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19 September 2019 Towards information extraction and semantic world modelling to support information management and intelligence creation in defense coalitions
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Abstract
This publication addresses a structured approach to support information management and intelligence creation in defense coalitions under consideration of the corresponding operational processes. From the methodical point of view, key aspects are the application of a semantic world modeling system and the dedicated combination of data-driven as well as knowledge-based Artificial Intelligence (AI) methods. In the context of this publication, during system operations, in particular Joint Intelligence, Surveillance and Reconnaissance (ISR) results in form of textual ISR reports being in accordance with NATO reporting standards and agreements serve as input to the world modeling system. To obtain maximum benefit from the respective information, relevant information elements have to be extracted from both, structured and unstructured parts of the reports and to be combined with information being already available in the semantic world modeling system. For structured parts of a report, a predefined mapping of the respective parts of the data model of the report to the target model of the semantic world modeling system can be applied. To extract the relevant information elements from unstructured parts of the report, Natural Language Processing (NLP) techniques are needed additionally. In this context, specific challenges with regard to the application of data-driven AI methods in the domain of defense are addressed through a two-step approach for information extraction from unstructured text based on an intermediate semantic representation.
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Almuth Hoffmann, Achim Kuwertz, and Jennifer Sander "Towards information extraction and semantic world modelling to support information management and intelligence creation in defense coalitions", Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 111690G (19 September 2019); https://doi.org/10.1117/12.2533320
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