ADR-006: Knowledge Layer Implementation with Knowledge Graph

Status: Accepted Version: 1.0 Date: 2025-10-01 Supersedes: N/A Related ADRs: N/A Related PRDs: PRD-006, PRD-007

Context

To provide an inferential projection of the DSL and a semantic grounding for AI agents.

Decision

Implement the Knowledge Layer using a Knowledge Graph (e.g., Oxigraph/RDF4J) adopting a SPARQL-first approach. Use RDF for data representation and SHACL for validation. OWL is supported for specific semantic modeling but heavy reasoning (inferences) is optional and secondary to explicit SPARQL querying.

Rationale

A Knowledge Graph provides a flexible and powerful way to represent complex relationships. A SPARQL-first approach ensures predictable query performance and standard access patterns, avoiding the unpredictability of heavy OWL reasoners at query time. Inferences should ideally be materialized (projected) into the graph rather than computed on-the-fly. SHACL ensures data quality and adherence to defined constraints.

Alternatives Considered

Relational Databases

Rejected - Limitations in representing complex, evolving relationships and performing graph-based inferences efficiently.

Document Databases

Rejected - Lack the formal semantic structure required for robust knowledge representation and reasoning.

Constraints

• MUST use SPARQL-first approach for queries
• MUST use RDF for data representation
• MUST use SHACL for validation
• MUST materialize inferences into the graph rather than computing on-the-fly
• MUST NOT rely on heavy OWL reasoners at query time

Quality Attributes

• Rich semantic context for AI
• Powerful querying capabilities
• Flexible data model
• Support for complex inferences
• Predictable query performance

Bounded Contexts Impacted

• Knowledge Layer
• Semantic Core
• Cognitive Extension Layer

Consequences

Positive

• Rich semantic context for AI
• Powerful querying capabilities
• Flexible data model
• Support for complex inferences

Negative

• Requires expertise in graph databases and semantic web technologies
• Potential performance considerations for very large graphs

Additional Notes

✅ MVP