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Automatic extraction of data governance knowledge from slack chat channels

Brennan, Rob orcid logoORCID: 0000-0001-8236-362X, Quigley, Simon orcid logoORCID: 0000-0002-9102-1901, De Leenheer, Pieter and Maldonado, Alfredo orcid logoORCID: 0000-0001-8426-5249 (2018) Automatic extraction of data governance knowledge from slack chat channels. In: On the Move to Meaningful Internet Systems. OTM 2018 Conferences, 22-26 Oct 2018, Valletta, Malta. ISBN 978-3-030-02670-7

This paper describes a data governance knowledge extraction prototype for Slack channels based on an OWL ontology abstracted from the Collibra data governance operating model and the application of statistical techniques for named entity recognition. This addresses the need to convert unstructured information flows about data assets in an organisation into structured knowledge that can easily be queried for data governance. The abstract nature of the data governance entities to be detected and the informal language of the Slack channel increased the knowledge extraction challenge. In evaluation, the system identified entities in a Slack channel with precision but low recall. This has shown that it is possible to identify data assets and data management tasks in a Slack channel so this is a fruitful topic for further research.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Ontologies; Data Management; Systems of Engagement
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: On the Move to Meaningful Internet Systems. OTM 2018 Conferences Confederated International Conferences. Lecture Notes in Computer Science 11230. Springer International Publishing. ISBN 978-3-030-02670-7
Publisher:Springer International Publishing
Official URL:https://doi.org/10.1007/978-3-030-02671-4_34
Copyright Information:© 2018 Springer Nature
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106), European Regional Development Fund
ID Code:22978
Deposited On:15 Feb 2019 13:01 by Thomas Murtagh . Last Modified 24 Jul 2019 15:01

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