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

Gaizka Garechana (), Rosa Río-Belver (), Enara Zarrabeitia () and Izaskun Alvarez-Meaza ()
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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)

Scientometrics, 2022, vol. 127, issue 9, No 18, 5459-5473

Abstract: 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.

Keywords: TeknoAssistant; Text mining; SAO; Naive Bayes; NLP; Natural language processing (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11192-022-04280-2

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