Research on intelligent parsing of task information needs based on ontology

Zhang Chao; Yang Jungang; Lu Zhenhai; Li Yao; Wang Lianqing; Jiang Chao

doi:10.11959/j.issn.2096-6652.ZNKX26037

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Research on intelligent parsing of task information needs based on ontology

Surveys and Prospectives | 更新时间：2026-07-29

- Research on intelligent parsing of task information needs based on ontology
- Chinese Journal of Intelligent Science and Technology Vol. 8, Issue 2, Pages: 169-182(2026)
- 作者机构：
  
  1.国防科技大学研究生院，湖南长沙 410000
  2.信息支援部队工程大学，湖北武汉 430000
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62402505);The Independent Innovation Science Fund of National University of Defense Technology(22-ZZCX-055)
- DOI：10.11959/j.issn.2096-6652.ZNKX26037
  CLC： TP39
- Received：20 March 2026，
  
  Revised：2026-05-03，
  
  Accepted：18 May 2026，
  
  Online First：27 July 2026，
  
  Published：15 June 2026
- 稿件说明：
移动端阅览
张超,杨君刚,卢震海等.基于本体的任务信息需求智能解析研究[J].智能科学与技术学报,2026,08(02):169-182.

Zhang Chao,Yang Jungang,Lu Zhenhai,et al.Research on intelligent parsing of task information needs based on ontology[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):169-182.
张超,杨君刚,卢震海等.基于本体的任务信息需求智能解析研究[J].智能科学与技术学报,2026,08(02):169-182. DOI： 10.11959/j.issn.2096-6652.ZNKX26037.

Zhang Chao,Yang Jungang,Lu Zhenhai,et al.Research on intelligent parsing of task information needs based on ontology[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):169-182. DOI： 10.11959/j.issn.2096-6652.ZNKX26037.

摘要

针对时间敏感性任务场景中信息需求解析面临的挑战，提出一种基于本体的智能解析方法。首先通过设计本体模型对任务与力量进行形式化描述；其次建立以本体模型为基础的语义转换与逻辑推理方法，实现从非结构化任务描述到结构化信息需求清单的自动生成。实验结果表明，该方法能够生成规范翔实的信息需求清单，与传统人工分析的方式相比，信息保障的效率得到显著提升。

Abstract

To address the challenges faced in the analysis of information needs in time-sensitive task scenarios

an intelligent parsing method based on ontology was proposed. Firstly

an ontology model was designed to describe tasks and forces formally. Secondly

a semantic transformation and logical reasoning method based on the ontology model was established to automatically generate a structured list of information needs from unstructured task descriptions. The experimental results show that this method can generate a standardized and detailed list of information needs. Compared with traditional manual analysis

the efficiency of information assurance is significantly improved.

关键词

Keywords

references

周国全, 赵国刚, 邵贵海. 应急指挥手段的分类与特点[J]. 消防界(电子版), 2021, 7(5): 80, 82.

Zhou G Q, Zhao G G, Shao G H. Classification and characteristics of emergency command means[J]. Fire Protection World (Electronic Edition), 2021, 7(5): 80, 82.

张寒, 黄炎焱, 耿泽, 等. 面向海上突发事件应急救援任务的协同规划[J]. 控制理论与应用, 2024, 41(4): 619-630.

Zhang H, Huang Y Y, Geng Z, et al. Collaborative planning for emergency rescue tasks in marine emergencies[J]. Control Theory & Applications, 2024, 41(4): 619-630.

汪烨, 陈骏武, 夏鑫, 等. 智能需求获取与建模研究综述[J]. 计算机研究与发展, 2021, 58(4): 681-705.

Wang Y, Chen J W, Xia X, et al. Intelligent requirements elicitation and modeling: a literature review[J]. Journal of Computer Research and Development, 2021, 58(4): 681-705.

秦芹芊. 突发公共卫生事件应急信息需求语料库的构建[D]. 南昌: 南昌大学, 2023.

Qin Q Q. Construction of the information demand corpus for public health emergencies[D]. Nanchang: Nanchang University, 2023.

崔洪振, 张龙豪, 彭云峰, 等. 关键词提取算法研究综述[J]. 中文信息学报, 2024, 38(2): 1-14, 24.

Cui H Z, Zhang L H, Peng Y F, et al. A survey for keyword extraction algorithms[J]. Journal of Chinese Information Processing, 2024, 38(2): 1-14, 24.

Metzler D, Tay Y, Bahri D, et al. Rethinking search: making domain experts out of dilettantes[J]. ACM SIGIR Forum, 2021, 55(1): 1-27.

Li X X, Jin J J, Zhou Y J, et al. From matching to generation: a survey on generative information retrieval[J]. ACM Transactions on Information Systems, 2025, 43(3): 1-62.

Tian Y E, Tang Y C, Wang K D, et al. Template-based financial report generation in agentic and decomposed information retrieval[C]//Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2025: 2706-2710.

Karabıyık M A, Tan F G, Yüksel A S. Application of prompt engineering techniques to optimize information retrieval in the metaverse[J]. Journal of Metaverse, 2024, 4(2): 157-164.

Mahalakshmi P, Fatima N S. Summarization of text and image captioning in information retrieval using deep learning techniques[J]. IEEE Access, 2022, 10: 18289-18297.

Formica A, Mele I, Taglino F. A template-based approach for question answering over knowledge bases[J]. Knowledge and Information Systems, 2024, 66(1): 453-479.

赵又霖, 庞烁, 吴宗大. 社会感知数据驱动下突发事件应急管理的时空语义模型构建研究[J]. 情报科学, 2021, 39(2): 44-53.

Zhao Y L, Pang S, Wu Z D. Research on geographic semantic ontology model based on social sensing data for emergency management of events[J]. Information Science, 2021, 39(2): 44-53.

Luo M, Mitra A, Gokhale T, et al. Improving biomedical information retrieval with neural retrievers[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(10): 11038-11046.

郑豪, 杨国伟, 李飞, 等. 融合知识图谱和大语言模型的罗氏沼虾养殖问答系统的设计与实现[J]. 智能科学与技术学报, 2025, 7(3): 361-369.

Zheng H, Yang G W, Li F, et al. Design and implementation of an intelligent question answering system for Macrobrachium rosenbergii aquaculture based on knowledge graph and large language model[J]. Chinese Journal of Intelligent Science and Technology, 2025, 7(3): 361-369.

Farrugia L, Azzopardi L M, Debattista J, et al. medicX-KG: a knowledge graph for pharmacists' drug information needs[J]. Journal of Biomedical Semantics, 2025, 16: 11.

Rolli F, Parretti C, Barbieri R, et al. RDF knowledge graphs designed with axiomatic methodology to enhance industry 4.0[J]. Machines, 2025, 13(1): 58.

马续补, 张阁静, 李波, 等. 基于多要素语义关联的政策知识图谱构建[J]. 情报理论与实践, 2025, 48(8): 93-104.

Ma X B, Zhang G J, Li B, et al. Policy knowledge graph construction based on multi-factor semantic relevance[J]. Information Studies (Theory & Application), 2025, 48(8): 93-104.

Gardasevic S, Lamba M. "It answers questions that I didn't know I had": PhD students' evaluation of an information-sharing knowledge graph[J]. Digital Library Perspectives, 2024, 40(4): 493-517.

祝哲, 周文倩, 林婕, 等. 大语言模型技术赋能的应急管理框架[J]. 指挥与控制学报, 2024, 10(6): 677-688.

Zhu Z, Zhou W Q, Lin J, et al. Emergency management framework enabled by large language model technology[J]. Journal of Command and Control, 2024, 10(6): 677-688.

Boukhlif M, Hanine M, Kharmoum N, et al. Natural language processing-based software testing: a systematic literature review[J]. IEEE Access, 2024, 12: 79383-79400.

Ge Y G, Zhang S L, Cai Y H, et al. Ontology based autonomous robot task processing framework[J]. Frontiers in Neurorobotics, 2024, 18: 1401075.

Shim M, Choi H, Koo H, et al. OmEGa(Ω): Ontology-based information extraction framework for constructing task-centric knowledge graph from manufacturing documents with large language model[J]. Advanced Engineering Informatics, 2025, 64: 103001.

王潇, 孙雨, 黄昕桐, 等. 暴雨洪水本体模型的可视化呈现与应急决策应用[J]. 中国安全科学学报, 2025, 35(4): 227-232.

Wang X, Sun Y, Huang X T, et al. Visualization of flood and rainstorm model and application of emergency decision[J]. China Safety Science Journal, 2025, 35(4): 227-232.

王芳, 杨京, 徐路路. 面向火灾应急管理的本体构建研究[J]. 情报学报, 2020, 39(9): 914-925.

Wang F, Yang J, Xu L L. Ontology construction for fire emergency management[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(9): 914-925.

王海鹰, 李志雄, 张涛, 等. 地震应急救援信息需求及获取建议[J]. 灾害学, 2016, 31(4): 176-180.

Wang H Y, Li Z X, Zhang T, et al. Information requirement of earthquake emergency response and suggestions[J]. Journal of Catastrophology, 2016, 31(4): 176-180.

中国法治出版社. 地震监测管理条例2024[M]. 北京:中国法治出版社, 2025.

China Legal Publishing House. Seismic monitoring management regulations 2024[M]. Beijing: China Legal Publishing House, 2025.

张文静, 惠杨, 侯作亮, 等. 辽宁省地震应急现场工作现状与思考[J]. 防灾减灾学报, 2023, 39(3): 66-70.

Zhang W J, Hui Y, Hou Z L, et al. Current situation and thoughts of earthquake emergency field work in Liaoning Province[J]. Seismological Research of Northeast China, 2023, 39(3): 66-70.

张翼, 范开红, 郭红梅, 等. 四川省地震应急指挥中心在芦山7.0级地震中应急模式分析[J]. 四川地震, 2014(2): 43-47.

Zhang Y, Fan K H, Guo H M, et al. Analyzing the emergency mode of the earthquake emergency response administration during the 2013 M7.0 Lushan earthquake[J]. Earthquake Research in Sichuan, 2014(2): 43-47.

法律出版社数字出版中心. 防震减灾法及相关规定精选[M]. 北京:法律出版社, 2023.

Digital Publishing Center of Law Press. Selected laws and regulations on earthquake prevention and disaster reduction[M]. Beijing: Law Press, 2023.

池国民, 赵银刚, 刘海林, 等. 山东省地震局地震应急响应指挥调度辅助软件的设计与实现[J]. 四川地震, 2024(4): 47-52.

Chi G M, Zhao Y G, Liu H L, et al. Design and implementation of response command and dispatch assistance software for earthquake emergency response at the Shandong earthquake agency[J]. Earthquake Research in Sichuan, 2024(4): 47-52.

Li Y, Guo C, Lu Z H, et al. Research on the automatic generation of information requirements for emergency response to unexpected events[J]. Applied Sciences, 2025, 15(22): 11953.

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