ATOC-LLM: An LLM-Driven Automatic Task Ontology Construction Method

Task ontologies are core knowledge carriers for intelligent decision-making and multi-agent collaboration, yet traditional construction approaches suffer from low automation, inconsistent hierarchical semantics, insufficient relational modeling, and poor dynamic adaptability. To address these limitations, this study proposes a large language model (LLM)-driven automatic construction method for hierarchical task ontologies. Built upon domain corpus modeling, the framework integrates hierarchical feature extraction, dynamic weight clustering, and event gap identification to support end-to-end ontology generation and incremental updating. Comprehensive experiments on emergency rescue scenarios were conducted using three mainstream LLMs: Deepseek, Doubao, and ERNIE Bot. Quantitative results demonstrate that the proposed method yields superior F1-scores in feature extraction and overall ontology quality compared with classic BiLSTM-CRF and baseline ablation models. Consistent performance across multiple LLMs verifies the framework’s robustness, while the optimal results achieved by Doubao further validate the effectiveness and strong generalization capability of the proposed LLM-driven ontology construction paradigm.