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

arXiv: https://arxiv.org/abs/2608.05867 · HTML: https://mlsanity.com/paper/2608.05867/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [BERTMap: A BERT-based Ontology Alignment System](https://mlsanity.com/paper/2112.02682/) (2021)
- [Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching](https://mlsanity.com/paper/2205.03447/) (2022)
- [Ontologies in Digital Twins: A Systematic Literature Review](https://mlsanity.com/paper/2308.15168/) (2023)
- [Best Practices for Implementing FAIR Vocabularies and Ontologies on the Web](https://mlsanity.com/paper/2003.13084/) (2020)
- [Social Engineering in Cybersecurity: A Domain Ontology and Knowledge Graph Application Examples](https://mlsanity.com/paper/2106.01157/) (2021)
- [Large Language Models as Oracles for Ontology Alignment](https://mlsanity.com/paper/2508.08500/) (2025)
- [Structuring Security: A Survey of Cybersecurity Ontologies, Semantic Log Processing, and LLMs Application](https://mlsanity.com/paper/2510.16610/) (2025)

## Abstract (arXiv)

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).