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TeknoAssistant : a domain specific tech mining approach for technical problem-solving support

Author

Listed:
  • Gaizka Garechana

    (University of the Basque Country (UPV/EHU))

  • Rosa Río-Belver

    (University of the Basque Country (UPV/EHU))

  • Enara Zarrabeitia

    (University of the Basque Country (UPV/EHU))

  • Izaskun Alvarez-Meaza

    (University of the Basque Country (UPV/EHU))

Abstract

This paper presents TeknoAssistant, a domain-specific tech mining method for building a problem–solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naïve Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either “problem”, “solution” or “none” categories, and a problem–solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process.

Suggested Citation

  • Gaizka Garechana & Rosa Río-Belver & Enara Zarrabeitia & Izaskun Alvarez-Meaza, 2022. "TeknoAssistant : a domain specific tech mining approach for technical problem-solving support," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5459-5473, September.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:9:d:10.1007_s11192-022-04280-2
    DOI: 10.1007/s11192-022-04280-2
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    References listed on IDEAS

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