Hierarchical conceptual spaces for concept combination
- Submitting institution
-
University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 94616800
- Type
- D - Journal article
- DOI
-
10.1016/j.artint.2016.04.008
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 204
- Volume
- 237
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- 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
-
1
- Research group(s)
-
A - Artificial Intelligence and Autonomy
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In natural language, concepts are inherently flexible and graded. They give different weights of importance to defining features, allow for degrees of typicality, and permit a rich array of combination rules. Natural language processing and semantic search can greatly benefit from a richer representation of natural concepts to capture this flexibility. We introduce a new hierarchical representation of concepts which incorporates a probabilistic model of prototypes to account for vague concept boundaries, semantic uncertainty and feature weights within concept ontologies. This well received work published in a leading AI journal contributed to a PhD funded by BCCS CDT EP/I013717/1.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -