Automatic extraction of data governance
knowledge from slack chat channels
Brennan, RobORCID: 0000-0001-8236-362X, Quigley, SimonORCID: 0000-0002-9102-1901, De Leenheer, Pieter and Maldonado, AlfredoORCID: 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.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Ontologies; Data Management; Systems of Engagement
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