Event Assignment Based on KBQA for Government Service Hotlines

Autor: Yongzhang Wang, Defu Lian, Enhong Chen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Applied Artificial Intelligence, Vol 38, Iss 1 (2024)
Druh dokumentu: article
ISSN: 08839514
1087-6545
0883-9514
DOI: 10.1080/08839514.2024.2348162
Popis: Government service hotlines are closely related to people’s lives, and the hotline events assignment task is very important in China, which affects people’s satisfaction with the Chinese government. To address the challenge of improving hotline event assignment accuracy, which is currently hindered by the lack of prior knowledge in the traditional direct departmental assignment approach, we introduce a novel hotline event assignment model based on Knowledge Base Question Answering (KBQA) to leverage prior knowledge and enhance assignment performance. The model extracts event key information by using event extraction module, and the key information of event extracte by this module is used to construct historical events knowledge graph. Then we employ subgraph retrieval module and text retrieval module to obtain relevant prior knowledge from the knowledge graph and “sanding” text respectively. The integrated prior knowledge is then used to predict department scores, guiding the selection of the optimal assignment department through ranking. The experiment results show that the proposed method outperforms existing baseline methods. Meanwhile, the Knowledge Graph (KG) edge elimination experiment also shows that the proposed model is better than other baseline models in terms of incomplete KG.
Databáze: Directory of Open Access Journals
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