Military Chain: Construction of Domain Knowledge Graph of Kill Chain Based on Natural Language Model
Military Chain: Construction of Domain Knowledge Graph of Kill Chain Based on Natural Language Model
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DOI：
10.1155/2022/7097385
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发表时间：
2022-07-14
影响因子：
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通讯作者：
Liu, Runmin
中科院分区：
文献类型：
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作者：
Wang, Yanfeng;Wang, Tao;Liu, Runmin
随着大数据时代的到来，杀伤链领域的专业数据急剧增加，基于引擎的信息检索方法难以满足用户对更准确答案的需求。杀伤链领域包括控制设备、传感器设备、打击设备（武器和平台）和评估设备四个组成部分，以及各组成部分包含的参数信息等大量有价值的信息。如果将这些碎片化、混乱的数据整合起来，建立有效的查询方法，就可以帮助专业人员完善军事杀伤链知识体系。本文构建的知识系统是基于Neo4j图形数据库和美军司令部仿真系统，建立面向目标的杀伤链知识图谱，旨在为问答系统提供数据支持。其次，为了便于查询，建立了基于连续词袋（CBOW）编码模型、双向长短期记忆-条件随机场（BiLSTM-CRF）命名实体模型和双向门状递归神经网络（BiGRU）中文杀链问答意图识别模型的实体和关系/属性挖掘；结合知识图三元形式返回相应的实体或属性值；最后构造答案返回。构建的杀伤链知识图谱包含2767个条目（包括海、陆、空），涉及的参数数为30124个。本次构建的问答系统，深度学习网络的模型参数个数为27.9 M，经过200次模拟查询，准确率达到85.5%。
With the advent of the Big Data era, the specialized data in the kill chain domain has increased dramatically, and the engine-based method of retrieving information can hardly meet the users' need for more accurate answers. The kill chain domain includes four components: control equipment, sensor equipment, strike equipment (weapon and platform), and evaluator equipment, as well as related data which contain a large amount of valuable information such as the parameter information contained in each component. If these fragmented and confusing data are integrated and effective query methods are established, they can help professionals complete the military kill chain knowledge system. The knowledge system constructed in this paper is based on the Neo4j graph database and the US Command simulation system to establish a target-oriented knowledge map of kill chain, aiming to provide data support for the Q&A system. Secondly, in order to facilitate the query, this paper establishes entity and relationship/attribute mining based on the continuous bag-of-words (CBOW) encoding model, bidirectional long short-term memory-conditional random field (BiLSTM-CRF) named entity model, and bidirectional gated recurrent neural network (BiGRU) intent recognition model for Chinese kill chain question and answer; returns the corresponding entity or attribute values in combination with the knowledge graph triad form; and finally constructs the answer return. The constructed knowledge map of the kill chain contains 2767 items (including sea, land, and air), and the number of parameters involved is 30124. The number of model parameters of the deep learning network is 27.9 M for the Q&A system built this time, and the accuracy rate is 85.5% after 200 simulated queries.