基于自然语言处理的意图驱动网络表征

doi:10.12305/j.issn.1001-506X.2024.01.36

系统工程与电子技术 ›› 2023, Vol. 46 ›› Issue (1): 318-325.doi: 10.12305/j.issn.1001-506X.2024.01.36

• 通信与网络 • 上一篇

基于自然语言处理的意图驱动网络表征

姬泽阳¹^,², 杨春刚¹^,², 李富强³^,*, 欧阳颖¹, 刘祥林¹

1. 西安电子科技大学通信工程学院, 陕西西安 710071
2. 西安电子科技大学杭州研究院, 浙江杭州 311200
3. 中国电子科技集团公司数据链重点实验室, 陕西西安 710068

收稿日期:2022-11-03 出版日期:2023-12-28 发布日期:2024-01-11
通讯作者: 李富强
作者简介:姬泽阳(1997—), 男, 硕士研究生, 主要研究方向为意图驱动网络和人工智能
杨春刚(1982—), 男, 教授, 主要研究方向为人工智能信息通信网络和意图驱动网络
李富强(1976—), 男, 研究员, 主要研究方向为数据链网络
欧阳颖(1995—), 女, 博士研究生, 主要研究方向为意图驱动网络和天地一体化网络
刘祥林(1999—), 男, 硕士研究生, 主要研究方向为意图驱动网络和意图协商
基金资助:
数据链技术重点实验室开放基金(CLDL-20202314)

Intent-driven network representation based on natural language processing

Zeyang JI¹^,², Chungang YANG¹^,², Fuqiang LI³^,*, Ying OUYANG¹, Xianglin LIU¹

1. School of Communication Engineering, Xidian University, Xi'an 710071, China
2. Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China
3. Data Link Key Laboratory of China Electronics Technology Group Corporation, Xi'an 710068, China

Received:2022-11-03 Online:2023-12-28 Published:2024-01-11
Contact: Fuqiang LI

摘要/Abstract

摘要：

巨量网络规模、复杂网络结构和低效人工配置等问题需要网络配置自动化和无人化。意图驱动网络可实现去人工的网络自动配置, 其中意图表征是关键; 然而, 现有意图表征范式未能形成统一的标准语法规则。提出了自然语言处理与知识图谱相结合的意图驱动网络表征系统, 该系统支持语音、文本等形式的意图输入。所提意图表征方法利用文本检错、纠错和相似度检测技术提升了意图表征的效果, 将意图表征结果保存为知识图谱, 实现了标准、统一的语法规则, 并通过实验验证了该系统的有效性。

关键词: 意图驱动网络, 意图表征, 自然语言处理, 知识图谱

Abstract:

Problems such as massive network scale, complex network structure, and inefficient manual configuration require automated and unmanned network configuration. Intent-driven networks can realize automatic configuration of the network without human labor, where intent representation is the key. However, the existing intent representation paradigm fails to form a uniform standard syntax rule. An intent-driven network representation system based on the combination of natural language processing and knowledge graph is proposed, which supports intentional input in the form of speech and text. The proposed intent representation method uses text error detection, error correction and similarity detection technology to achieve the effect of improving intent representation, saves the intent representation results as a knowledge graph, and realizes standard and unified syntax rules. Finally, the effectiveness of the system is verified by experiments.

Key words: intent-driven network, intent representation, natural language processing, knowledge graph

中图分类号:

TP273+

姬泽阳, 杨春刚, 李富强, 欧阳颖, 刘祥林. 基于自然语言处理的意图驱动网络表征[J]. 系统工程与电子技术, 2023, 46(1): 318-325.

Zeyang JI, Chungang YANG, Fuqiang LI, Ying OUYANG, Xianglin LIU. Intent-driven network representation based on natural language processing[J]. Systems Engineering and Electronics, 2023, 46(1): 318-325.

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参考文献 30

1	PANG L , YANG C G , CHEN D Y , et al. A survey on intent-driven networks[J]. IEEE Access, 2020, 8, 22862- 22873. doi: 10.1109/ACCESS.2020.2969208
2	HORN A, KHERADMAND A, PRASAD M R. A precise and expressive lattice-theoretical framework for efficient network verification[C]//Proc. of the IEEE 27th International Conference on Network Protocols, 2019.
3	ESPOSITO F, WANG J, CONTOLI C, et al. A behavior-driven approach to intent specification for software-defined infrastructure management[C]//Proc. of the IEEE Conference on Network Function Virtualization and Software Defined Networks, 2018.
4	VECHEV M, VANBEVER L, BIRKNER R, et al. Net2Text: query-guided summarization of network forwarding behaviors[C]// Proc. of the 15th USENZX Conference on Networked systems Design and Implementation, 2018: 609-623.
5	JACOBS A S, PFITSCHER R J, RIBEIRO R H, et al. Hey, Lumi! using natural language for {intent-based} network ma-nagement[C]//Proc. of the USENIX Annual Technical Confe-rence, 2021: 625-639.
6	OUYANG Y , YANG C G , SONG Y B , et al. A brief survey and implementation on refinement for intent-driven networking[J]. IEEE Network, 2021, 35 (6): 75- 83. doi: 10.1109/MNET.001.2100194
7	JACOBS A S, PFITSCHER R J, FERREIRA R A, et al. Refining network intents for self-driving networks[C]//Proc. of the Afternoon Workshop on Self-Driving Networks, 2018: 15-21.
8	KIRAN M , POUYOUL E , MERCIAN A , et al. Enabling intent to configure scientific networks for high performance demands[J]. Future Generation Computer Systems, 2018, 79, 205- 214. doi: 10.1016/j.future.2017.04.020
9	ABHASHKUMAR A, KANG J M, BANERJEE S, et al. Supporting diverse dynamic intent-based policies using janus[C]//Proc. of the 13th International Conference on emerging Networking Experiments and Technologies, 2017: 296-309.
10	FOSTER N , HARRISON R , FREEDMAN M J , et al. Frenetic: a network programming language[J]. ACM Sigplan Notices, 2011, 46 (9): 279- 291. doi: 10.1145/2034574.2034812
11	HAM J V D, GROSSO P, POL R V D, et al. Using the network description language in optical networks[C]//Proc. of the 10th IFIP/IEEE International Symposium on Integrated Network Management, 2007: 199-205.
12	SOULÉ R, BASU S, MARANDI P J, et al. Merlin: a language for provisioning network resources[C]//Proc. of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, 2014: 213-226.
13	PRAKASH C , LEE J , TURNER Y , et al. Pga: using graphs to express and automatically reconcile network policies[J]. ACM SIGCOMM Computer Communication Review, 2015, 45 (4): 29- 42. doi: 10.1145/2829988.2787506
14	ANGI A, SACCO A, ESPOSITO F, et al. NLP4: an architecture for intent-driven data plane programmability[C]//Proc. of the IEEE 8th International Conference on Network Softwarization, 2022: 25-30.
15	XIAO Y R, QUAN W, ZHOU H C, et al. Lightweight natural language driven intent translation mechanism for intent based networking[C]//Proc. of the 7th International Confe-rence on Computer and Communication Systems, 2022: 46-51.
16	SONG Y B, YANG C G, ZHANG J M, et al. Full-life cycle intent-driven network verification: challenges and approaches[EB/OL]. [2022-10-03]. https://arxiv.org/abs/2212.09944.
17	TATARINOV I, VIGLAS S D, BEYER K, et al. Storing and querying ordered XML using a relational database system[C]//Proc. of the 2002 ACM SIGMOD International Conference on Management of Data, 2002: 204-215.
18	ANGLES R, THAKKAR H, TOMASZUK D. RDF and property graphs interoperability: status and issues[C]//Proc. of the Alberto Mendelzon Workshop on Foundations of Data Management, 2019.
19	ZHANG J M, GUO J J, YANG C G, et al. A conflict resolution scheme in intent-driven network[C]//Proc. of the IEEE/CIC International Conference on Communications in China, 2021: 23-28.
20	马晗, 唐柔冰, 张义, 等. 语音识别研究综述[J]. 计算机系统应用, 2022, 31 (1): 1- 10.
	MA H , TANG R B , ZHANG Y , et al. Review of speech recognition research[J]. Computer Systems Application, 2022, 31 (1): 1- 10.
21	CHOROWSKI J K , BAHDANAU D , SERDYUK D , et al. Attention-based models for speech recognition[J]. Advances in Neural Information Processing Systems, 2015, 28, 577- 585.
22	LUONG M T, PHAM H, MANNING C D, et al. Effective approaches to attention-based neural machine translation[EB/OL]. [2022-10-03]. https://www.bimco.org/.
23	SHARMA S , GUPTA S . A correction model for real-word errors[J]. Procedia Computer Science, 2015, 70, 99- 106. doi: 10.1016/j.procs.2015.10.047
24	史健婷, 吴林皓, 张英涛, 等. 基于Soft-Masked BERT的新闻文本纠错研究[J]. 计算机技术与发展, 2022, 32 (5): 202- 207.
	SHI J T , WU L H , ZHANG Y T , et al. Research on error correction of news text based on Soft-Masked BERT[J]. Computer Technology and Development, 2022, 32 (5): 202- 207.
25	ZHANG S H, HUANG H R, LIU J C, et al. Spelling error correction with soft-masked BERT[EB/OL]. [2022-10-03]. https://arxiv.org/abs/2005.07421.
26	MIWA M, BANSAL M. End-to-end relation extraction using lstms on sequences and tree structures[EB/OL]. [2022-10-03]. https://arxiv.org/abs/1601.00770.
27	ARORA S, LIANG Y Y, MA T Y, et al. A simple but tough-to-beat baseline for sentence embeddings[C]//Proc. of the International Conference on Learning Representations, 2017.
28	KUSNER M, SUN Y, KOLKIN N, et al. From word embeddings to document distances[C]//Proc. of the International Conference on Machine Learning, 2015: 957-966.
29	SONG Y, SHI S M, LI J, et al. Directional skip-gram: explicitly distinguishing left and right context for word embeddings[C]// Proc. of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018, 2: 175-180.
30	CHANG X T, YANG C G, WANG H, et al. KID: knowledge graph-enabled intent-driven network with digital twin[C]//Proc. of the 27th Asia Pacific Conference on Communications, 2022: 272-277.

现有研究	描述	意图输入形式	意图表征形式
Frenetic^[10]	Frenetic具备有限网络范围和语义, 支持模块的重新设计	高级生命性查询语言	通用网络策略编排库
NDL^[11]	为应用层和网络层提供了通用的语义	RDF	图数据库以RDF保存
Merlin^[12]	使用逻辑谓语、正则表达式和算术公式输入意图, 生成网络策略	声明性程序语言	声明性程序语言
PGA^[13]	将用户意图转化为PGA格式, 实现策略的规范化	类自然语言, 固定参数格式	图结构
Lumi^[5]	使用自然语言处理得到关键信息	自然语言, 人机交互	Nile参数
NLP4^[14]	使用多层感知器将用户意图转化为网络策略	自然语言、语音或文本	P4
[15]	使用自然语言处理对意图进行表征, 如果表征失败, 使用相似度检测对数据库中的意图进行比对	自然语言、语音或文本	知识图谱

意图表征形式	可推理	可复用	可拓展
正则表达式	×	×	×
可拓展标记性语言(extensible markup language, XML)	×	√	√
RDF	√	×	√
知识图谱	√	√	√

源地址	目的地址	业务类型	业务等级	时间
北京A	北京B	数据	普通	2022.9.18
天津C	天津D	语音	重要	2022.10.22
				

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基于自然语言处理的意图驱动网络表征

Intent-driven network representation based on natural language processing

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