Intelligent modeling method for OV models in DoDAF2.0 based on knowledge graph

doi:10.23919/JSEE.2024.000034

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (1): 139-154.doi: 10.23919/JSEE.2024.000034

• SYSTEMS ENGINEERING • Previous Articles

Intelligent modeling method for OV models in DoDAF2.0 based on knowledge graph

Yue ZHANG(), Jiang JIANG(), Kewei YANG(), Xingliang WANG(), Chi XU(), Minghao LI()

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Received:2022-07-04 Online:2025-02-18 Published:2025-03-18
Contact: Minghao LI E-mail:2638930341@qq.com;jiangjiangnudt@163.com;kayyang27@nudt.edu.cn;wangxingliangnudt@163.com;xuchi20@nudt.edu.cn;liminghao_nudt@foxmail.com
About author:
ZHANG Yue was born in 1998. He received his master degree in management science and engineering with the College of Systems Engineering, National University of Defense Technology. His main research interests are model-based systems engineering and system-of-systems architecture modeling. E-mail: 2638930341@qq.com

JIANG Jiang was born in 1981. He received his B.E. degree in systems engineering and Ph.D. degree in management science and engineering from National University of Defense Technology, Changsha, China, in 2006 and 2011, respectively. He is a professor of management science and engineering with the College of Systems Engineering, National University of Defense Technology, where he is the Director of the System-of-Systems Engineering Laboratory. His research interests focus on management science and engineering, systems engineering, model-based systems engineering, systems requirements planning, risk decision and analysis. E-mail: jiangjiangnudt@163.com

YANG Kewei was born in 1977. He received his B.E. and Ph.D. degrees in systems engineering, and mangagement science and engineering from National University of Defence Technology in 1999 and 2004, respectively. He is a professor and Ph.D. candidate supervisor at the National University of Defense Technology (NUDT). He is the associate dean of the College of Systems Engineering of NUDT. His research interests focus on the system of systems engineering, complex systems, and complex equipment test and evaluation. E-mail: kayyang27@nudt.edu.cn

WANG Xingliang was born in 1998. He received his master degree in management science and engineering with Nationsl University of Defence Technology in 2022. His main research interests are systems engineering, and system portfolio selection and optimization. E-mail: wangxingliangnudt@163.com

XU Chi was born in 1990. He received his master degree in management science and engineering with National University of Defence Technology in 2022. His main research interests are natural language processing and deep learning. E-mail: xuchi20@nudt.edu.cn

LI Minghao was born in 1990. He received his B.E. degree in systems engineering and Ph.D. degree in management science and engineering from National University of Defense Technology, Changsha, China, in 2013 and 2018, respectively. He is a lecturer of management science and engineering with the College of Systems Engineering, National University of Defense Technology. His main research interests are model-based system engineering, defense acquisition, and system-of-systems modeling and optimization. E-mail: liminghao_nudt@foxmail.com
Supported by:
This work was supported by the National Natural Science Foundation of China (71690233; 71971213; 71901214).

Abstract

Abstract:

Architecture framework has become an effective method recently to describe the system of systems (SoS) architecture, such as the United States (US) Department of Defense Architecture Framework Version 2.0 (DoDAF2.0). As a viewpoint in DoDAF2.0, the operational viewpoint (OV) describes operational activities, nodes, and resource flows. The OV models are important for SoS architecture development. However, as the SoS complexity increases, constructing OV models with traditional methods exposes shortcomings, such as inefficient data collection and low modeling standards. Therefore, we propose an intelligent modeling method for five OV models, including operational resource flow OV-2, organizational relationships OV-4, operational activity hierarchy OV-5a, operational activities model OV-5b, and operational activity sequences OV-6c. The main idea of the method is to extract OV architecture data from text and generate interoperable OV models. First, we construct the OV meta model based on the DoDAF2.0 meta model (DM2). Second, OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field (BiLSTM-CRF) model. And OV architecture relationships are collected with relationship extraction rules. Finally, we define the generation rules for OV models and develop an OV modeling tool. We use unmanned surface vehicles (USV) swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.

Key words: system of systems (SoS) architecture, operational viewpoint (OV) model, meta model, bidirectional long short-term memory and conditional random field (BiLSTM-CRF), model generation, systems modeling language

Yue ZHANG, Jiang JIANG, Kewei YANG, Xingliang WANG, Chi XU, Minghao LI. Intelligent modeling method for OV models in DoDAF2.0 based on knowledge graph[J]. Journal of Systems Engineering and Electronics, 2025, 36(1): 139-154.

Figures/Tables 25

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Result of OV relationships extraction"

Number	OV entity relationship	Number	OV entity relationship	Number	OV entity relationship	Number	OV entity relationship
1	$ \text{Include}({A_5},{A_9}) $	16	$ \text{ProvideResource}({O_4},{O_1}) $	31	$ \text{After}({A_8},{A_4}) $	46	$ \text{Produce}({O_4},{I_5}) $
2	$ \text{Include}({A_9},{A_6}) $	17	$ \text{ProvideResource}({O_2},{O_5}) $	32	$ \text{After}({A_{13}},{A_{11}}) $	47	$ \text{Produce}({O_3},{I_1}) $
3	$ \text{Include}({A_7},{A_3}) $	18	$ \text{ProvideResource}({O_3},{O_5}) $	33	$ \text{Perform}({O_2},{A_6}) $	48	$ \text{Consume}({O_5},{I_4}) $
4	$ \text{Include}({A_5},{A_7}) $	19	$ \text{ProvideResource}({O_3},{O_2}) $	34	$ \text{Perform}({O_3},{A_4}) $	49	$ \text{Consume}({O_2},{I_1}) $
5	$ \text{Include}({A_1},{A_8}) $	20	$ \text{Produce}({A_2},{I_4}) $	35	$ \text{Perform}({O_2},{A_2}) $	50	$ \text{Consume}({O_5},{I_3}) $
6	$ \text{Include}({A_9},{A_2}) $	21	$ \text{Produce}({A_4},{I_2}) $	36	$ \text{Perform}({O_5},{A_8}) $	51	$ \text{Consume}({O_5},{I_2}) $
7	$ \text{Include}({A_5},{A_1}) $	22	$ \text{Produce}({A_{14}},{I_1}) $	37	$ \text{Perform}({O_3},{A_{11}}) $	52	$ \text{Consume}({O_4},{I_4}) $
8	$ \text{Include}({A_5},{A_{12}}) $	23	$ \text{Produce}({A_3},{I_6}) $	38	$ \text{Perform}({O_2},{A_{13}}) $	53	$ \text{Consume}({O_3},{I_4}) $
9	$ \text{Include}({A_1},{A_4}) $	24	$ \text{Consume}({A_4},{I_4}) $	39	$ \text{Perform}({O_3},{A_{14}}) $	54	$ \text{Consume}({O_3},{I_3}) $
10	$ \text{Include}({A_{12}},{A_{11}}) $	25	$ \text{Consume}({A_{13}},{I_1}) $	40	$ \text{Perform}({O_1},{A_5}) $	55	$ \text{Consume}({O_1},{I_5}) $
11	$ \text{Include}({A_7},{A_{13}}) $	26	$ \text{Produce}({O_3},{I_4}) $	41	$ \text{Perform}({O_2},{A_3}) $	56	$ \text{Include}({O_5},{O_4}) $
12	$ \text{Before}({A_6},{A_4}) $	27	$ \text{Produce}({O_2},{I_4}) $	42	$ \text{Perform}({O_1},{A_{10}}) $	57	$ \text{Include}({O_1},{O_3}) $
13	$ \text{Before}({A_{11}},{A_3}) $	28	$ \text{Produce}({O_1},{I_4}) $	43	$ \text{Perform}({O_2},{A_{15}}) $	58	$ \text{Include}({O_1},{O_2}) $
14	$ \text{After}({A_4},{A_2}) $	29	$ \text{Produce}({O_2},{I_3}) $	44	$ \text{ProvideResource}({O_1},{O_4}) $	59	$ \text{Coordinate}({O_3},{O_2}) $
15	$ \text{After}({A_{11}},{A_8}) $	30	$ \text{Produce}({O_3},{I_2}) $	45	$ \text{ProvideResource}({O_2},{O_3}) $	60	$ {\mathrm{Command}}({O_5},{O_1}) $

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References 32

1	HAVVA G G, BEDIR T. Analyzing systems engineering concerns in architecture frameworks—a survey study. Proc. of the IEEE International Systems Engineering Symposium, 2018. DOI: 10.1109/SysEng.2018.8544385.
2	ZHAO Q S, YANG K W, CHEN Y W, et al. System of systems engineering and system of systems modeling. Beijing: National Defense Industry Press, 2013. (in Chinese)
3	WAGENHALS L W, SHIN L, KIM D, et al C4ISR architectures: II. a structured analysis approach for architecture design. Systems Engineering, 2000, 3 (4): 248- 287.
4	NI F, WANG M Z, GUO F B, et al Design of SoS architecture based on object-oriented idea. Systems Engineering and Electronics, 2015, 32 (11): 2367- 2373.
5	XU H Y, ZHUANG Y, GU J J A formal modeling method for embedded software architecture. Acta Electonica Sinica, 2014, 42 (8): 1515.
6	CICCHETTI A, CICCOZZI F, PIERANTONIO A Multi-view approaches for software and system modelling: a systematic literature review. Software & Systems Modeling, 2019, 18 (6): 3207- 3233.
7	The MoDAF Development Team. The ministry of defence architecture framework. https://www.gov.uk/guidance/mod-architecture-framework.
8	DANDASHI F, HAUSE M C. UAF for system of systems modeling. Proc. of the 10th System of Systems Engineering Conference, 2015: 199−204.
9	ZHANG M M, CHEN H H, MAO Y, et al An approach to measuring business-IT alignment maturity via DoDAF2.0. Journal of Systems Engineering and Electronics, 2020, 31 (1): 95- 108.
10	ZHENG H X, XIN L, WU J H, et al The on-orbit mission analysis of OTV based on DoDAF. Aircraft Engineering and Aerospace Technology, 2021, 93 (6): 937- 945. doi: 10.1108/AEAT-03-2020-0062
11	DoD Architecture Framework Working Group. DoD architecture framework. https://dodcio.defense.gov/Library/DoD-Architecture-Framework/.
12	GIACHETTI R E Evaluation of the DoDAF meta-model’s support of systems engineering. Procedia Computer Science, 2015, 61, 254- 260. doi: 10.1016/j.procs.2015.09.208
13	WAN H N, SHU Z, HUANG L, et al Theory of metamodel and its application in the development and design of enterprise architecture. Systems Engineering – Theory & Practice, 2012, 32 (4): 847- 853.
14	Object Management Group. Unified modeling language (UML) V2.5.1. https://www.omg.org/spec/UML/.
15	NIKOLAIDOU M, KAPOS G D, TSADIMAS A, et al. Simulating SysML models: overview and challenges. Proc. of the 10th System of Systems Engineering Conference, 2015: 328−333.
16	International Business Machines Corporation (IBM). Overview of IBM engineering systems design rhapsody. https://www.ibm.com/docs/en/rhapsody/9.0.1?topic=overview.
17	CHEN X, HUANG L, LUO X S Analysis and research on SA of architecture modeling tool. Electronic Design Engineering, 2011, 19 (13): 19- 22.
18	Vitech. CORE systems engineering guided tour. https://www.vitechcorp.com/support/documentation/core/900/seguidedtour.pdf.
19	LIU L, WANG D B A review on named entity recognition. Journal of the China Society for Scientific and Technical Information, 2018, 37 (3): 329- 340.
20	COLLINS M, SINGER Y. Unsupervised models for named entity classification. Proc. of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999: 100−110.
21	BIKEL D M An algorithm that learns what’s in a name. Machine Learning, 1999, 34 (1/3): 211- 231.
22	SHUAI Y, SONG T L, WANG J P Integrated parallel forecasting model based on modified fuzzy time series and SVM. Journal of Systems Engineering and Electronics, 2017, 28 (4): 766- 775. doi: 10.21629/JSEE.2017.04.16
23	MCCALLUM A, LI W. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. Proc. of the 7th Conference on Natural Language Learning, 2003: 188−191.
24	ZHANG X Y, LIU Y, SONG J N Short-term orbit prediction based on LSTM neural network. Systems Engineering and Electronics, 2022, 44 (3): 939- 947.
25	HUANG Z H, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging. https://arxiv.org/abs/1508.01991.
26	AITKEN J S. Learning information extraction rules: an inductive logic programming approach. Proc. of the European Conference on Artificial Intelligence, 2002: 355−359.
27	XIE D P, CHANG Q Review of relation extraction. Application Research of Computers, 2020, 37 (7): 1921- 1924.
28	LI D M, ZHANG Y, LI D Y, et al Review of entity relation extraction methods. Journal of Computer Research and Development, 2020, 57 (7): 1424- 1448.
29	GE B F, REN C S, ZHAO Q S, et al Executable architecture modeling and analysis for system-of-systems. Systems Engineering —Theory & Practice, 2011, 31 (11): 2191- 2201.
30	FU J, LUO A M, LUO X S, et al Approach for generating Petri net executable model based on physical exchange specification of architecture. Systems Engineering and Electronics, 2017, 39 (5): 1030- 1035.
31	ZHANG X X, LUO A M, HUANG L, et al Method of creating architecture executable model based on DM2. Journal of National University of Defense Technology, 2013, 40 (2): 27- 33.
32	SONG S N, GUO X Y, XU F C, et al. Research on modeling of USV swarm target defense mission system from DoDAF operational viewpoint. Proc. of the IEEE International Conference on Unmanned Systems, 2021: 214−218.

Model	P	R	F₁
BiLSTM-CRF	87.90	77.83	82.08
BiLSTM	83.96	66.33	74.11
CRF	78.64	61.78	69.20

Rule template	Relationship type
$ {A_i} $ + consume + $ {R_i} $	$ \text{Consume}({A_i},{R_i}) $
$ {A_i} $ + require + $ {R_i} $	$ \text{Consume}({A_i},{R_i}) $
$ {A_i} $ + need + $ {R_i} $	$ \text{Consume}({A_i},{R_i}) $
$ {R_i} $ + are consumed + $ {A_i} $	$ \text{Consume}({A_i},{R_i}) $
$ P{f_i} $ + perform + $ {A_i} $	$ {\mathrm{Perform}}(P{f_i},{A_i}) $
$ P{f_i} $ + complete + $ {A_i} $	$ {\mathrm{Perform}}(P{f_i},{A_i}) $
$ {A_i} $ + require + $ P{f_i} $	$ {\mathrm{Perform}}(P{f_i},{A_i}) $
$ P{f_i} $ + is used to + $ {A_i} $	$ {\mathrm{Perform}}(P{f_i},{A_i}) $
$ {O_i} $ + include + $ {O_j} $	$ \text{Include}({O_i},{O_j}) $
$ {O_j} $ + form + $ {O_i} $	$ \text{Include}({O_i},{O_j}) $
$ {O_j} $ + affiliated to + $ {O_i} $	$ \text{Include}({O_i},{O_j}) $
$ {O_j} $ + part of + $ {O_i} $	$ \text{Include}({O_i},{O_j}) $
$ {O_i} $ + command + $ {O_j} $	$ {\mathrm{Command}}({O_i},{O_j}) $
$ {O_j} $ + obey + $ {O_i} $	$ {\mathrm{Command}}({O_i},{O_j}) $
$\vdots $	$\vdots $

OV meta model	SysML BDD element	Concrete syntax
$ P{f_i} $ $ {O_i} $ $ P{s_i} $ $ {S_i} $	< Performer > Block The block has the same name as the performer and adjunct properties
$ {\mathrm{Produce}}(P{f_i},{R_i}) $ $ {\mathrm{Consume}}(P{f_j},{R_i}) $ $ {\mathrm{ProvideResource}}(P{f_i},P{f_j}) $	< Associative Link > The associative link is one-way and has a “resource” property

OV meta model	SysML BDD element	Concrete syntax
$ {O_i} $	< Organization > Block The block has the same name as the organization and adjunct properties
$ {\mathrm{Include}}({O_i},{O_j}) $	< Part Association >
$ {\mathrm{Coordinate}}({O_i},{O_j}) $	< Associative Link > The associative link is two-way
$ {\mathrm{Command}}({O_i},{O_j}) $	< Associative Link >

OV meta model	SysML BDD element	Concrete syntax
$ {A_i} $	< Activity > Block The block has the same name as the activity and adjunct properties
$ P{f_i} $	< Performer > Block The block has the same name as the performer and adjunct properties
$ {\mathrm{Include}}({A_i},{A_j}) $	< Part Association >
$ {\mathrm{Perform}}(P{f_i},{A_i}) $	< Dependency Associative >

Intelligent modeling method for OV models in DoDAF2.0 based on knowledge graph

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Number	OV architecture named entities	Number	OV architecture named entities	Number	OV architecture named entities
1	$ {I_1}{\text{ = \{ 1}},{\text{drive away command}}\} $	10	$ {A_7}{\text{ = \{ 7}},{\text{action}},/,/,/\} $	19	$ {A_{13}}{\text{ = \{ 13}},{\text{drive away the target}},{I_1},/,{O_2}\} $
2	$ {I_2}{\text{ = \{ 2}},{\text{target intention}}\} $	11	$ {A_6}{\text{ = \{ }}6,{\text{circular patrols}},/,/,{O_2}\} $	20	$ {A_{14}}{\text{ = \{ 14}},{\text{send command}},/,{I_1},{O_3}\} $
3	$ {I_3}{\text{ = \{ 3}},{\text{target image}}\} $	12	$ {A_5}{\text{ = \{ 5}},{\text{defend fixed targets}},/,/,{O_1}\} $	21	$ {A_{15}}{\text{ = \{ 15}},{\text{send environmental data}},/,/,{O_2}\} $
4	$ {I_4}{\text{ = \{ 4}},{\text{environmental data}}\} $	13	$ {A_4}{\text{ = \{ 4}},{\text{determine target intention}},{I_4},{I_2},{O_3}\} $	22	$ {O_1}{\text{ = \{ 1}},{\text{USV swarm}}\} $
5	$ {I_5}{\text{ = \{ 5}},{\text{mission command}}\} $	14	$ {A_8}{\text{ = \{ 8}},{\text{monitor ocean environment}},/,/,{O_5}\} $	23	$ {O_2}{\text{ = \{ 2}},{\text{collaborative USV}}\} $
6	$ {I_6}{\text{ = \{ 6}},{\text{target position}}\} $	15	$ {A_9}{\text{ = \{ 9}},{\text{observation}},/,/,/\} $	24	$ {O_3}{\text{ = \{ 3}},{\text{command USV}}\} $
7	$ {A_1}{\text{ = \{ 1}},{\text{orientation}},/,/,/\} $	16	$ {A_{10}}{\text{ = \{ 10}},{\text{target defense}},/,/,{O_1}\} $	25	$ {O_4}{\text{ = \{ 4}},{\text{console}}\} $
8	$ {A_2}{\text{ = \{ 2}},{\text{environmental data acquisition}},/,{I_4},{O_2}\} $	17	$ {A_{11}}{\text{ = \{ 11}},{\text{determine target of expulsion}},/,/,{O_3}\} $	26	$ {O_5}{\text{ = \{ 5}},{\text{command center}}\} $
9	$ {A_3}{\text{ = \{ 3}},{\text{target observation}},/,{I_6},{O_2}\} $	18	$ {A_{12}}{\text{ = \{ 12}},{\text{decision}},/,/,/\} $

Evaluation indicator	${\mathrm{TE}}$=9	${\mathrm{ME}}$=1	${\mathrm{RE}}$=4
${\mathrm{TE}} + {\mathrm{ME}}$=10	90% (MC)	10% (MM)	−
${\mathrm{TE}} + {\mathrm{RE}}$=13	−	−	30.8% (MR)

Evaluation indicator	${\mathrm{TE}}$=10	${\mathrm{ME}}$=0	${\mathrm{RE}}$=0
${\mathrm{TE}} + {\mathrm{ME}}$=10	100% (MC)	0 (MM)	−
${\mathrm{TE}} + {\mathrm{RE}}$=10	−	−	0% (MR)

Evaluation indicator	${\mathrm{TE}}$=33	${\mathrm{ME}}$=0	${\mathrm{RE}}$=8
${\mathrm{TE}} + {\mathrm{ME}}$=33	100% (MC)	0% (MM)	−
${\mathrm{TE}} + {\mathrm{RE}}$=33	−	−	19.5% (MR)

Evaluation indicator	${\mathrm{TE}}$=27	${\mathrm{ME}}$=0	${\mathrm{RE}}$=0
${\mathrm{TE}} + {\mathrm{ME}}$=27	100% (MC)	0% (MM)	−
${\mathrm{TE}} + {\mathrm{RE}}$=27	−	−	0% (MR)