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Technological troubleshooting based on sentence embedding with deep transformers

Author

Listed:
  • Antonio L. Alfeo

    (University of Pisa)

  • Mario G. C. A. Cimino

    (University of Pisa)

  • Gigliola Vaglini

    (University of Pisa)

Abstract

In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine similarity is processed by the scoring module to match the expected similarity between the two original sentences. Finally, this similarity measure is employed to retrieve the most suitable recorded solutions for the ongoing problem. The effectiveness of the proposed approach is tested (i) against a state-of-the-art competitor and two well-known textual similarity approaches, and (ii) with two case studies, i.e. private company technical assistance reports and a benchmark dataset for semantic textual similarity. With respect to the state-of-the-art, the proposed approach results in comparable retrieval performance and significantly lower management cost: 30-min questionnaires are sufficient to obtain the semantic context knowledge to be injected into our textual search engine.

Suggested Citation

  • Antonio L. Alfeo & Mario G. C. A. Cimino & Gigliola Vaglini, 2021. "Technological troubleshooting based on sentence embedding with deep transformers," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1699-1710, August.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:6:d:10.1007_s10845-021-01797-w
    DOI: 10.1007/s10845-021-01797-w
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    References listed on IDEAS

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    1. Klaus North & Ronald Maier & Oliver Haas, 2018. "Value Creation in the Digitally Enabled Knowledge Economy," Progress in IS, in: Klaus North & Ronald Maier & Oliver Haas (ed.), Knowledge Management in Digital Change, pages 1-29, Springer.
    2. Ruben Costa & Celson Lima & João Sarraipa & Ricardo Jardim-Gonçalves, 2016. "Facilitating knowledge sharing and reuse in building and construction domain: an ontology-based approach," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 263-282, February.
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    Cited by:

    1. Aman Kumar & Binil Starly, 2022. "“FabNER”: information extraction from manufacturing process science domain literature using named entity recognition," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2393-2407, December.
    2. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).

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