mTRACK: monitoring time-varying relations in approximately

Authors:
Trevor Martin;Yun Shen
Affiliations:
Artificial Intelligence Group, University of Bristol, UK;Artificial Intelligence Group, University of Bristol, UK
Venue:
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Year:
2009

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Abstract

Recent initiatives in defence related information systems have emphasised the need to bring together information from multiple sources and fuse it into a form suitable for decision makers. This paper outlines a four stage system for fusing unstructured and semi-structured text and numerical data by extraction of entities and relations, identification of duplicate entities, organisation into the most appropriate hierarchical categories and determination of relations between fuzzy categories. The novel contribution of this paper is in the final stage of the process, where we determine associations between fuzzy categories and identify strong and/or unusual levels of association as well as changes over time. A demonstrator application shows how information on terrorist incidents from multiple sources can be integrated and monitored.