Data, Knowledge, Context, and Semantic Engineering
Separate data fitness, reusable knowledge, semantic meaning, and task specific context so failures can be located and corrected.
A focused view of boundaries, contracts, state, authority, failure paths, and tradeoffs drawn from this chapter.
Reconstruct and defend this chapter’s architecture.
Reconstruct the architecture, name each boundary, and defend the tradeoffs.
Open the source exercise
Design a knowledge and context path for a multi-repository security change. Include source registration, missing-data checks, lexical and vector retrieval, entity resolution, reranking, permission filters, contradiction handling, context budgeting, citations, and four distinct evaluation layers. Introduce an obsolete policy document that ranks highly and show how the system detects it.
4. Tradeoffs and alternatives
Centralizing knowledge simplifies governance and discovery but can create a stale copy of systems that already have authoritative APIs. Live retrieval preserves currentness but increases latency and dependency risk. A hybrid approach may index discovery metadata while resolving consequential facts from the source at decision time.
Fine-grained chunking improves targeted retrieval but can remove necessary context. Large chunks preserve narrative structure but consume budget and blur ranking. Knowledge graphs improve explicit traversal and lineage at the cost of modeling, ingestion, and consistency work.
An ontology is valuable when several sources repeatedly disagree about meaning. It is premature when a small controlled vocabulary and stable identifiers solve the actual problem. Semantic engineering should remove measured ambiguity, not create a speculative enterprise model of everything.
5. Current Mission Control Implementation
At study commit
d902fae,
Mission Control has provenance-backed retrieval, graph relationships, planning,
Attempt-bound Context Packages, context evaluations, configuration drift scans,
versioned context manifests, and content-hash checks. Factory Memory is
advisory and cannot satisfy acceptance.
These mechanisms provide a strong context-governance foundation. The studied evidence does not establish a complete production source registry, connector and checkpoint lifecycle, data-quality gate, semantic-contract registry, permission-aware hybrid retrieval service, or independently benchmarked reranking pipeline. The graph and memory mechanisms should therefore not be presented as a general enterprise knowledge system.
Review this chapter.
Challenge a claim, boundary, missing failure mode, unclear term, or unsupported evidence statement.
- Claim
- Boundary
- Failure
- Evidence