semantic-relations

What problem does it solve?

Semantic-relations helps you avoid confusing or mislabeling the meaning of connections between concepts, so your knowledge graph edges support correct hierarchy, disambiguation, and boundary decisions.

Core Features & Use Cases

• Relation-type coverage: Distinguishes taxonomic (IS-A), mereological (PART-OF), associative/lexical (synonymy, antonymy, polysemy, homonymy, metonymy), and thematic (agent, patient, instrument, cause, goal, etc.) relations.
• Relation properties discipline: Applies symmetry, asymmetry, transitivity, reflexivity, and irreflexivity to prevent direction and closure mistakes.
• Verification & anti-pattern detection: Uses a checklist to catch vague related_to edges, circular IS-A, IS-A vs PART-OF conflation, synonym sprawl, untyped polysemy, and property-free relation definitions.
• Boundary routing: Clarifies when the task belongs to adjacent skills (e.g., linguistics, ontology-modeling, taxonomy-design, entity-relationship-modeling) versus relation typing.

Quick Start

Ask the agent to audit a set of proposed graph edges and classify each edge into the correct semantic relation type, including which relation properties apply and which likely anti-patterns it matches.