Abstract
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.
This research has received funding from the ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded by the European Regional Development Fund.
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Brennan, R., Quigley, S., De Leenheer, P., Maldonado, A. (2018). Automatic Extraction of Data Governance Knowledge from Slack Chat Channels. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11230. Springer, Cham. https://doi.org/10.1007/978-3-030-02671-4_34
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DOI: https://doi.org/10.1007/978-3-030-02671-4_34
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