液氧液氢发动机领域知识图谱构建与应用

陈潇萍; 王剑锋; 张虹; 胡庆杰

doi:10.13700/j.bh.1001-5965.2022.0333

液氧液氢发动机领域知识图谱构建与应用

doi: 10.13700/j.bh.1001-5965.2022.0333

1.
北京航天动力研究所，北京 100076
2.
中国船舶科学研究中心，无锡 214082

详细信息

通讯作者:
E-mail：chenxp@calt11.cn

中图分类号: TP391.1；TP18；V434
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- 文章访问数: 132
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- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-07
- 录用日期: 2022-08-07
- 网络出版日期: 2022-10-31
- 整期出版日期: 2024-03-27

Construction and application of knowledge graph in LOX/LH2 engine domain

1.
Beijing Aerospace Propulsion Institute，Beijing 100076，China
2.
China Ship Scientific Research Center，Wuxi 214082，China

More Information

Corresponding author: E-mail：chenxp@calt11.cn

摘要

摘要:
液氧液氢发动机作为航天领域的关键分系统，为有效开展其研制过程的智能化转型工作，针对液氧液氢发动机领域构建知识图谱，在储备领域知识的同时，提高科研生产人才的培养能力。针对液氧液氢发动机领域特点，对领域语料标注、领域知识识别、实体关系识别3个方面进行了研究，在此基础上进行了领域知识图谱的构建，并从领域知识搜索、知识推荐、探索式分析3个角度梳理了其业务应用模式。形成了液氧液氢发动机领域知识体系，并提出构建方法和应用模式。研究结果为航天领域智能化转型提供了参考。
- 液氧液氢 /
- 发动机 /
- 领域知识图谱 /
- 知识实体 /
- 关系抽取
Abstract:
As a crucial component of the aerospace industry, we construct the domain knowledge graph in the domain of liquid oxygen and liquid hydrogen engines in order to maintain domain knowledge and efficiently enhance the training capacity of scientific research and production talents. According to the characteristics of this domain, three aspects of domain corpus labeling, domain entity recognition, and entity relationship recognition are studied. Based on the research results, the construction of a domain knowledge graph is carried out, and the application mode from three perspectives is sorted out: domain knowledge search, knowledge recommendation, and exploratory analysis. Ultimately, the building methods and application modes are proposed, and the knowledge system in the field of liquid oxygen and liquid hydrogen engines is developed. These materials serve as a guide for the intelligent transformation of the aerospace sector.
- liquid oxygen and liquid hydrogen /
- engine /
- domain knowledge graph /
- knowledge entity /
- relation extraction

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图 1 知识图谱应用

Figure 1. Knowledge graph applications

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图 2 液氧液氢发动机领域知识图谱技术架构

Figure 2. Liquid oxygen and liquid hydrogen engine domain knowledge graph technical architecture

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图 3 液氧液氢发动机领域知识图谱逻辑架构

Figure 3. Logical architecture of knowledge graph for liquid oxygen and liquid hydrogen engine domain

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图 4 领域语料标注结果

Figure 4. Domain corpus labelling results

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图 5 关系语料标注结果

Figure 5. Relational corpus labelling results

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图 6 3层模型工作流程

Figure 6. Three-layer model workflow

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图 7 广义后缀树算法

Figure 7. Generalised suffix tree algorithm

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图 8 t时刻下的BiLSTM示例

Figure 8. Example of BiLSTM at moment t

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图 9 实体融合过程的决策策略

Figure 9. Entity integration strategy

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图 10 知识应用系统的主要功能界面

Figure 10. Main functional interface of the knowledge application system

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图 11 知识图谱检索功能界面

Figure 11. Knowledge graph retrieval function interface

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图 12 知识实体识别实验结果对比图

Figure 12. Comparison of knowledge entity recognition experimental results

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图 13 实体关系识别实验结果

Figure 13. Results of the entity relationship recognition experiment

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图 14 A_N效果对比

Figure 14. A_N effect comparison

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表 1 主要扩展属性

Table 1. Main extended attributes

概念类型	属性用途	基本构成
文档知识	描述获取该文档的方式，及其内容信息	文档标题、来源、等级、业务方向、存储路径、文档内容
推进剂	描述推进剂的主要成分	成分、配比、储存条件、储存时长、燃烧效率、能量效率
结构	描述发动机的主要结构技术	点火方式、燃烧室、喷嘴、冷却系统、声学系统、排气系统
APP	描述APP的基本信息和使用信息	应用名、软件平台、供应商、运营商、简介、内容语言

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表 2 实验数据情况

Table 2. Experimental data status

数据集	实体数量	实体对	实体关系
训练语料	32 384	834 629	60 947
开发语料	9 102	90 640	5 275
测试语料	11 286	103 809	7 206

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表 3 知识实体识别模型参数设置

Table 3. Knowledge entity recognition model parameter settings

Transformer 层数	向量维度	隐藏层数	Dropout	学习率	批大小	迭代周期
12	256	1024	0.5	3×10⁻⁵	32	100

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表 4 实体关系识别模型参数设置

Table 4. Parameterisation of the entity relationship recognition model

$\theta ({d})$	$\theta ({w})$
5	{3,4}

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表 5 对照参数组

Table 5. Control parameter group

组别	向量维度	隐藏层数	批大小	$ \theta ({\mathrm{dist}})$	$\theta ({\mathrm{wind}})$
A	128	512	16	4	{3,3}
B	128	768	16	4	{3,4}
C	128	1024	16	5	{3,5}
D	256	512	32	6	{4,5}
E	256	768	32	6	{4,6}

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表 6 知识实体识别实验结果

Table 6. Results of knowledge entity recognition experiments

组别	P	R	F₁
A	0.852	0.805	0.828
B	0.894	0.846	0.869
C	0.902	0.869	0.885
D	0.879	0.852	0.865
E	0.910	0.869	0.889
实验组	0.908	0.882	0.895

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表 7 实体关系识别实验结果

Table 7. Results of the entity relationship recognition experiment

组别	P	R	F₁	A₅	A₁₀	A₂₀
A	0.802	0.582	0.675	0.822	0.828	0.830
B	0.789	0.637	0.705	0.797	0.805	0.813
C	0.754	0.783	0.768	0.762	0.785	0.800
D	0.603	0.795	0.686	0.609	0.609	0.615
E	0.585	0.804	0.677	0.627	0.665	0.709
实验组	0.772	0.768	0.770	0.798	0.822	0.835

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