Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks

Jonathon Dilworth; Pedro Giesteira Cotovio; David Herron; Paul Cripps; Nigel Dewdney; Catia Pesquita; Ernesto Jiménez-Ruiz

cs.AIAug 6, 2026

Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks

Authors: Jonathon Dilworth, Pedro Giesteira Cotovio, David Herron, Paul Cripps, Nigel Dewdney, Catia Pesquita, Ernesto Jiménez-Ruiz

Organizations: City St George’s, University of London, London, UK · LASIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal · Defence Science and Technology Laboratory, UK · The Alan Turing Institute, UK

Abstract

The use of ontologies and knowledge graphs is becoming increasingly widespread in the defence and national security domain. Numerous ontologies have been developed through initiatives led by academia, industry, and government. Achieving interoperability across diverse defence and national security ontologies remains a major challenge due to the domain's breadth and specialisation. In this work, we analyse and document over 60 publicly available ontologies and introduce a new track for the Ontology Alignment Evaluation Initiative (OAEI). This track comprises eight matching tasks, consensus alignments and manually-curated (silver-standard) mappings. The consensus alignments are derived by aggregating the outputs of several state-of-the-art ontology alignment systems. The silver-standard is obtained from the manual validation of the consensus alignment together with a subset of the unique mappings (i.e., mappings suggested by only one system).

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Aug 31, 2026 · cs.AIJ/K move · Enter open · S save

OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques

Hamed Babaei Giglou, Sören Auer, Peio Popov, Mahsa Sanaei+1

TIB Leibniz Information Centre for Science and Technology, Hannover, Germany · L3S Research Center, Leibniz University of Hannover, Hannover, Germany · Graphwise, Sofia, Bulgaria · University of Tabriz, Tabriz, Iran

Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding (KGE) models and, more recently, Large Language Model (LLM)-based approaches. Although modern OA frameworks provide unified ecosystems for deploying these heterogeneous aligners, mechanisms for systematically reconciling their complementary and sometimes conflicting predictions remain relatively underexplored. We present OntoAligner-Ensemble, a modular and aligner-agnostic framework that combines candidate correspondences through a configurable two-stage process comprising voting-based fusion strategies followed by post-fusion selection policies. The framework supports any aligner implemented within OntoAligner that produces candidate correspondences, enabling diverse alignment paradigms to be integrated through a unified decision process. To demonstrate its effectiveness, we instantiate the framework using representative lightweight string-aligner, KGE-based, and Retrieval-Augmented Generation aligners powered by both open-weight and API-based LLMs. We evaluate individual aligners and ensemble configurations across eight benchmark tasks from five OAEI tracks spanning biomedical to beyond-equivalence. The results show that ensemble fusion consistently improves the balance between precision and recall and frequently outperforms standalone aligners across diverse domains. Furthermore, our analysis reveals that ensemble composition directly affects the precision-recall trade-off: heterogeneous cross-paradigm ensembles generally improve precision, whereas homogeneous LLM ensembles more often achieve higher overall F1-scores. These findings demonstrate that systematic ensemble learning offers a robust and reproducible strategy for OA while providing practical guidance for selecting ensemble compositions under different alignment scenarios.

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Abstract

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