Skip to main content

Advertisement

Requirement-service mapping technology in the industrial application field based on large language models

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The article introduces a method of requirements-service mapping based on large-scale language models, utilizing the significant semantic understanding capability of large language models. It leverages multiple rounds of natural language question-answering to interact with users, achieve the transformation of users’ vague requirements into structured information, and eventually map to specific application services. Through combining large language models with traditional vector searching techniques, the micro-adjustment of large language models is realized for extracting and structuring requirements’ information without retraining or inputting massive data to build context. It presents classification of requirements and definition of service attributes to constrain and regulate content of user requirements, providing rules for large language models to express non-structured raw requirements into clear structured information. Upon obtaining the structured information, word embedding is further used to vectorize service information and requirements. The service mapping process is completed through vector matching algorithms, realizing the ultimate transformation from requirements to services. Finally, through industrial application service template, case studies have been conducted to analyze the accuracy of mapping under different requirement rules, thus ultimately demonstrating the effectiveness of the requirement-mapping method proposed in this article.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability and Access

The collected service data is used for proprietary systems and is not available.

References

  1. Agarwal N, Sikka G, Awasthi LK (2020) Enhancing web service clustering using length feature weight method for service description document vector space representation. Expert Syst Appl 161:113682. https://doi.org/10.1016/j.eswa.2020.113682

    Article  Google Scholar 

  2. Agarwal N, Sikka G, Awasthi LK (2024) Integrating semantic similarity with dirichlet multinomial mixture model for enhanced web service clustering. Knowl Inf Syst 66(4):2327–2353. https://doi.org/10.1007/s10115-023-02034-x

    Article  MATH  Google Scholar 

  3. Arya S, Mount DM, Netanyahu NS et al (1998) An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J ACM 45(6):891–92. https://doi.org/10.1145/293347.293348

    Article  MathSciNet  MATH  Google Scholar 

  4. Asudani DS, Nagwani NK, Singh P (2023) Impact of word embedding models on text analytics in deep learning environment: a review. Artif Intell Rev 56(9):10345–1042. https://doi.org/10.1007/s10462-023-10419-1

    Article  MATH  Google Scholar 

  5. Bajaj D, Goel A, Gupta SC et al (2022) Muce: a multilingual use case model extractor using gpt-3. Int J Inf Technol 14(3):1543–155. https://doi.org/10.1007/s41870-022-00884-2

    Article  MATH  Google Scholar 

  6. Bao T, Zhang C (2023) Extracting chinese information with chatgpt:an empirical study by three typical tasks. Data Anal Knowl Discovery 7(1–11)

  7. Bharadiya J (2023) A comprehensive survey of deep learning techniques natural language processing. European J Technol 7(1):58–66. https://doi.org/10.47672/ejt.1473

    Article  MATH  Google Scholar 

  8. Bianchi F, Terragni S, Hovy D (2021) Pre-training is a hot topic: Contextualized document embeddings improve topic coherence. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, Online, pp 759–766. https://doi.org/10.18653/v1/2021.acl-short.96, https://aclanthology.org/2021.acl-short.96

  9. Biswas S, Logan NS, Davies LN et al (2023) Assessing the utility of chatgpt as an artificial intelligence-based large language model for information to answer questions on myopia. Ophthalmic Physiol Opt 43(6):1562–157. https://doi.org/10.1111/opo.13207

    Article  Google Scholar 

  10. Bombieri M, Meli D, Dall’Alba D et al (2023) Mapping natural language procedures descriptions to linear temporal logic templates: an application in the surgical robotic domain. Appl Intell 53(22):26351–26363. https://doi.org/10.1007/s10489-023-04882-0

    Article  Google Scholar 

  11. Brown T, Mann B, Ryder N, et al (2020) Language models are few-shot learners. In: Larochelle H, Ranzato M, Hadsell R, et al (eds) Advances in Neural Information Processing Systems, vol 33. Curran Associates, Inc., pp 1877–1901, https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf

  12. Bu K, Liu Y, Ju X (2024) Efficient utilization of pre-trained models: a review of sentiment analysis via prompt learning. Knowl-Based Syst 283:11114. https://doi.org/10.1016/j.knosys.2023.111148

    Article  MATH  Google Scholar 

  13. Cao X, Liu Y (2023) Relmkg: reasoning with pre-trained language models and knowledge graphs for complex question answering. Appl Intell 53(10):12032–1204. https://doi.org/10.1007/s10489-022-04123-w

    Article  MATH  Google Scholar 

  14. Das A, Balabantaray RC (2019) Mynlidb: a natural language interface to database. In: 2019 International Conference on Information Technology (ICIT), pp 234–238. https://doi.org/10.1109/ICIT48102.2019.00048

  15. Guodong L, Zhang Q, Ding Y et al (2020) Research on service discovery methods based on knowledge graph. IEEE Access 8:138934–138943. https://doi.org/10.1109/ACCESS.2020.3012670

    Article  MATH  Google Scholar 

  16. Haleem A, Javaid M, Singh RP (2022) An era of chatgpt as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2(4):10008. https://doi.org/10.1016/j.tbench.2023.100089

    Article  MATH  Google Scholar 

  17. Horkoff J (2022) Keynote - requirements engineering for machine learning: Non-functional requirements as core functions. In: 2022 IEEE 30th International Requirements Engineering Conference Workshops (REW), pp 141–141. https://doi.org/10.1109/REW56159.2022.00034

  18. Jin D, Jin Z, Chen X et al (2024) Chatmodeler: a human-machine collaborative and iterative requirements elicitation and modeling approach via large language models. J Comput Res Develop 61(02):338–350

    MATH  Google Scholar 

  19. Kim JK, Chua M, Rickard M et al (2023) Chatgpt and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic medicine. J Pediatr Urol 19(5):598–604

    Article  Google Scholar 

  20. Kojima T, Gu SS, Reid M, et al (2022) Large language models are zero-shot reasoners. In: Koyejo S, Mohamed S, Agarwal A, et al (eds) Advances in Neural Information Processing Systems, vol 35. Curran Associates, Inc., pp 22199–22213, https://proceedings.neurips.cc/paper_files/paper/2022/file/8bb0d291acd4acf06ef112099c16f326-Paper-Conference.pdf

  21. Leong IT, Barbosa R (2023) Translating natural language requirements to formal specifications: A study on gpt and symbolic nlp. In: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pp 259–262. https://doi.org/10.1109/DSN-W58399.2023.00065

  22. Li R, Rongcheng P, SHEN J, et al (2024) Knowledge distillation of large language models based on chain of thought. J Data Acquisition Process 39(03):547–558. https://doi.org/10.16337/j.1004-9037.2024.03.004

  23. Liu P, Yuan W, Fu J et al (2023) Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Comput Surv 55(9). https://doi.org/10.1145/3560815

  24. Liu Y, Han T, Ma S et al (2023) Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology 1(2):100017. https://doi.org/10.1016/j.metrad.2023.100017

    Article  MATH  Google Scholar 

  25. Lu X, Deng Y, Sun T et al (2022) Mkpm: multi keyword-pair matching for natural language sentences. Appl Intell 52(2):1878–1892. https://doi.org/10.1007/s10489-021-02306-5

    Article  MATH  Google Scholar 

  26. Malkov Y, Ponomarenko A, Logvinov A et al (2014) Approximate nearest neighbor algorithm based on navigable small world graphs. Inf Syst 45:61–6. https://doi.org/10.1016/j.is.2013.10.006

    Article  Google Scholar 

  27. Malkov YA, Yashunin DA (2020) Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Trans Pattern Anal Mach Intell 42(4):824–83. https://doi.org/10.1109/TPAMI.2018.2889473

    Article  MATH  Google Scholar 

  28. Mihalcea R, Tarau P (2004) Textrank: Bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp 404–411, https://aclanthology.org/W04-3252

  29. Montagna S, Mariani S, Gamberini E et al (2020) Complementing agents with cognitive services: A case study in healthcare. J Med Syst 44(10):18. https://doi.org/10.1007/s10916-020-01621-7

    Article  MATH  Google Scholar 

  30. OpenAI (2024) Openai cookbook. https://github.com/openai/openai-cookbook, accessed: 2024-06-22

  31. Roman (1985) A taxonomy of current issues in requirements engineering. Computer 18(4):14–23. https://doi.org/10.1109/MC.1985.1662861

  32. Saha BK, Gordon P, Gillbrand T (2023) Nlinq: a natural language interface for querying network performance. Appl Intell 53(23):28848–28864. https://doi.org/10.1007/s10489-023-05043-z

    Article  Google Scholar 

  33. Strubell E, Ganesh A, Mccallum A (2019) Energy and policy considerations for deep learning in nlp. pp 3645–3650. https://doi.org/10.18653/v1/P19-1355

  34. Sun Q, Han J, Ma D (2021) A framework for service semantic description based on knowledge graph. Electronics 10(9):101. https://doi.org/10.3390/electronics10091017

    Article  MATH  Google Scholar 

  35. Taherdoost H (2021) Data collection methods and tools for research; a step-by-step guide to choose data collection technique for academic and business research projects authors. Post-Print hal-03741834, HAL, https://ideas.repec.org/p/hal/journl/hal-03741834.html

  36. Wadhwa S, Amir S, Wallace BC (2023) Revisiting relation extraction in the era of large language models. Proc Conf Assoc Comput Linguist Meet 2023:15566–15589

  37. Wang X, Wei J, Schuurmans D, et al (2023) Self-consistency improves chain of thought reasoning in language models. In: The Eleventh International Conference on Learning Representations, https://openreview.net/forum?id=1PL1NIMMrw

  38. Wang Z, Zhang Z, Traverso A et al (2024) Assessing the role of gpt-4 in thyroid ultrasound diagnosis and treatment recommendations: enhancing interpretability with a chain of thought approach. Quant Imaging Med Surg 14(2):1602–1615

    Article  MATH  Google Scholar 

  39. Wei J, Wang X, Schuurmans D, et al (2022) Chain-of-thought prompting elicits reasoning in large language models. In: Koyejo S, Mohamed S, Agarwal A, et al (eds) Advances in Neural Information Processing Systems, vol 35. Curran Associates, Inc., pp 24824–24837, https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf

  40. Xu HD, Mao XL, Yang P et al (2024) Cross-domain coreference modeling in dialogue state tracking with prompt learning. Knowl-Based Syst 283:11118. https://doi.org/10.1016/j.knosys.2023.111189

    Article  MATH  Google Scholar 

  41. Xue S, Ren F (2021) Intent-enhanced attentive bert capsule network for zero-shot intention detection. Neurocomputing 458:1–13. https://doi.org/10.1016/j.neucom.2021.05.085

    Article  Google Scholar 

  42. Yu Y, Zeng J, Yao J, et al (2020) Web service discovery based on knowledge graph and similarity network. In: 2020 IEEE World Congress on Services (SERVICES), pp 231–236. https://doi.org/10.1109/SERVICES48979.2020.00054

  43. Zaki-Ismail A, Osama M, Abdelrazek M, et al (2021) Arf: Automatic requirements formalisation tool. In: 2021 IEEE 29th International Requirements Engineering Conference (RE), pp 440–441. https://doi.org/10.1109/RE51729.2021.00060

  44. Zhang B, Tu Z, Wang C et al (2024) Requirements elicitation and response generation for conversational services. Appl Intell 54(7):5576–559. https://doi.org/10.1007/s10489-024-05454-6

    Article  MATH  Google Scholar 

  45. Zhou D, Schärli N, Hou L, et al (2023) Least-to-most prompting enables complex reasoning in large language models. In: The Eleventh International Conference on Learning Representations, https://openreview.net/forum?id=WZH7099tgfM

Download references

Acknowledgements

This work was supported by National Key Research and Development Program of China (No.2022YFB330570) and Shanghai Science Innovation Action Plan(No.21511104302).

Author information

Authors and Affiliations

Authors

Contributions

All the authors contributed equally to this work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Liu Xianhui.

Ethics declarations

Competing Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Ethical and Informed Consent for Data Used

This article does not contain studies with human participants or animals. As such, informed consent forms are not applicable to this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ruixiang, L., Qiujun, D., Xianhui, L. et al. Requirement-service mapping technology in the industrial application field based on large language models. Appl Intell 55, 70 (2025). https://doi.org/10.1007/s10489-024-05969-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-05969-y

Keywords