Improving comprehension of Knowledge Representation languages: a case study with Description Logics
- Submitting institution
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The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1451115
- Type
- D - Journal article
- DOI
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10.1016/j.ijhcs.2018.08.009
- Title of journal
- International Journal of Human-Computer Studies
- Article number
- -
- First page
- 145
- Volume
- 122
- Issue
- -
- ISSN
- 1071-5819
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2018
- 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
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3
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is part of a line of research on Empirical Knowledge Representation, which focuses on the difficulties people experience in understanding and using Knowledge Representation (KR) formalisms. In particular, the paper focuses on Description Logic and presents three empirical studies, which demonstrate that the adoption of alternative syntactic constructs improves user comprehension and performance on KR tasks. The paper, which was published in IJHCS, a leading journal in Human Computer Interaction with a 15% acceptance rate, provides important usability insights to researchers and practitioners concerned with KR teaching and the design and implementation of KR solutions.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -