dc.contributor.author | Zhuoxun, Zheng | |
dc.contributor.author | Zhou, Baifan | |
dc.contributor.author | Zhou, Dongzhuoran | |
dc.contributor.author | Soylu, Ahmet | |
dc.contributor.author | Kharlamov, Evgeny | |
dc.date.accessioned | 2023-07-11T08:29:18Z | |
dc.date.available | 2023-07-11T08:29:18Z | |
dc.date.created | 2022-11-14T15:48:42Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-1-4503-9236-5 | |
dc.identifier.uri | https://hdl.handle.net/11250/3077558 | |
dc.description.abstract | Data analytics including machine learning (ML) is essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad-hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable knowledge graph (KG), which represent KGs that rely on semantic technologies to formally encode ML knowledge and solutions. These KGs can be translated to executable scripts in a reusable and modularised fashion. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Association for Computing Machinery (ACM) | en_US |
dc.relation.ispartof | Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 | |
dc.relation.ispartofseries | CIKM: Conference on Information and Knowledge Management; | |
dc.title | ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics | en_US |
dc.type | Chapter | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Conference object | en_US |
dc.description.version | acceptedVersion | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 2073734 | |
dc.source.pagenumber | 5 | en_US |
dc.relation.project | Norges forskningsråd: 308817 | en_US |
dc.relation.project | Norges forskningsråd: 237898 | en_US |