Abstract
In order to solve the problem of unfriendly interface of the existing search engines, multi-source heterogeneous and uneven quality of knowledge and multiple selection factors in the process of weapon equipment selection, a question-answer (QA)-based support technology of weapon equipment selection was proposed, improving the timeliness and efficiency of decision-makers to obtain information required and overcoming the inability to guarantee the credibility of the decisions made. The knowledge graph was adopted to deal with the problem of multi-source heterogeneous knowledge in the field of weapon equipment. And through the formulation of 8 categories of question templates and an improved question similarity algorithm that incorporates lexical part-of-speech features, a combination of frequently asked questions (FAQ) queries and template matching based question and answer methods were used to realize a QA system that supports weapon equipment selection. Experiments show that the QA system based on this technology can effectively identify the selection questions raised by the decision-makers, and provide appropriate answers to the decision-makers in a relatively short time under the condition that the knowledge required for the question exists in the weapon equipment knowledge graph, thereby improving The timeliness and convenience of equipment selection decision-makers to make correct decisions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Han, Y., Liu, T.L., Li, Y., Zhang, Z.J.: Construction of intelligent weapon system effectiveness evaluation index system based on Delphi method. In: 3rd International Conference on Advanced Algorithms and Control Engineering (ICAACE), vol. 1570, IOP, Zhangjiajie, China (2020)
Du, Z., Li, Y., Li, H., Jiang, Q., You, Z., Xiao, J., Tan, Z.: Discussion on ANP-based weapon equipment effectiveness evaluation method. In: Materials Science, Energy Technology and Power Engineering II (MEP2018), vol. 1971, AIP (2018)
Petrović, I., Sretović, S., Kankaraš, M.: The prioritization of criteria for the selection of radar for the air traffic control and protection by multi-criteria decision: making application in the fuzzy environment. Vojno delo 70(4), 128–137 (2018)
Gunes, E., Szechtman, R.: A simulation model of a helicopter ambulance service. In: 37th Winter Simulation Conference, pp. 951–957. IEEE, Orlando, FL, USA (2005)
Wan, C., Xiong, W., Ye, Y., Zhao, Q., Yang, K.: Research on development strategy of weapon equipment in antagonistic environment. In: 2017 Annual IEEE International Systems Conference (SysCon), pp. 1–5. IEEE, Montreal, QC, Canada (2017)
Li, J., Ge, B., Jiang, J., Yang, K., Chen, Y.: High-end weapon equipment portfolio selection based on a heterogeneous network model. J. Global Optim. 78(4), 743–761 (2018). https://doi.org/10.1007/s10898-018-0687-1
Liao, F., Ma, L., Yang, D.: Research on construction method of knowledge graph of US military equipment based on BiLSTM model. In: 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), pp. 146–150. IEEE, Shenzhen, China (2019)
Athira, P.M., Sreeja, M., Reghuraj, P.: Architecture of an Ontology-Based Domain-Specific Natural Language Question Answering System. Int. J. Web Semant. Technol. 4(4), 31–39 (2013)
Tomljanovic, J., Pavlic, M., Katic, M.A.: Intelligent question-answering systems: review of research. In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1228–1233. MIPRO, Opatija, Croatia (2014)
Dwivedi, S.K., Singh, V.: Research and reviews in question answering system. Procedia Technol. 10, 417–424 (2013)
Zhang, Y., et al.: COVID-19 public opinion and emotion monitoring system based on time series thermal new word mining. Comput. Mater. Continua 64(3), 1415–1434 (2020)
Felter, W., Ferreira, A., Rajamony, R., Rubio, J.: An updated performance comparison of virtual machines and Linux containers. In: Proceedings of 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 171–172. IEEE, Philadelphia, PA, USA (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zeng, M., Hao, J., Yan, Y., Zhao, L., Zhu, Z., Lin, J. (2022). Support Technology of Weapon Equipment Selection Based on Question and Answer. In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-81007-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81006-1
Online ISBN: 978-3-030-81007-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)