Knowledge Graph Representation of Open-Source Homicide Information
Year: 2025
Type: conference-paper
Abstract: Homicide information is openly disseminated across various sources such as government websites, news portals, blogs, social media sites through which intelligence can be derived. However, conducting homicide investigations relying on open-source intelligence poses various challenges, including complexity and epistemic uncertainty. Both ontology and knowledge graph have demonstrated some potential in mitigating complexity and uncertainty across various domains. However, their application in context to open-source intelligence and homicide investigations with respect to uncertainty representation is lacking. This study addresses the underlying research gaps and thus presents two different approaches to represent open-source information and the epistemic uncertainty associated with it using knowledge graphs to support homicide investigations. The dataset used in the study consists of details on nine homicide cases in the Netherlands. The first approach used web ontology language, whereas the second approach used graph-based modelling using the networkX library for ontological modelling. Similarly, probabilistic approach and visualisation attributes such as node distance, edge width and node colour were used to address the epistemic uncertainty associated with open-source homicide information in the first and second approach, respectively. The two knowledge graphs were visualised using an example of a homicide case and then evaluated on the basis of visual clarity. Both approaches demonstrated low visual clarity when visualising an entire homicide case. However, the knowledge graph developed using the second approach demonstrated better visual clarity. Overall, the findings signify the potential of knowledge graphs to support investigators in addressing the epistemic uncertainty associated with open sources for conducting effective homicide investigations.
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: University of Twente
OpenAlex ID: W7135774257
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