TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
dc.contributor.author | Garechana Anacabe, Gaizka ![]() | |
dc.contributor.author | Río Belver, Rosa María ![]() | |
dc.contributor.author | Zarrabeitia Bilbao, Enara ![]() | |
dc.contributor.author | Álvarez Meaza, Izaskun ![]() | |
dc.date.accessioned | 2022-09-30T14:57:23Z | |
dc.date.available | 2022-09-30T14:57:23Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Scientometrics 127 : 5459-5473 (2022) | es_ES |
dc.identifier.issn | 0138-9130 | |
dc.identifier.issn | 1588-2861 | |
dc.identifier.uri | http://hdl.handle.net/10810/57889 | |
dc.description.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 Naive 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. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | TeknoAssistant | es_ES |
dc.subject | text mining | es_ES |
dc.subject | SAO | es_ES |
dc.subject | naive bayes | es_ES |
dc.subject | NLP | es_ES |
dc.subject | natural language processing | es_ES |
dc.title | TeknoAssistant : a domain specific tech mining approach for technical problem-solving support | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/ | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s11192-022-04280-2 | es_ES |
dc.identifier.doi | 10.1007/s11192-022-04280-2 | |
dc.departamentoes | Organización de empresas | es_ES |
dc.departamentoeu | Enpresen antolakuntza | es_ES |
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material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/