Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15–17 March 2024, Changsha, China

Research Article

Construction of a Knowledge Graph for Conflict and Dispute Events Based on Multimodal Data

Download13 downloads
  • @INPROCEEDINGS{10.4108/eai.15-3-2024.2346377,
        author={Yue  Liu and Qinglang  Guo and Xiaosi  Wang and Fei  Wang},
        title={Construction of a Knowledge Graph for Conflict and Dispute Events Based on Multimodal Data},
        proceedings={Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15--17 March 2024, Changsha, China},
        publisher={EAI},
        proceedings_a={PMIS},
        year={2024},
        month={6},
        keywords={conflict and dispute; knowledge graph; multimodal data},
        doi={10.4108/eai.15-3-2024.2346377}
    }
    
  • Yue Liu
    Qinglang Guo
    Xiaosi Wang
    Fei Wang
    Year: 2024
    Construction of a Knowledge Graph for Conflict and Dispute Events Based on Multimodal Data
    PMIS
    EAI
    DOI: 10.4108/eai.15-3-2024.2346377
Yue Liu1, Qinglang Guo1,*, Xiaosi Wang2, Fei Wang2
  • 1: University of Science and Technology of China
  • 2: Institute of Dataspace
*Contact email: gql1993@mail.ustc.edu.cn

Abstract

The research presented in this article focuses on the construction of a Knowledge Graph for contradictory dispute events, leveraging multimodal data. A Knowledge Graph is a structured knowledge base composed of entities and their interrelations, enriched with extensive semantic information and reasoning capabilities. This particular Knowledge Graph is built upon diverse modalities of data sources, including images, texts, and structured data, specifically tailored for the domain of contradictory disputes. The primary objective of the study is to scientifically manage such disputes by mining and analyzing the information embedded in the data. This approach facilitates intelligent detection, assessment, early warning, and personnel alerts for various types of contradictory dispute events. The research significantly contributes to the effective handling and resolution of conflicts in the public security domain, offering robust support through advanced data-driven insights.