LLM-Assisted Ontology Engineering and Construction of a French Legal Knowledge Graph

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

Requirements engineering plays a central role in mechanical design, yet technical requirements remain predominantly expressed in natural language, limiting traceability, validation, and computational reasoning. This work presents an ontology-constrained pipeline for transforming natural-language engineering requirements into Industrial Ontologies Foundry (IOF)-grounded knowledge graphs enriched with QUDT-based quantitative semantics. The pipeline decomposes text blocks into individual prescriptive clauses, extracts structural slots and constraint atoms through a typed intermediate representation, normalizes quantitative expressions via QUDT unit and quantity-kind grounding, and instantiates IOF-compliant OWL ABox graphs. The transformation is implemented as a hybrid neuro-symbolic workflow that combines Large Language Models (LLMs) with typed intermediate representations, rule-based post-processing, and description-logic reasoning. Evaluation on a Formula SAE (FSAE) rules corpus, intentionally selected to stress quantitative constraint handling, shows good structural reliability in the evaluated setting. Slot-level extraction achieved a macro accuracy of 94.50%, while quantitative constraint identification reached 97.64% precision and 96.88% recall. Normalization coverage was 98.78%, with residual errors primarily attributable to quantity-kind disambiguation. At the graph level, 93.33% of grounded artifacts passed all ontology-conformance checks, with residual violations concentrated in requirement–specification linkage and specification typing rather than in quantitative-value modeling or systematic misuse of the IOF backbone. These 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.

GrOIL: Graph-Grounded Domain Ontology Induction with Constrained LLM Mediation

Constructing formal ontologies from domain documents requires simultaneously enforcing corpus grounding, vocabulary consistency, axiom-level expressivity, and end-to-end provenance, a combination no existing automatic system delivers. We present a seven-stage graph-grounded pipeline that converts domain documents into a complete, auditable Web Ontology Language (OWL) Terminological Box (TBox) without any unconstrained generation step. Documents are first encoded as Unified Discourse-Hypergraphs (UDH) capturing entity participation and discourse dependencies; subsequent stages transform this graph evidence into a class hierarchy, typed object and datatype properties, and restriction axioms, with Large Language Model (LLM) usage restricted to narrow, graph-grounded mediation tasks. A paired Assertional Box (ABox) population procedure grounds named individuals in the induced TBox, enabling SPARQL-based functional evaluation. Every emitted term carries a full decision chain from raw source passages through each pipeline stage, making the TBox directly auditable and suitable for targeted human refinement. Evaluated on the life insurance domain using two established benchmarks and a new 100-contract corpus spanning ten product types, our pipeline achieves strong results across all evaluation dimensions, outperforming direct and multi-agent LLM baselines on competency-question (CQ) coverage (0.85 vs. 0.63 and 0.62 on one term-life contract; 0.77 vs. 0.40 and 0.44 on another contract), while also attaining high keyphrase coverage comparable to a manually-constructed reference ontology and strong performance on structured gap-and-overlap reasoning, all without any manual TBox engineering. Ontology growth analysis provides evidence consistent with vocabulary saturation at scale, demonstrating that the pipeline produces stable, reusable domain representations from large document corpora.

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

Domain-specific information systems often maintain their data model, rule base, and application infrastructure as separate artifacts, complicating maintenance and pre-deployment verification. This study investigates whether these artifacts can be unified in a verifiable ontology-to-code pipeline without changing the expected classifications. The proposed Model-Driven Architecture uses the Business Application Builder framework and a Web Ontology Language 2 Description Logic ontology to represent domain structure, classification rules, and generation metadata. HermiT verifies consistency, satisfiability, and subsumption under open-world semantics before code generation. The generator produces persistence, business-logic, data-transfer, and presentation layers, while the generated Java application evaluates stored records under closed-world semantics and resolves overlapping categories using ontology-declared priorities. In a Serbian research-evaluation case study, the generated system reproduced the M30 and M33 classifications of an established Jess implementation. An internal secondary experiment generated and executed a prenatal-diagnosis application; all six runtime classifications matched the HermiT entailments and expected outcomes. The public artifact independently reproduces the ontology-level experiments but excludes the proprietary generator and generated source code. 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. No performance superiority is claimed.

DeLTA: A Description Logic-based Annotation Schema for Constructing Expressive OWL DL Axioms from Text

An Ontology-Guided, Deduplication-Aware Extraction Layer for Knowledge Graph Construction from Heterogeneous Documents

A production extraction layer that converts a live document stream into a validated knowledge graph aligned to a formal ontology, and improved search recall from roughly 70 to 95 percent with no false merges, and corrected seven classes of silent quality defect.

A Web-Based Pipeline for Ontology-Free Biomedical Knowledge Graph Construction and Exploration

The exponential growth of biomedical regulatory documentation has created an urgent need for automated, scalable knowledge structuring mechanisms that do not rely on rigid, expert-curated ontologies. This paper presents the system implementation and empirical analysis of the Ontology-Free Biomedical Knowledge Graph Induction (OF-Bio-KG) framework, focusing on three dimensions: (i) the end-to-end computational pipeline, (ii) the structural and statistical properties of the induced knowledge graph, and (iii) a proposed crowd-sourced expert evaluation strategy intended for future deployment. The OF-Bio-KG framework operates on a corpus of 1,068 European Medicines Agency (EMA) pharmaceutical product leaflets totalling over 131 million tokens. Using the nomic-embed-text model for long-context semantic encoding, a Balanced K-Means Constrained clustering algorithm for thematic organization, and LLaMA-3 8B for ontology-free relational inference, the system produces a multi-level knowledge graph comprising 256,148 leaf nodes, 3,742 cluster-level concept nodes, over 1.6 million intra-cluster edges, and 147,000 inter-cluster edges, all persisted in Neo4j. A central challenge in this work is the absence of a groundtruth biomedical knowledge graph against which the induced graph can be evaluated using standard metrics. To address this, we propose a human-in-the-loop validation strategy planned for future deployment: a purpose-built web application will be made available to Master's students at ENSIT, who will act as domain-proficient annotators to explore and validate graph components. This paper describes the current system, its empirical results, and the design of this forthcoming evaluation platform.