12 May 2016 Enabling task-based information prioritization via semantic web encodings
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Abstract
Modern Soldiers rely upon accurate and actionable information technology to achieve mission objectives. While increasingly rich sensor networks for Areas of Operation (AO) can offer many directions for aiding Soldiers, limitations are imposed by current tactical edge systems on the rate that content can be transmitted. Furthermore, mission tasks will often require very specific sets of information which may easily be drowned out by other content sources.

Prior research on Quality and Value of Information (QoI/VoI) has aimed to define ways to prioritize information objects based on their intrinsic attributes (QoI) and perceived value to a consumer (VoI). As part of this effort, established ranking approaches for obtaining Subject Matter Expert (SME) recommendations, such as the Analytic Hierarchy Process (AHP) have been considered. However, limited work has been done to tie Soldier context – such as descriptions of their mission and tasks – back to intrinsic attributes of information objects.

As a first step toward addressing the above challenges, this work introduces an ontology-backed approach – rooted in Semantic Web publication practices – for expressing both AHP decision hierarchies and corresponding SME feedback. Following a short discussion on related QoI/VoI research, an ontology-based data structure is introduced for supporting evaluation of Information Objects, using AHP rankings designed to facilitate information object prioritization. Consistent with alternate AHP approaches, prioritization in this approach is based on pairwise comparisons between Information Objects with respect to established criteria, as well as on pairwise comparison of the criteria to assess their relative importance. The paper concludes with a discussion of both ongoing and future work.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James R. Michaelis, "Enabling task-based information prioritization via semantic web encodings", Proc. SPIE 9851, Next-Generation Analyst IV, 98510N (12 May 2016); doi: 10.1117/12.2221915; https://doi.org/10.1117/12.2221915
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