From Genesis to Maturity: Managing Knowledge Graph Ecosystems Through Life Cycles

dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Electrónica e Computación
dc.contributor.authorGeisler, Sandra
dc.contributor.authorCappiello, Cinzia
dc.contributor.authorCelino, Irene
dc.contributor.authorChaves Fraga, David
dc.contributor.authorDimou, Anastasia
dc.contributor.authorIglesias-Molina, Ana
dc.contributor.authorLenzerini, Maurizio
dc.contributor.authorRula, Anisa
dc.contributor.authorVan Assche, Dylan
dc.contributor.authorWelten, Sascha
dc.contributor.authorVidal, Maria-Esther
dc.date.accessioned2025-04-24T07:01:40Z
dc.date.available2025-04-24T07:01:40Z
dc.date.issued2025
dc.description.abstractKnowledge graphs (KGs) play a crucial role in the integration and organization of heterogeneous data and knowledge, enabling advanced data analytics and decision-making across various industries. This vision paper addresses critical challenges in managing KGs, emphasizing their relevance in integrating information from disparate sources. We propose the concept of knowledge graph ecosystems and life cycles to systematically manage tasks, e.g., data integration, standardization, continuous updates, efficient querying, and provenance tracking. By adopting our approach, organizations can enhance the accuracy, consistency, and reliability of KGs, thus improving knowledge management, enabling the extraction of valuable insights, and ensuring transparency and accountability.
dc.description.sponsorshipThe authors thank the Dagstuhl team for hosting Seminar 24061 in February 2024,where the initial ideas for this article were developed. This work has been funded by: champI4.0ns (grant 01MJ22011B- BMWK), PNRR Project FAIR (grant: PE0000013- MUR), PERKS (grant: 101120323- Horizon Europe), REOPEN (PID2023-149549NBI00- AEI-Spain), ED431G 2023/04, ED431C 2022/19- Xunta-CE, PRIN 2022 (grant: NEXTCART 2022YXXZH5- MUR), Flanders Make,UGentSpecial Research Fund(grant: BOF20/DOC/132-BOF), TrustKG (grant P99/2020 Leibniz Association).
dc.identifier.doi10.14778/3718057.3718067
dc.identifier.issn2150-8097
dc.identifier.urihttps://hdl.handle.net/10347/41016
dc.issue.number5
dc.journal.titleProceedings of the VLDB Endowment (PVLDB)
dc.language.isoeng
dc.publisherVLDB Endowment
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-149549NB-I00/ES/APROVECHANDO LA INTELIGENCIA ARTIFICIAL PARA UNA MONITORIZACION PREDICTIVA ROBUSTA EN MINERIA DE PROCESOS/
dc.rightsThis work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of this license. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment. Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleFrom Genesis to Maturity: Managing Knowledge Graph Ecosystems Through Life Cycles
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number18
dspace.entity.typePublication
relation.isAuthorOfPublicationff664f8c-843d-44f0-bb5c-fe605fe90289
relation.isAuthorOfPublication.latestForDiscoveryff664f8c-843d-44f0-bb5c-fe605fe90289

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