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Title:
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DIGITAL SEMANTIC NETWORK FOR FORENSIC INTELLIGENCE: GRAPH-BASED MACHINE LEARNING |
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Author(s):
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Dmytro Uzlov, Oleksii Vlasov, Sergiy Yakovlev, Lyudmyla Kirichenko and Denys Sadilo |
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ISBN:
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978-989-8704-71-9 |
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Editors:
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Paula Miranda and Pedro Isaías |
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Year:
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2025 |
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Edition:
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Single |
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Keywords:
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Graph Machine Learning, Knowledge Graph, Forensic Intelligence, Graph Neural Networks, Spatiotemporal Prediction |
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Type:
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Poster |
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First Page:
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311 |
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Last Page:
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313 |
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Language:
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English |
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Cover:
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Full Contents:
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Paper Abstract:
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This paper proposes an advanced approach to forensic intelligence by integrating graph-based machine learning techniques.
We design a semantic network architecture that transforms fragmented and semi-structured crime records into structured
knowledge graphs. These graphs serve as the foundation for embedding, classification, link prediction, and anomaly
detection through advanced graph neural network (GNN) architectures. Our experimental evaluation on real-world criminal
datasets demonstrates the efficacy of this approach in uncovering hidden relationships, forecasting crime patterns, and
identifying entities of interest within forensic investigations. |
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