Reasoning with small data samples for organised crime

Wilmuth Müller; Dirk Pallmer; Dirk Mühlenberg; Ioannis Loumiotis; Konstantina Remoundou; Pavlos Kosmides; Konstantinos Demestichas

doi:10.1117/12.2557543

21 April 2020 Reasoning with small data samples for organised crime

Wilmuth Müller, Dirk Pallmer, Dirk Mühlenberg, Ioannis Loumiotis, Konstantina Remoundou, Pavlos Kosmides, Konstantinos Demestichas

Author Affiliations +

Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114130D (2020) https://doi.org/10.1117/12.2557543
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Building upon the possibilities of technologies like big data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions and reasoning tools, even with only small data samples. Due to the fact that the MAGNETO tools have to operate on highly sensitive data from criminal investigations, the data samples provided to the tool developers have been small, scarce, and often not correlated. The project team had to overcome these drawbacks. The developed reasoning tools are based on the MAGNETO ontology and knowledge base and enables LEA officers to uncover derived facts that are not expressed in the knowledge base explicitly, as well as discover new knowledge of relations between different objects and items of data. Two reasoning tools have been implemented, a probabilistic reasoning tool based on Markov Logic Networks and a logical reasoning tool. The design of the tools and their interfaces will be presented, as well as the results provided by the tools, when applied to operational use cases.

Citation Download Citation

Wilmuth Müller, Dirk Pallmer, Dirk Mühlenberg, Ioannis Loumiotis, Konstantina Remoundou, Pavlos Kosmides, and Konstantinos Demestichas "Reasoning with small data samples for organised crime", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114130D (21 April 2020); https://doi.org/10.1117/12.2557543

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Data modeling

Logic

Data communications

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