Semantic Governance Paradigm of Heterogeneous World Models for Agentic AI Systems

Abstract

Agentic AI systems require world models that support reliable reasoning, planning, and decision-making under complex and heterogeneous conditions. Although heterogeneous graphs are natural candidates for this role, their structural design, particularly the selection of relational paths, is typically ad hoc and weakly governed from a semantic perspective, limiting robustness and interpretability. This paper proposes a semantic governance paradigm for heterogeneous world models, in which relational structures are explicitly constrained and validated at design time prior to learning. The paradigm is instantiated through Ontology-Driven Metapath Design (ODMD), which integrates ontological constraints, competency-based filtering, and lightweight predictive scoring to derive and select semantically admissible metapaths systematically. ODMD is integrated with a heterogeneous embedding pipeline, enabling the construction of governance-aware representations that combine node features, neighborhood aggregation, semantic information, and metapath-based encodings. An experimental evaluation on a multimodal heterogeneous graph shows that ontology-governed metapath design improves structural stability, semantic coherence, and robustness when compared to manual and brute-force alternatives. These results demonstrate that semantic governance provides a principled and practical foundation for agent-ready heterogeneous world models, supporting reusable, interpretable, and more reliable representations for proactive and autonomous AI systems.