Vector symbolic open source information discovery

Cai Davies; Sam Meek; Philip Hawkins; Benomy Tutcher; Graham Bent; Alun Preece

doi:10.1117/12.3013447

7 June 2024 Vector symbolic open source information discovery

Cai Davies, Sam Meek, Philip Hawkins, Benomy Tutcher, Graham Bent, Alun Preece

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 1305118 (2024) https://doi.org/10.1117/12.3013447
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Combined, joint, intra-governmental, inter-agency and multinational (CJIIM) operations require rapid data sharing without the bottlenecks of metadata curation and alignment. Curation and alignment is particularly infeasible for external open source information (OSINF), e.g., social media, which has become increasingly valuable in understanding unfolding situations. Large language models (transformers) facilitate semantic data and metadata alignment but are inefficient in CJIIM settings characterised as denied, degraded, intermittent and low bandwidth (DDIL). Vector symbolic architectures (VSA) support semantic information processing using highly compact binary vectors, typically 1-10k bits, suitable in a DDIL setting. We demonstrate a novel integration of transformer models with VSA, combining the power of the former for semantic matching with the compactness and representational structure of the latter. The approach is illustrated via a proof-of-concept OSINF data discovery portal that allows partners in a CJIIM operation to share data sources with minimal metadata curation and low communications bandwidth. This work was carried out as a bridge between previous low technology readiness level (TRL) research and future higher-TRL technology demonstration and deployment.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cai Davies, Sam Meek, Philip Hawkins, Benomy Tutcher, Graham Bent, and Alun Preece "Vector symbolic open source information discovery", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 1305118 (7 June 2024); https://doi.org/10.1117/12.3013447

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