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Automatic Extraction of Data Governance Knowledge from Slack Chat Channels

  • Rob Brennan
  • Simon Quigley
  • Pieter De Leenheer
  • Alfredo Maldonado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11230)

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.

Keywords

Ontologies Data management Systems of engagement 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Rob Brennan
    • 1
  • Simon Quigley
    • 1
  • Pieter De Leenheer
    • 2
  • Alfredo Maldonado
    • 1
  1. 1.ADAPT Centre, Computer Science and StatisticsTrinity College DublinDublin 2Ireland
  2. 2.Collibra Research LabNew YorkUSA

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