Knowledge Is Not Static
A knowledge graph is a living system. New information arrives. Old information becomes outdated. Understanding evolves. Priorities shift.
Treating a knowledge graph as a finished product leads to decay. Treating it as an evolving system leads to compounding value. The difference is design for evolution.
Types of Evolution
Knowledge graphs evolve in several dimensions:
Content evolution: New documents added, existing documents updated, obsolete documents retired.
Schema evolution: New entity types, new relationship types, changed attributes, deprecated structures.
Quality evolution: Extracted entities improve, relationship accuracy increases, embeddings get better.
Structural evolution: Clusters form and dissolve, central nodes shift, new subgraphs emerge.
Usage evolution: Query patterns change, use cases expand, performance requirements grow.
Each dimension requires different strategies. Content evolution is continuous. Schema evolution is periodic. Quality evolution is gradual.
Content Lifecycle
Documents have lifecycles:
Creation: New content enters the system. Initial processing establishes its place.
Active use: Document is retrieved frequently, cited, builds connections.
Maturation: Document's insights get incorporated into other documents. Its direct importance may decrease.
Obsolescence: Document becomes outdated. Still historically interesting but not authoritative.
Archival: Document is no longer actively maintained but preserved for reference.
Deletion: Document is removed entirely (rare for knowledge systems).
Managing the lifecycle means:
- Freshness indicators showing when content was last verified
- Automated staleness detection
- Archival workflows that preserve access without polluting current retrieval
- Succession paths when new content supersedes old
Schema Evolution Patterns
Schemas change as understanding grows:
Additive changes: New entity types, new attributes, new relationship types. Backward compatible--existing data still works.
Rename changes: Entity type "project" becomes "initiative." Requires migration of existing data.
Merge changes: Two entity types combine into one. More complex migration.
Split changes: One entity type divides into multiple. Requires classifying existing entities.
Deprecation: An entity type or attribute is no longer used. Graceful transition period before removal.
Best practices:
- Version your schema
- Document changes with rationale
- Migrate incrementally when possible
- Keep deprecated structures readable while discouraging new usage
- Test migrations thoroughly before applying
Quality Improvement Cycles
Graph quality improves through systematic effort:
Entity refinement: Review extracted entities. Merge duplicates. Fix errors. Add missing entities.
Relationship validation: Audit discovered relationships. Remove false positives. Add missing links.
Embedding updates: When better models become available, re-embed content. Regenerate similarity-based links.
Feedback incorporation: When users flag errors or missing connections, fix them. Build feedback loops.
Coverage analysis: Identify gaps in the graph. Topics mentioned but not developed. Entities referenced but not characterized.
Quality work is never finished. Schedule periodic reviews. Track quality metrics over time.
Handling Conflicting Information
Knowledge isn't always consistent. Sources disagree. Facts change over time.
Temporal versioning: Track when information was true. "As of 2024, X was the approach. As of 2025, Y replaced it."
Source authority: Some sources are more reliable. Weight accordingly.
Conflict detection: Identify contradictory assertions. Surface for review.
Resolution policies: When conflicts are detected, how are they resolved? Most recent wins? Most authoritative source wins? Human arbitration?
Provenance tracking: For every assertion, know where it came from. Enables tracing when problems appear.
Pretending conflicts don't exist leads to confused outputs. Explicit handling leads to clearer reasoning.
Pruning and Consolidation
Not all evolution is growth. Sometimes shrinking improves the graph:
Redundancy removal: Multiple near-duplicate documents or entities. Merge into canonical versions.
Noise elimination: Low-value content that pollutes retrieval. Archive or remove.
Link pruning: Weak or erroneous relationships that mislead traversal.
Entity consolidation: Over-fragmented entities that should be unified.
Pruning is harder than adding. Addition is obviously valuable. Removal risks losing something useful. But unpruned graphs become unusable over time.
Establish pruning criteria. Apply consistently. Trust the process.
Migration Strategies
Major changes require migration:
Big bang migration: Switch everything at once. Simple but risky.
Parallel run: New system runs alongside old. Compare results. Cut over when confident.
Gradual migration: Move content incrementally. Query both systems during transition.
Lazy migration: Convert on access. Old format stored but new format served.
For personal knowledge systems, gradual migration usually works well. Low stakes, no external users, time to fix problems.
For systems with users or integrations, parallel run reduces risk.
Backup and Recovery
Evolution introduces risk. Good backup practices mitigate it:
Regular backups: Full database dumps at predictable intervals.
Point-in-time recovery: Transaction logs enabling recovery to any moment.
Document versioning: Keep history of document changes, not just current state.
Schema versioning: Track schema changes alongside content changes.
Test restores: Regularly verify that backups actually restore.
The backup you haven't tested is the backup that won't work when you need it.
Long-Term Sustainability
A knowledge graph should outlive any particular technology:
Format independence: Store content in portable formats (markdown, JSON) alongside specialized indices.
Export capabilities: Ability to dump the entire graph for migration.
Documentation: Record design decisions, schema rationale, processing pipelines.
Minimal dependencies: Avoid dependencies on services that might disappear.
The goal: if you had to rebuild on different infrastructure, you could.
The Compound Effect
Evolution compounds. Each improvement makes the graph more valuable. Higher quality leads to better retrieval. Better retrieval leads to better use. Better use generates feedback. Feedback drives improvement.
The knowledge graph that grows thoughtfully becomes more valuable over time. The one that accumulates without curation becomes less valuable.
Invest in evolution. The returns compound.