WikiMonitor-onto: Ontology-aware Staleness Propagation for LLM-maintained Knowledge Bases

Manuscript submitted May 15, 2026; accepted June 26, 2026; published July 24, 2026

Abstract—Large Language Models (LLMs) are increasingly used to maintain persistent, domain-specific knowledge bases—a paradigm in which assertions must remain accurate as the field evolves. Existing staleness detection treats each assertion independently, missing a structural reality: when a foundational concept becomes outdated, every dependent concept inherits some degree of that staleness through ontology relationships. We present WikiMonitor-Onto, a lightweight propagation layer built on WikiMonitor that models staleness as a signal flowing through a domain ontology graph. We extract a concept graph of 642 nodes and 487 edges from 61 AI lecture documents, define three typed propagation relations (is-a, depends-on, related-to), and propagate staleness via weighted Breadth-First Search (BFS) with exponential hop decay. On a human-annotated gold standard of 62 concepts (25 indirect-stale, 37 fresh), the independent baseline detects zero indirect-stale concepts by construction, while WikiMonitor-Onto achieves precision 0.824 and recall 0.560 at the optimally tuned configuration (λ = 0.30). Grid search reveals that is-a and depends-on carry equal optimal propagation weight (both 0.90). A sensitivity analysis confirms that propagation is robust to the choice of seed-value distribution, with F1 varying by only 0.10 (0.571–0.667) across four tested distributions. Propagation saturates at hop depth 1 under conservative thresholds, suggesting that one-hop propagation suffices for high-precision deployment.

keywords—knowledge base maintenance, Large Language Model (LLM) wiki, ontology-aware staleness, Breadth-First Search (BFS) propagation, knowledge graph

Cite: Bailing Zhang,"WikiMonitor-onto: Ontology-aware Staleness Propagation for LLM-maintained Knowledge Bases," Journal of Advances in Artificial Intelligence, vol. 4, no. 3, pp. 152-163, 2026. doi: 10.18178/JAAI.2026.4.3.152-163

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).