This study presents a knowledge graph construction scheme leveraging large language models (LLMs) for task-oriented semantic communication systems. The proposed methodology systematically addresses four critical stages: corpus collection, entity extraction and relationship analysis, knowledge base generation, and dynamic updating mechanisms. It is worth noting that prompt engineering is combined with few-shot learning to enhance reliability and accuracy in this methodology. Experimental demonstration showed that this methodology had superior entity extraction performance, achieving 89.7% precision and 92.3% recall rate. This scheme overcomes the demand for domain knowledge and the labor cost of traditional knowledge base construction schemes. It greatly improves the construction efficiency of knowledge graphs. This paper provides an efficient and reliable task knowledge base construction scheme for task-oriented semantic communication, which is expected to promote its wider application.

Conceptual ontology construction, a foundational knowledge engineering task, is constrained by traditional methods’ heavy manual reliance and poor complex-domain adaptability. This study proposes the AOCG-LLM+ framework for fully automated conceptual ontology construction, integrating reinforcement learning with human feedback and a dynamically adjusted radius hierarchical clustering algorithm. Three experiments validated the framework via an LLM-calibrated evaluation system combining quantitative metrics and subjective criteria. Results show AOCG-LLM+ outperforms baselines: the RLHF-driven entity-relation extraction module achieves 92.5% entity precision (10.1% points above plain LLMs) and 88.3% relation F1-score (13 points above BERT+GCN), with 20% higher entity recall than shallow statistical learning-based techniques. The clustering algorithm attains state-of-the-art metrics, achieving superior performance on all clustering metrics. Subjective evaluation demonstrates the framework’s universality across different LLMs. This work verifies AOCG-LLM+’s technical advantages, addresses traditional methods’ hierarchical rationality and consistency defects, and provides a reliable fully automated conceptual ontology construction solution.

In dealing with emergency events, it is very important when making scientific and correct decisions. As an important premise, the creation of information needs is quite essential. Taking earthquakes as a type of unexpected event, this paper constructs a large and model-driven system for automating the generating process of information requirements for earthquake response. This research explores how the different departments interact during an earthquake emergency response, how the information interacts with each other, and how the information requirement process operates. The system is designed from three points of view, building a knowledge base, designing and developing prompts, and designing the system structure. It talks about how computers automatically make info needs for sudden emergencies. During the experimental process, the backbone architectures used were four Large Language Models (LLMs): chatGLM (GLM-4.6), Spark (SparkX1.5), ERNIE Bot (4.5 Turbo), and DeepSeek (V3.2). According to the desired system process, information needs is generated by real-word cases and then they are compared to the gathered information needs by experts. In the comparison process, the “keyword weighted matching + text structure feature fusion” method was used to calculate the semantic similarity. Like true positives, false positives, and false negatives can be used to find differences and calculate metrics like precision and recal. And the F1-score is also computed. The experimental results show that all four LLMs achieved a precision and recall of over 90% in earthquake information extraction, with their F1-scores all exceeding 85%. This verifies the feasibility of the analytical method a chatGLM dopted in this research. Through comparative analysis, it was found that chatGLM exhibited the best performance, with an F1-score of 93.2%. Eventually, Python is used to script these aforementioned processes, which then create complete comparison charts for visual and test result checking. In the course of researching we also use Protege to create the knowledge requirements ontology, so it is easy for us to show and look at it. This research is particularly useful for emergency management departments, earthquake emergency response teams, and those working on intelligent emergency information systems or those focusing on the automated information requirement generation using technologies such as LLMs. It provides practical support for optimizing rapid decision-making in earthquake emergency response.

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.