Pappas, D. and Paraskakis, I. (2017) HOMER: A semantically enhanced knowledge management approach in the domain of homemade explosives intelligence. Social Network Analysis and Mining, 7 (1). 43. ISSN 1869-5450
Abstract
This paper presents a new approach, in handling data (encoding, managing and retrieving) in secure sensitive and classified organisations (such as Law Enforcement Agencies (LEAs)), that utilises Web 3.0 technologies as well as knowledge management techniques and pushing of information. This approach signals a departure from current use of databases and pulling of information technologies as well as allowing separation of concerns between how data are organised/structured and how data are manipulated/processed. Such an approach utilises an adaptive knowledge management platform capable of supporting organisational operations of LEAs using data aggregated from assorted, heterogeneous and online sources. Such knowledge is then pushed to the users, using recommenders, in an effortless manner addressing the needs of the organisation. Moreover, the system is designed to afford easier change of operational needs through the addition and removal of multiple folksonomies (representing changes in focus or new trends). These changes are further enriched with semantics providing specialised domain-specific content recommendations and semantically enriched search capabilities. This approach to knowledge retrieval has been applied to the domain of homemade explosives and counter-terrorism efforts as part of the HOMER project, where data are aggregated from sources such as police databases, online forums and explosives wikis. Data are stored in an unstructured manner and annotated by the users, ultimately being categorised as per the knowledge retrieval needs of the organisation, which in this case is to carry out efficient and effective investigations regarding homemade explosives. We describe the architecture of a system that can efficiently and effectively support related investigatory activities, and we also present an evaluation from the perspective of the end-users.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017, The Authors. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Keywords: | Knowledge retrieval; Recommender systems; Semantic enrichment; Folksonomies; Personalisation; Homemade explosives; Classified organisations |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Mar 2019 13:48 |
Last Modified: | 04 Mar 2019 13:48 |
Published Version: | https://doi.org/10.1007/s13278-017-0451-4 |
Status: | Published |
Publisher: | Springer Verlag |
Refereed: | Yes |
Identification Number: | 10.1007/s13278-017-0451-4 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141249 |
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Filename: Pappas-Paraskakis2017_Article_HOMERASemanticallyEnhancedKnow.pdf
Licence: CC-BY 4.0