Using knowledge anchors to facilitate user exploration of data graphs
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-698
- Type
- D - Journal article
- DOI
-
10.3233/SW-190347
- Title of journal
- Semantic Web
- Article number
- -
- First page
- 205
- Volume
- 11
- Issue
- 2
- ISSN
- 1570-0844
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
10.3233/SW-190347
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
B - AI (Artificial Intelligence)
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Unique approach for generating trajectories through semantic data which allows broadening the scope of semantic applications for serendipitous learning. While such learning happens quite often, no prior work explores how this can be supported. Formalisation allows domain independence and applicability in a range of scenarios and contexts (e.g. music/careers) are demonstrated(HT2016)). Presented in Dimitrova’s ICCE2020 invited talk. Part of stream of work enhancing Leeds’ reputation in intelligent exploration of semantic data (IESD), (starting with ImREAL, €4M; Dicode, €3.5M, Dimitova/Thakker organising IESD workshops(15/16) which led to a special issue of the Semantic Web Journal IESD special issue (2019) co-edited by Dimitrova/Thakker.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
