HESML: A scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset
- Submitting institution
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The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1459220
- Type
- D - Journal article
- DOI
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10.1016/j.is.2017.02.002
- Title of journal
- Information Systems
- Article number
- -
- First page
- 97
- Volume
- 66
- Issue
- -
- ISSN
- 0306-4379
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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-
- 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
-
4
- Research group(s)
-
-
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This reproducibility paper introduces HESML, a software library that implements 25 existing and new ontology-based semantic similarity metrics based on WordNet, a set of reproducible experiments on word similarity, and a replication framework. This work is a key resource for the Natural Language Processing and Semantic Web Communities, providing a scalable and efficient implementation of state-of-the-art similarity metrics, and a framework for evaluating new proposed metrics against existing ones. It is used as a reference framework for Ontology Learning and Matching competitions (da Silva et al., 2018). Published by the journal of information systems (JCR impact factor 4.267).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
