Automatic Extraction of Data Governance Knowledge from Slack Chat Channels
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Conference PaperDate:
2018Access:
openAccessCitation:
Brennan, R., Quigley, S., De Leenheer, P. & Maldonado, A., Automatic Extraction of Data Governance Knowledge from Slack Chat Channels, he 17th International Conference on Ontologies, DataBases, and Applications of Semantics, Malta, 23-24 October 2018Abstract:
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.
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https://link.springer.com/chapter/10.1007/978-3-030-02671-4_34http://hdl.handle.net/2262/91232
Sponsor
Grant Number
Science Foundation Ireland (SFI)
13/RC/2106
Author's Homepage:
http://people.tcd.ie/rbrennahttp://people.tcd.ie/maldona
Sponsor:
Science Foundation Ireland (SFI)Other Titles:
he 17th International Conference on Ontologies, DataBases, and Applications of SemanticsType of material:
Conference PaperURI:
https://link.springer.com/chapter/10.1007/978-3-030-02671-4_34http://hdl.handle.net/2262/91232
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Full text availableSubject (TCD):
Digital Engagement , DATA GOVERNANCE , Natural Language Processing , SEMANTIC WEBMetadata
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