Knowledge Mining

Knowledge Mining

Use When

• Use when the goal is deep reusable knowledge, not just one report.
• Use after source verification and before knowledge productization.
• Use when building claim libraries, evidence maps, dossiers, reusable briefs, or refreshable intelligence assets.

Do Not Use When

• The project is a one-off deliverable with no reuse path.
• The corpus has not yet passed verification.

Knowledge Mining Required Context

• Verified sources, claim graph, audience needs, target products, and reuse constraints.
• Sensitivity, confidentiality, and commercial boundaries.

Knowledge Mining Core Method Notes

1. Ingest the corpus by source, entity, event, issue, geography, and time.
2. Extract reusable claims and link them to evidence.
3. Build entity-event-issue maps.
4. Identify reusable frameworks, templates, visuals, and briefs.
5. Mark sensitivity and reuse limits.
6. Define refresh cadence and change triggers.
7. Hand the asset portfolio to productization.

Quality Standards

• Every reusable asset keeps provenance.
• Reuse boundaries are explicit.
• Knowledge assets have buyers, users, or decisions attached.
• Refresh logic is defined for time-sensitive claims.

Knowledge Mining Existing Failure Notes

• Treating knowledge mining as folder organization.
• Extracting insights without evidence links.
• Reusing client-sensitive knowledge as generic IP.
• Creating a knowledge base with no refresh trigger.

Knowledge Mining Core Deliverables

• Knowledge asset inventory.
• Claim library.
• Entity-event-issue map.
• Reusable brief set.
• Refresh cadence.

Evidence Produced

References

• Load references/knowledge-mining-workflow.md for extraction and asset rules.

Inputs

Capability contract

Read and corpus-search access are required for extraction. Updating a claim library or ontology needs explicit authority; original sources and their identifiers must not be altered.

Degraded mode

If the corpus or extraction tooling is unavailable, return a qualified mining schema and gap register, with identity resolution and coverage checks marked unassessed.

Decision rules

Outputs

Knowledge Mining Evidence Notes 1

• Preserve extraction provenance, entity-resolution decisions, contradiction status, refresh dates, and coverage gaps.

Worked example

Extract one organisation mention as an entity node, connect dated actions as events, and keep conflicting accounts as separate claims until verified.

Companion Skills

• evidence-claim-graph supplies the traceable substrate.
• knowledge-productization packages assets into offers.
• analytical-report-shapes chooses reusable output shapes.

Workflow

1. Define the reusable asset types and extraction schema.
2. Extract entities, events, issues, and claims with source identifiers.
3. Stop promotion when a claim lacks verified evidence or conflicts remain unresolved.
4. Validate the register; recover by preserving the item as a gap or labelled inference.

Knowledge Mining Evidence Notes 2

Anti-Patterns

• Extracting claims without source identifiers. Fix: attach provenance at capture.
• Collapsing conflicting claims. Fix: preserve both and record the contradiction.
• Treating an entity mention as verified identity. Fix: resolve and corroborate it.
• Creating assets with no downstream use. Fix: name the consumer and decision.
• Hiding stale or missing evidence. Fix: record refresh status and gaps.

Reference Index

• Knowledge-mining workflow