Dr. Konstantinos Demestichas Profile

Dr. Konstantinos Demestichas

at National Technical Univ of Athens

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Proceedings Article | 21 April 2020 Paper

Reasoning with small data samples for organised crime

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

Proceedings Volume 11413, 114130D (2020) https://doi.org/10.1117/12.2557543

KEYWORDS: Data modeling, Logic, Data communications, Taxonomy, Computing systems, Data processing, Systems modeling, Machine learning

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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.

Proceedings Article | 21 April 2020 Paper

Machine learning for discovery analytics to support criminal investigations

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

Proceedings Volume 11395, 1139504 (2020) https://doi.org/10.1117/12.2557541

KEYWORDS: Machine learning, Data fusion, Analytics, Legal, Data conversion, Information fusion, Databases, Computer programming, Visualization

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Over the last decades, criminal activities have progressively expanded into the information technology (IT) world, adding to the “traditional” criminal activities, ignoring political boundaries and legal jurisdictions. 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 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 for information fusion and classification tools intended to support LEA’s investigations. The Person Fusion Tool will be responsible for finding in an underlying knowledge graph different person instances that refer to the same person and fuse these instances. The general approach, the similarity metrics, the architecture of the tool and design choices as well as measures to improve the efficiency of the tool will be presented. The tool for classifying money transfer transactions uses decision trees. This is due to a requirement of easy explainability of the classification results, which is demanded from the ethical and legal perspective of the MAGNETO project. The design of the tool, the selected implementation and an evaluation based on anonymized financial data records will be presented.

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