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Proceedings Paper

Embedding dynamic knowledge graphs based on observational ontologies in semantic vector spaces
Author(s): Declan Millar; Dave Braines; Laura D'Arcy; Iain Barclay; Doug Summers-Stay; Paul Cripps
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Paper Abstract

Knowledge graphs (KGs) provide a useful representation format for capturing complex knowledge about an information domain, with rich logical descriptions available for defining the relationships between entities. Separately, semantic vector spaces (SVSs) capture the relative meanings of terms based on their actual usage within a dataset and allow useful operations for exploring the relationships between these terms. Combining KGs and SVSs via knowledge graph embedding (KGE) enables further analysis tasks to leverage learned semantic vectors to gain additional insights. Therefore, KGE represents an interesting and potentially powerful tool for identifying emergent or unexpected behavior, or for seeking previously unaccounted for relationships, event, and groups. In this work, we report on the state-of-the-art in KGE. We describe the operational benefits that can be gained from this approach and the considerations that apply for observational ontologies that describe a complex, untrusted, time-sensitive, and rapidly-evolving environment. We suggest several promising avenues for future research in this context.

Paper Details

Date Published: 12 April 2021
PDF: 10 pages
Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461O (12 April 2021); doi: 10.1117/12.2585888
Show Author Affiliations
Declan Millar, IBM Research Europe (United Kingdom)
Dave Braines, IBM Research Europe (United Kingdom)
Laura D'Arcy, Cardiff Univ. (United Kingdom)
Iain Barclay, Cardiff Univ. (United Kingdom)
Doug Summers-Stay, CCDC Army Research Lab. (United States)
Paul Cripps, Defence Science and Technology Lab. (United Kingdom)

Published in SPIE Proceedings Vol. 11746:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III
Tien Pham; Latasha Solomon, Editor(s)

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