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Alessandro Negro

Leveraging Knowledge Graphs for Disrupting Criminal Networks

A Talk by Alessandro Negro (Chief Scientist, GraphAware, London, United Kingdom)

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About this Talk

This presentation proposes a three-step process that utilises knowledge graphs to empower law enforcement agencies in criminal intelligence gathering and criminal network analysis. The approach tackles the challenge of siloed data hindering effective crime investigation.

Step 1: Transitioning to Holistic Intelligence Data

The paper acknowledges the data analysis hurdle faced by law enforcement due to disparate data sources. It proposes transitioning to a knowledge graph to unify these sources and create a holistic view of criminal intelligence data. The knowledge graph collates structured and unstructured data - internal to police forces and from open source data (ex. OSINT) - and resolve entities. This step also leverages LLMs to properly convert text in organised knowledge.

Step 2: From Search to Exploration in Criminal Intelligence

The second step advocates for leveraging the knowledge graph's ability to explore data connections. This enables analysts to navigate connections among people and events, fostering deeper investigative insights compared to traditional search methods. Graphs facilitate this task in terms of speed and understandability.

Step 3: From Analytics to Predictive Policing

Finally, the presentation explores how knowledge graphs can be used for predictive policing. By analysing patterns within the graph, the approach helps identify potential criminal activity and key network players. Also, it allows to reveal hidden connections and to relate crimes to potential offenders empowering modern suspect nomination and risk assessment functions. The predictions provided are fully explainable to guarantee transparency in police operations. 

Overall Benefit

The presentation positions knowledge graphs as a transformative tool for criminal intelligence, aiding disruption of criminal networks regardless their size and structure, improved decision-making, and ultimately, enhanced public safety.

12 December 2024, 02:35 PM

Edges Stage

02:35 PM - 03:05 PM

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About The Speakers

Alessandro Negro

Alessandro Negro

Chief Scientist, GraphAware, London, United Kingdom

Alessandro Negro is a Chief Scientist at GraphAware. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.

Alessandro Negro

Location

Convene 133 Houndsditch

133 Houndsditch, London

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