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

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...

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Detalles Bibliográficos
Autores: Garechana Anacabe, Gaizka, Río Belver, Rosa María, Zarrabeitia Bilbao, Enara, Álvarez Meaza, Izaskun
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/57889
Acceso en línea:http://hdl.handle.net/10810/57889
Access Level:acceso abierto
Palabra clave:TeknoAssistant
text mining
SAO
naive bayes
NLP
natural language processing
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oai_identifier_str oai:addi.ehu.eus:10810/57889
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spelling TeknoAssistant : a domain specific tech mining approach for technical problem-solving supportGarechana Anacabe, GaizkaRío Belver, Rosa MaríaZarrabeitia Bilbao, EnaraÁlvarez Meaza, IzaskunTeknoAssistanttext miningSAOnaive bayesNLPnatural language processingThis 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.Springer202220222022info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/57889reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://link.springer.com/article/10.1007/s11192-022-04280-2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/© 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/Atribución 3.0 Españaoai:addi.ehu.eus:10810/578892026-06-18T09:23:17Z
dc.title.none.fl_str_mv TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
title TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
spellingShingle TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
Garechana Anacabe, Gaizka
TeknoAssistant
text mining
SAO
naive bayes
NLP
natural language processing
title_short TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
title_full TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
title_fullStr TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
title_full_unstemmed TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
title_sort TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
dc.creator.none.fl_str_mv Garechana Anacabe, Gaizka
Río Belver, Rosa María
Zarrabeitia Bilbao, Enara
Álvarez Meaza, Izaskun
author Garechana Anacabe, Gaizka
author_facet Garechana Anacabe, Gaizka
Río Belver, Rosa María
Zarrabeitia Bilbao, Enara
Álvarez Meaza, Izaskun
author_role author
author2 Río Belver, Rosa María
Zarrabeitia Bilbao, Enara
Álvarez Meaza, Izaskun
author2_role author
author
author
dc.subject.none.fl_str_mv TeknoAssistant
text mining
SAO
naive bayes
NLP
natural language processing
topic TeknoAssistant
text mining
SAO
naive bayes
NLP
natural language processing
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/57889
url http://hdl.handle.net/10810/57889
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://link.springer.com/article/10.1007/s11192-022-04280-2
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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