An edge label should state a specific relation, direction, domain, range, and relevant constraints. A generic 'related to' edge hides the reasoning the graph is meant to support.

Relations Need Declared Semantics

Subjects, roles, resources, purposes, and grants form an authorization graph. Retrieval should enforce allowed paths before content enters model context, not only before final display.

Authorization Can Be Evaluated as a Graph

A causal edge claims that intervening on one variable changes another under stated assumptions. Correlation or sequence alone does not justify that edge.

Causal Graphs Assert More Than Association

A graph neural network aggregates features across related nodes to learn representations or predictions. Its output depends on edge semantics, neighborhood sampling, depth, and training labels.

Graph Neural Networks Learn From Neighborhood Structure

Lexical search captures exact terms, vectors capture semantic resemblance, and graph traversal captures structured relationships. Fusion should be evaluated against the task and preserve each signal's provenance.

Hybrid Retrieval Combines Lexical, Vector, and Graph Signals

An ontology declares concepts, relations, constraints, and sometimes inference rules for a domain. It should be as small as the decisions it must support and evolve under version control.

Ontologies Define Shared Graph Meaning

Graph retrieval expands from seed entities across selected relation types and depths. Each hop increases context and risk, so traversal needs semantic filters, budgets, and path evidence.

Relation-Aware Retrieval Traversal

A subject–predicate–object triple asserts one relationship between identified things. Multiple triples can describe a richer situation while keeping each claim independently attributable.

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