Towards Cyber-Physical Cognition: A Unified Ontology-Driven Knowledge Graph for Real-Time Autonomous Grid Operations

A Knowledge-Enhanced LLM Framework for Power Semantic Understanding and Multi-Agent Decision Support

A knowledge-enhanced LLM framework for power semantic understanding and multi-agent decision learning that consistently improves semantic grounding accuracy, restoration quality, and feasible-decision rate over rule-based, text-centric RAG, and graph-retrieval multi-agent baselines.

Semantic Graph Unification for Industrial Digital Threads: Bridging 11 Heterogeneous Manufacturing Systems Through Ontology-Driven Knowledge Graphs

Modern manufacturing enterprises operate heterogeneous systems -- ERP, MES, PLM, SCADA, QMS, SCM -- each with its own data model and API. The resulting silos prevent holistic analysis, delay root-cause investigation, and obstruct Industry 4.0 traceability. Point-to-point integration scales as O(n^2) and accumulates brittle dependencies. This paper presents an open framework for semantic graph unification of industrial digital threads. An ontology-driven RDF knowledge graph unifies data from 11 simulated sources across nine domains through a five-stage ETL pipeline with automated entity resolution spanning 97 owl:sameAs identity links. The ontology encompasses 78 RDFS classes, 108 object properties, and 243 data properties, drawing on ISA-95, OPC UA, eClass, the Asset Administration Shell, RAMI 4.0, and additional standards. An automated discovery engine applies nine strategy categories -- cross-station correlation, alarm coverage, ECN impact, CUSUM/EWMA drift detection -- to surface insights spanning system boundaries. The primary empirical result: blocking 24 cross-system tools reduces recall from 1.00 to 0.31 (F1 from 1.00 to 0.48), showing that 69% of discoverable signals require cross-system graph joins. Leave-one-out ablation confirms six of nine strategies contribute unique signals. Verification against a 65-signal manifest (16 positive, 49 null) yields F1 = 1.00 (95% Clopper-Pearson CI [0.79, 1.00]); as the manifest was author-constructed, this constitutes verification not independent validation. The graph is exposed to LLM agents via 287 Model Context Protocol tools as a SPARQL-native semantic layer. Five industry templates (aerospace, CPG, pharma, medical devices, turbine blades) demonstrate schema stability across manufacturing verticals.

TEMPORAL EVENT-DRIVEN SCHEDULED ACTIONS (TESA) ONTOLOGY FOR CYBER PHYSICAL SYSTEMS WITH PRIORITY MECHANISM

From Context-Aware Collaborative IoT to Digital Twins: Architectural Foundations and Research Roadmap for Distributed Intelligent Systems and Systems of Systems

The increasing deployment of Internet of Things (IoT) infrastructures in smart environments is enabling the continuous generation of heterogeneous data from devices, services and stakeholders, facilitating the interconnection between physical and digital entities. However, many current solutions still process such data in a fragmented manner, with limited support for cross-domain contextualization, real-time reasoning, and distributed intelligence. In parallel, Digital Twins (DTs) emerge as a promising paradigm for representing and managing complex smart systems, yet many existing approaches still lack a clear path from context-aware IoT data processing to operational, adaptive, and collaborative twins. This paper presents a research roadmap from Collaborative IoT and context awareness toward DTs and Systems of Systems (SoS) of DTs, grounded in our previous work on context-aware service architectures, homogeneous context representation, real-time event correlation, and distributed processing across the computing continuum. The roadmap is structured around three complementary directions: context-aware models for adaptive and intelligent DT behavior, composability, collaboration and hybrid intelligence in systems and SoS of DTs, and servitization and augmentation in DT ecosystems. The paper also identifies ongoing research efforts and the key challenges that must be addressed to enable the practical realization of this roadmap.

Future Generation Computer Systems

Ontology-driven billing engine for multi-dimensional electricity market settlement

The transition toward new power systems with high penetration of renewable generation and distributed energy resources introduces unprecedented settlement complexity, characterized by multi-domain coupling across transmission, distribution, and retail; multi-entity coordination involving virtual power plants and storage aggregators; multi-variety product superposition spanning energy, ancillary services, and green certificates; and multi-scale temporal nesting from real-time to annual settlement. Traditional hard-coded billing architectures exhibit fundamental limitations in semantic consistency and rule extensibility under such operational paradigms. This paper presents an ontology-driven billing engine architecture to address these challenges. A formalized seven-domain ontology model encompassing Entity–Resource–Contract– Measurement–Price–Bill–Rule is established, achieving semantic standardization and logical decoupling of settlement elements. A three-layer logic flow orchestration mechanism (Object–Logic–Operation) supports visual assembly and dynamic loading of business logic. Furthermore, a meta-model-driven dynamic execution engine with a vectorized compute kernel and a business-semantic-level full-link observer is developed, forming a closed loop from design mode to runtime mode. Engineering validation in a provincial-level electricity marketing system demonstrates multiple increase in settlement throughput and a 93.3% reduction in full-process latency, confirming the efficacy of the ontological approach for large-scale new power system market operations.