity="day",
)

Key parameters for change management workflows:

• enable_temporal – Activates time-aware graph construction
• version_snapshots – Enables point-in-time graph captures
• track_history – Preserves modification history for auditability
• temporal_granularity – Defines resolution ("day", "hour", "minute")

Creating and Querying Temporal Snapshots

After building your graph, create immutable snapshots for version control:

from datetime import datetime

# Build from source data

graph = builder.build(sources=my_sources)

# Create a point-in-time snapshot

snapshot = builder.create_temporal_snapshot(timestamp=datetime.utcnow())

# Query historical state

historic_graph = builder.query_temporal(at=datetime(2024, 5, 1))

Snapshots integrate with any GraphStore implementation. Configure persistence via the graph_store attribute:

builder.graph_store = MyNeo4jStore(uri="bolt://localhost:7687")
builder.graph_store.save(graph)

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Conflict Detection Categories

The ConflictDetector class in semantica/conflicts/conflict_detector.py implements four distinct conflict resolution workflows:

Category	Detection Method	Trigger Scenario
Value conflicts	detect_value_conflicts()	Same property holds different values across sources
Type conflicts	detect_type_conflicts()	Entity identifier annotated with incompatible types
Temporal conflicts	detect_temporal_conflicts()	Inconsistent timestamps (e.g., two founded_year values)
Logical conflicts	detect_logical_conflicts()	Semantically incompatible type combinations

Each detection method returns structured Conflict objects containing severity levels, confidence scores, and source provenance.

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Step-by-Step Conflict Resolution Workflow

Step 1: Access the Detector

The conflict detector initializes automatically when resolve_conflicts=True:

conflict_detector = builder.conflict_detector

Step 2: Run Targeted Detection

Execute blanket scans or targeted queries:

# Detect all conflict types across entities

all_conflicts = conflict_detector.detect_conflicts(entities)

# Detect only value conflicts for specific property

name_conflicts = conflict_detector.detect_conflicts(
    entities, method="value", property_name="name"
)

Step 3: Inspect Conflict Metadata

Each Conflict object exposes diagnostic fields:

for c in all_conflicts:
    print(f"{c.conflict_id}: {c.severity} – {c.recommended_action}")
    print(f"  Confidence: {c.confidence}")
    print(f"  Sources: {[s.document_id for s in c.sources]}")

Step 4: Choose Resolution Strategy

Automatic resolution applies heuristics (e.g., most-recent-source-wins):

conflict_detector = ConflictDetector(auto_resolve=True)

Manual review surfaces conflicts to domain experts via the sources list:

critical = [c for c in all_conflicts if c.severity == "critical"]

# Present c.sources in analyst UI for confirmation

Step 5: Integrate Resolved Data

Filter or reconcile entities before rebuilding:

critical_ids = {c.entity_id for c in critical}
clean_entities = [
    e for e in raw_entities
    if e["id"] not in critical_ids
]

graph = builder.build(sources=[{
    "entities": clean_entities,
    "relationships": raw_rels
}])

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Provenance Tracking with SourceTracker

Every conflict links to its origin through SourceTracker (accessed via conflicts.source_tracker and implemented in semantica/provenance/manager.py). This enables audit trails for regulatory compliance and debugging:

• Original document identifier
• Page or section location
• Extraction confidence score
• Timestamp of ingestion

The provenance graph is queryable for impact analysis—identify which source documents contributed to specific entity properties.

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Complete Implementation Example

The following demonstrates end-to-end change management and conflict resolution:

from datetime import datetime
from semantica.kg import GraphBuilder

# ----------------------------------------------------------------------

# 1. Configure builder with full change management

# ----------------------------------------------------------------------

builder = GraphBuilder(
    merge_entities=True,
    resolve_conflicts=True,
    enable_temporal=True,
    track_history=True,
    version_snapshots=True,
    temporal_granularity="day",
    conflict_detection={"auto_resolve": False},  # manual review

)

# ----------------------------------------------------------------------

# 2. Supply raw sources with intentional conflicts

# ----------------------------------------------------------------------

sources = [
    {
        "entities": [
            {"id": "E1", "name": "Acme Corp", "type": "Organization", "source": "docA"},
            {"id": "E1", "name": "Acme Corp.", "type": "Company", "source": "docB"},
        ],
        "relationships": [
            {"source": "E1", "target": "E2", "type": "EMPLOYS"},
        ],
    },
]

# ----------------------------------------------------------------------

# 3. Build with automatic conflict detection

# ----------------------------------------------------------------------

graph = builder.build(sources=sources)

# ----------------------------------------------------------------------

# 4. Review generated conflict report

# ----------------------------------------------------------------------

conflicts = builder.conflict_detector.get_conflict_report()
for c in conflicts["conflicts"]:
    print(
        f"{c['conflict_id']} – {c['severity']} – "
        f"{c['recommended_action']} – sources: {c['sources']}"
    )

# ----------------------------------------------------------------------

# 5. Create versioned snapshot

# ----------------------------------------------------------------------

snapshot = builder.create_temporal_snapshot(timestamp=datetime.utcnow())

This workflow emits progress events through ProgressTracker, enabling real-time monitoring in CI pipelines or dashboard UIs.

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Key Source Files Reference

File	Responsibility
semantica/kg/graph_builder.py	Orchestration, temporal construction, snapshotting
semantica/conflicts/conflict_detector.py	Detection algorithms and conflict object generation
semantica/provenance/manager.py	Provenance tracking and audit trails
semantica/utils/progress_tracker.py	Progress event emission for monitoring
semantica/kg/graph_store/neo4j_store.py	Example persistence backend

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Summary

• Change management in Semantica relies on GraphBuilder flags (enable_temporal, version_snapshots, track_history) to preserve graph evolution
• Conflict resolution workflows use ConflictDetector to categorize and surface value, type, temporal, and logical inconsistencies
• Provenance tracking via SourceTracker ensures every conflict is traceable to source documents
• Flexible resolution supports both automatic heuristics and manual analyst review
• Progress monitoring through ProgressTracker integrates with operational tooling

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Frequently Asked Questions

What conflict types does Semantica detect automatically?

Semantica detects four conflict categories: value conflicts (differing property values), type conflicts (incompatible entity types), temporal conflicts (inconsistent timestamps), and logical conflicts (semantically impossible type combinations). Each category has a dedicated detection method in conflict_detector.py.

How does Semantica track which source documents caused a conflict?

The SourceTracker class in semantica/provenance/manager.py records provenance for every property value, including document identifier, location, confidence score, and ingestion timestamp. This metadata attaches directly to Conflict objects for auditability.

Can I disable automatic conflict resolution and require manual review?

Yes. Set conflict_detection={"auto_resolve": False} in GraphBuilder or instantiate ConflictDetector(auto_resolve=False). This surfaces all conflicts through get_conflict_report() for analyst review rather than applying resolution heuristics.

What storage backends support temporal snapshots?

Any GraphStore implementation can persist snapshots. Semantica provides reference implementations including Neo4jStore in semantica/kg/graph_store/neo4j_store.py. The snapshot mechanism is storage-agnostic—backends receive the serialized graph state at the specified timestamp.

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