Conference item
Learning relations from social tagging data
- Abstract:
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An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
-
(Accepted manuscript, pdf, 519.0KB)
-
- Publisher copy:
- 10.1007/978-3-319-97304-3_3
Authors
Bibliographic Details
- Publisher:
- Springer Publisher's website
- Host title:
- PRICAI 2018: Trends in Artificial Intelligence
- Series:
- Lecture Notes in Computer Science
- Volume:
- 11012
- Pages:
- 29-41
- Publication date:
- 2018-07-27
- Acceptance date:
- 2018-01-01
- DOI:
- EISSN:
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1611-3349
- ISSN:
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0302-9743
- ISBN:
- 978-3-319-97303-6
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1264319
- Local pid:
- pubs:1264319
- Deposit date:
- 2022-09-13
Terms of use
- Copyright holder:
- Springer Nature Switzerland AG
- Copyright date:
- 2018
- Rights statement:
- © 2018 Springer Nature Switzerland AG
- Notes:
-
This is the accepted manuscript version of the article. The final version is available from Springer at https://doi.org/10.1007/978-3-319-97304-3_3
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