Capturing and anticipating user intents in data analytics via knowledge graphs

In today’s data-driven world, the ability to extract meaningful information from data is becoming essential for businesses, organizations and researchers. For this purpose, a wide range of tools and systems exists addressing data-related tasks, from data integration, preprocessing and modeling, to t...

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Detalles Bibliográficos
Autores: Pons Recasens, Gerard|||0000-0003-2225-3255, Bilalli, Besim|||0000-0002-0575-2389, Queralt Calafat, Anna|||0000-0003-2782-2955
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:dnet:upcommonspor::91788d9617014cf884f0a003a049a102
Acceso en línea:https://hdl.handle.net/2117/460085
https://dx.doi.org/10.1016/j.knosys.2026.115835
Access Level:acceso abierto
Palabra clave:Intentional analytics
Knowledge graphs
Link prediction
Graph embeddings
Graph neural networks
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
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network_acronym_str ES
network_name_str España
repository_id_str
spelling Capturing and anticipating user intents in data analytics via knowledge graphsPons Recasens, Gerard|||0000-0003-2225-3255Bilalli, Besim|||0000-0002-0575-2389Queralt Calafat, Anna|||0000-0003-2782-2955Intentional analyticsKnowledge graphsLink predictionGraph embeddingsGraph neural networksÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixementIn today’s data-driven world, the ability to extract meaningful information from data is becoming essential for businesses, organizations and researchers. For this purpose, a wide range of tools and systems exists addressing data-related tasks, from data integration, preprocessing and modeling, to the interpretation and evaluation of the results. As data continues to grow in volume and complexity, there is an increasing need for advanced yet user-friendly tools, such as intelligent discovery assistants (IDAs) or automated machine learning (AutoML) systems, that facilitate the user’s interaction with data. This enables non-expert users to effectively leverage powerful data analytics techniques. However, the use of these tools still requires non-trivial user input that cannot be anticipated from the analytical problem’s data alone, but must be tailored to each individual user and their specific intents. To this end, this work explores the use of Knowledge Graphs (KG) as a foundational representation for capturing complex analytics workflows, as well as information about the users, their intents and their feedback, in order to facilitate user interaction with IDAs or AutoML tools. This is achieved through established techniques from recommender systems, in particular a link prediction approach based on KG embeddings and graph neural networks. Experimental results show that the proposed method effectively captures the graph structure and produces meaningful suggestions for users. To demonstrate the feasibility of the approach, a working prototype is presented.This work is supported by the Horizon Europe Programme under GA.101093164 (ExtremeXP) and the Spanish Ministerio de Ciencia e Innovación under project PID2023-152841OAI00/AEI/10.13039/501100011033 (TALC).Peer Reviewed20262026-05-2320262026-03-31journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/460085https://dx.doi.org/10.1016/j.knosys.2026.115835reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/501100000780 HE 101093164 EXPeriment driven and user eXPerience oriented analytics for eXtremely Precise outcomes and decisionsAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-152841OA-I00 HACIA UN CICLO DE VIDA AUTOMATIZADO DE DATOS CENTRADO EN LA IAopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:upcommonspor::91788d9617014cf884f0a003a049a1022026-05-27T15:37:01Z
dc.title.none.fl_str_mv Capturing and anticipating user intents in data analytics via knowledge graphs
title Capturing and anticipating user intents in data analytics via knowledge graphs
spellingShingle Capturing and anticipating user intents in data analytics via knowledge graphs
Pons Recasens, Gerard|||0000-0003-2225-3255
Intentional analytics
Knowledge graphs
Link prediction
Graph embeddings
Graph neural networks
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
title_short Capturing and anticipating user intents in data analytics via knowledge graphs
title_full Capturing and anticipating user intents in data analytics via knowledge graphs
title_fullStr Capturing and anticipating user intents in data analytics via knowledge graphs
title_full_unstemmed Capturing and anticipating user intents in data analytics via knowledge graphs
title_sort Capturing and anticipating user intents in data analytics via knowledge graphs
dc.creator.none.fl_str_mv Pons Recasens, Gerard|||0000-0003-2225-3255
Bilalli, Besim|||0000-0002-0575-2389
Queralt Calafat, Anna|||0000-0003-2782-2955
author Pons Recasens, Gerard|||0000-0003-2225-3255
author_facet Pons Recasens, Gerard|||0000-0003-2225-3255
Bilalli, Besim|||0000-0002-0575-2389
Queralt Calafat, Anna|||0000-0003-2782-2955
author_role author
author2 Bilalli, Besim|||0000-0002-0575-2389
Queralt Calafat, Anna|||0000-0003-2782-2955
author2_role author
author
dc.subject.none.fl_str_mv Intentional analytics
Knowledge graphs
Link prediction
Graph embeddings
Graph neural networks
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
topic Intentional analytics
Knowledge graphs
Link prediction
Graph embeddings
Graph neural networks
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
description In today’s data-driven world, the ability to extract meaningful information from data is becoming essential for businesses, organizations and researchers. For this purpose, a wide range of tools and systems exists addressing data-related tasks, from data integration, preprocessing and modeling, to the interpretation and evaluation of the results. As data continues to grow in volume and complexity, there is an increasing need for advanced yet user-friendly tools, such as intelligent discovery assistants (IDAs) or automated machine learning (AutoML) systems, that facilitate the user’s interaction with data. This enables non-expert users to effectively leverage powerful data analytics techniques. However, the use of these tools still requires non-trivial user input that cannot be anticipated from the analytical problem’s data alone, but must be tailored to each individual user and their specific intents. To this end, this work explores the use of Knowledge Graphs (KG) as a foundational representation for capturing complex analytics workflows, as well as information about the users, their intents and their feedback, in order to facilitate user interaction with IDAs or AutoML tools. This is achieved through established techniques from recommender systems, in particular a link prediction approach based on KG embeddings and graph neural networks. Experimental results show that the proposed method effectively captures the graph structure and produces meaningful suggestions for users. To demonstrate the feasibility of the approach, a working prototype is presented.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-05-23
2026
2026-03-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/460085
https://dx.doi.org/10.1016/j.knosys.2026.115835
url https://hdl.handle.net/2117/460085
https://dx.doi.org/10.1016/j.knosys.2026.115835
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/501100000780 HE 101093164 EXPeriment driven and user eXPerience oriented analytics for eXtremely Precise outcomes and decisions
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-152841OA-I00 HACIA UN CICLO DE VIDA AUTOMATIZADO DE DATOS CENTRADO EN LA IA
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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