TY - GEN
T1 - Enhanced tracking and reporting of missing persons using Knowledge Graph and Ontology Engineering
AU - Chowdhury, Suparno Roy
AU - Desai, Ayush
AU - Kapure, Mrunal
AU - Shikalgar, Mustakim
AU - Venugopal, Ramchander
AU - Bansal, Srividya
N1 - Publisher Copyright: © 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The issue of missing persons remains a significant concern in the United States, with thousands of cases reported annually. Addressing this challenge requires innovative methods to integrate and analyze disparate data sources to uncover patterns in disappearances. This research explores the potential of Ontology Engineering and Knowledge Graphs to provide a structured and interconnected perspective on missing person data. Ontologies enable the representation and linking of real-world entities within a unified framework, facilitating more meaningful data relationships.In this study, a knowledge graph is constructed to capture key details about missing persons, including the circumstances of their disappearance, personal characteristics, and last known locations. Additionally, a Large Language Model (LLM) is integrated to summarize query results, enhancing data interpretation. This knowledge graph serves as a foundation for advanced data analysis, enabling real-time insights, supporting proactive interventions, and aiding in case resolution.
AB - The issue of missing persons remains a significant concern in the United States, with thousands of cases reported annually. Addressing this challenge requires innovative methods to integrate and analyze disparate data sources to uncover patterns in disappearances. This research explores the potential of Ontology Engineering and Knowledge Graphs to provide a structured and interconnected perspective on missing person data. Ontologies enable the representation and linking of real-world entities within a unified framework, facilitating more meaningful data relationships.In this study, a knowledge graph is constructed to capture key details about missing persons, including the circumstances of their disappearance, personal characteristics, and last known locations. Additionally, a Large Language Model (LLM) is integrated to summarize query results, enhancing data interpretation. This knowledge graph serves as a foundation for advanced data analysis, enabling real-time insights, supporting proactive interventions, and aiding in case resolution.
KW - Data Integration
KW - Knowledge Graphs
KW - LLM
KW - Missing people
KW - NamUS
KW - Ontologies
KW - Risk factors
KW - Semantic web
UR - https://www.scopus.com/pages/publications/105016143452
UR - https://www.scopus.com/pages/publications/105016143452#tab=citedBy
U2 - 10.1109/COMPSAC65507.2025.00084
DO - 10.1109/COMPSAC65507.2025.00084
M3 - Conference contribution
T3 - Proceedings - 2025 IEEE 49th Annual Computers, Software, and Applications Conference, COMPSAC 2025
SP - 619
EP - 627
BT - Proceedings - 2025 IEEE 49th Annual Computers, Software, and Applications Conference, COMPSAC 2025
A2 - Shahriar, Hossain
A2 - Alam, Kazi Shafiul
A2 - Ohsaki, Hiroyuki
A2 - Cimato, Stelvio
A2 - Capretz, Miriam
A2 - Ahmed, Shamem
A2 - Ahamed, Sheikh Iqbal
A2 - Majumder, AKM Jahangir Alam
A2 - Haque, Munirul
A2 - Yoshihisa, Tomoki
A2 - Cuzzocrea, Alfredo
A2 - Takemoto, Michiharu
A2 - Sakib, Nazmus
A2 - Elsayed, Marwa
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 49th Annual IEEE Computers, Software, and Applications Conference, COMPSAC 2025
Y2 - 8 July 2025 through 11 July 2025
ER -