Ontology alignment based on word embedding and random forest classification.
- Submitting institution
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Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- Wiratunga_1
- Type
- E - Conference contribution
- DOI
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10.1007/978-3-030-10925-7_34
- Title of conference / published proceedings
- Machine learning and knowledge discovery in databases
- First page
- 557
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- January
- Year of publication
- 2019
- URL
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-
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel equivalent concept matching metric developed in this paper that led to a geo-NER-tool to enable search and browse of geological documents across international geological agencies is publicly accessible here: https://github.com/BritishGeologicalSurvey/geo-ner-model. The metric introduced in the paper was used by Dr Rachel Heaven (reh@bgs.ac.uk) from the British Geological Survey (BGS), and was demonstrated at a GeoBiodiversityDatabase workshop at the Nanjing Institute of Geology and Palaeontology (China) in 2018 and at a GeoDeepDive workshop at University of Wisconsin-Madison (USA) in 2018, and will be a contribution to the global Loop3D consortium (https://loop3d.org/).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
