Integrating large language models and knowledge graphs for adaptive design review

From Rule Engines to Ontologies: An OWL 2 DL Approach for Domain-Specific Evaluation Information Systems

The results support the feasibility of ontology-driven generation for static-classification systems, whereas arithmetic risk computation and temporal event processing remain better suited to complementary procedural technologies.

Constructing Dynamic Master Logic Models as Knowledge Graphs for Complex System Diagnostics Using Retrieval-Augmented Large Language Models

Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements. However, DML construction typically relies on expert interpretation of technical documentation, limiting scalability for complex systems. This study presents a framework for automated construction of DML models from system descriptions and their representation as Knowledge Graphs (KG-DML), using Retrieval-Augmented Generation and Large Language Models as enabling tools. Building on prior work with small-scale systems, the framework extends automated KG-DML construction and evaluation to substantially larger and more complex systems. Model construction proceeds across the DML hierarchy using targeted retrieval while preserving functional dependencies and explicit logical relationships. The resulting KG-DML supports diagnostic reasoning, safety assessment, upward failure propagation, and downward dependency tracing. A multi-level validation methodology evaluates layer-specific precision and recall, logical gate consistency, and overall structural integrity. Application to the Low-Pressure Coolant Injection system of a decommissioned Boiling Water Reactor demonstrates consistent reconstruction across repeated runs. The results show that automated KG-DML construction can transform technical documentation into executable functional models for diagnostic and reliability analysis.

Intelligent Methods for Software Architecture and Business Logic Recovery: Current State and Prospects for Application

Structured Business Logic Injection for LLM-Based Code Generation: What a Design Document Must Carry

Large language models generate code effectively but falter in enterprise settings that demand complex business rules, particularly in rule consistency and conflict detection. This study proposes the Atomic Logic Sheet (ALS), a structured representation of business logic, injected through the Hierarchical Business Logic Injection (H-BLI) framework. Four conditions were compared on a warehouse management domain, 20 runs each: requirements only; a natural-language design document; the same content with worked examples, anti-patterns, and an explicit conflict-handling directive in prose; and that content expressed in ALS. Conflict responses were adjudicated by two independent raters (κ = 0.993 and 1.000) and cross-checked against a byte-level code comparison. Logic compliance converged near 97% whenever a design document was supplied; conflict detection separated them: 10.0% and 43.1% for the requirements-only and plain-document conditions, and 100% for both content-bearing conditions, neither of which modified code. The two notations detected conflicts identically, and where they differed, the prose condition was better, so the effect follows the content a document is obliged to carry rather than its notation. A replication under a second model reproduced the gap between the requirements-only baseline and the full treatment, and a replication on independently designed conflicts reproduced the detection result for the document conditions; neither included the prose condition, so neither reproduces the comparison between notations. Schema deviation, however, was higher under ALS. As a single-domain study, replication elsewhere remains necessary.

LLM-Based Formalization of Engineering Requirements into Ontology-Constrained Knowledge Graphs

Results indicate that ontology-constrained LLM pipelines can support the formalization of engineering requirements into semantically explicit graph representations that are suitable for downstream querying, validation, and analysis.

Symbolic Logic Validation of LLM Interactions in Critical Systems

This paper proposes a validation framework that combines the flexibility of LLMs with the logic reasoning capabilities of Answer Set Programming as a complementary layer to existing guardrail mechanisms and demonstrates the practical implementation through a modular architecture that supports customizable validation components.