Knowledge-Graph-Centric Architecture for Reliable Fault Diagnosis

Abstract

Building operations still rely heavily on manual review of unstructured maintenance records, which is slow, error-prone, and difficult to standardize across systems and teams. We present a knowledge-graph-centric middleware framework that mediates between heterogeneous IT/OT sources and analytics applications to support reliable, low-latency root-cause analysis. The framework integrates large language models (LLMs) with a domain knowledge graph (KG) under explicit quality criteria to ensure reliable construction and evolution. It begins with LLM-assisted schema generation, guided by Knowledge Graph Schema (KGS) metrics that ensure consistency, completeness, scalability, and minimal redundancy. Building on this foundation, the graph structure is refined using Graph Convolutional Network (GCN) embeddings combined with recursive KMeans clustering, which groups similar types and lowers query overhead. Next, a two-stage extraction pipeline converts unstructured text into a structured knowledge data frame and enriches relations by leveraging contextual proximity through weighted aggregation. To accommodate evolving data, the system supports incremental updates for both seen and unseen entities, preserving chronological consistency while avoiding disruptive schema revisions. Together, these components allow applications to remain decoupled from heterogeneous source formats while ensuring robust governance of both schema and data. The design further reduces query latency, improves retrieval precision, and reduces manual effort, thus supporting more timely and consistent maintenance decisions.