TY - GEN
T1 - LE2C
T2 - 9th Asia-Pacific Web and Web-Age Information Management Joint International Conference on Web and Big Data, APWeb-WAIM 2025
AU - Liang, Jiayi
AU - Wu, Shuchun
AU - Wang, Xiaoling
AU - Niu, Junyu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - In the development of intelligent systems, multi-label event classification plays a vital role in enabling accurate decision-making across diverse scenarios such as incident response, customer service, and urban management. Although existing graph-based approaches for multi-label classification have shown potential, they struggle to model directed label dependencies and lack explainability, resulting in black-box decision-making processes. To address these issues, we propose a novel LLM-enhanced Event Evolutionary graph for Explainable Classification (LE2C) method. Specifically, we first leverage the powerful semantic learning capabilities of Large Language Models to construct an event evolutionary graph that models event dynamics. Furthermore, we introduce a co-occurrence probability matrix to enhance the expressivity and explainability of the graph, guiding explainable classification. Extensive experiments on two large real-world event classification tasks demonstrate the efficiency, effectiveness, and explainability of LE2C. The code is available at https://github.com/NinaLiangjy/LE2C.
AB - In the development of intelligent systems, multi-label event classification plays a vital role in enabling accurate decision-making across diverse scenarios such as incident response, customer service, and urban management. Although existing graph-based approaches for multi-label classification have shown potential, they struggle to model directed label dependencies and lack explainability, resulting in black-box decision-making processes. To address these issues, we propose a novel LLM-enhanced Event Evolutionary graph for Explainable Classification (LE2C) method. Specifically, we first leverage the powerful semantic learning capabilities of Large Language Models to construct an event evolutionary graph that models event dynamics. Furthermore, we introduce a co-occurrence probability matrix to enhance the expressivity and explainability of the graph, guiding explainable classification. Extensive experiments on two large real-world event classification tasks demonstrate the efficiency, effectiveness, and explainability of LE2C. The code is available at https://github.com/NinaLiangjy/LE2C.
KW - Event Evolutionary Graph
KW - Explainable Multi-label Classification
KW - Knowledge Discovery
KW - Large Language Model
UR - https://www.scopus.com/pages/publications/105029827215
U2 - 10.1007/978-981-95-5719-6_23
DO - 10.1007/978-981-95-5719-6_23
M3 - 会议稿件
AN - SCOPUS:105029827215
SN - 9789819557189
T3 - Lecture Notes in Computer Science
SP - 357
EP - 372
BT - Web and Big Data - 9th International Joint Conference, APWeb-WAIM 2025, Proceedings
A2 - Li, Jiajia
A2 - Zong, Chuanyu
A2 - Chbeir, Richard
A2 - Li, Lei
A2 - Zhang, Yanfeng
A2 - Zhang, Mengxuan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 28 August 2025 through 30 August 2025
ER -