Related papers: Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations

Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations

URL: http://arxiv.org/abs/2509.26487v1
Date: Tue, 30 Sep 2025 16:32:26 GMT
Title: Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations
Authors: Riccardo Pozzi, Valentina Barbera, Renzo Alva Principe, Davide Giardini, Riccardo Rubini, Matteo Palmonari,
Abstract summary: Criminal investigations often involve the analysis of messages exchanged through instant messaging apps such as WhatsApp.<n>Our approach integrates knowledge graphs and NLP models to support this analysis by semantically enriching data collected from suspects' mobile phones.<n>We adopt two different solutions to help users get insights into the data, one based on querying and visualizing the graph, and one based on semantic search.
Score: 0.2893969925652728
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Criminal investigations often involve the analysis of messages exchanged through instant messaging apps such as WhatsApp, which can be an extremely effort-consuming task. Our approach integrates knowledge graphs and NLP models to support this analysis by semantically enriching data collected from suspects' mobile phones, and help prosecutors and investigators search into the data and get valuable insights. Our semantic enrichment process involves extracting message data and modeling it using a knowledge graph, generating transcriptions of voice messages, and annotating the data using an end-to-end entity extraction approach. We adopt two different solutions to help users get insights into the data, one based on querying and visualizing the graph, and one based on semantic search. The proposed approach ensures that users can verify the information by accessing the original data. While we report about early results and prototypes developed in the context of an ongoing project, our proposal has undergone practical applications with real investigation data. As a consequence, we had the chance to interact closely with prosecutors, collecting positive feedback but also identifying interesting opportunities as well as promising research directions to share with the research community.

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