AI Article Synopsis
- The paper evaluates three semantic knowledge sources (UMLS, FMA-OBO, WordNet) for predicting synonymous anatomical terms between mice and zebrafish.
- Using these specialized knowledge sources leads to highly accurate predictions, which were confirmed through manual curation.
- The authors suggest that combining these sources improves results and can enable reliable ontology mappings with little human oversight.
Article Abstract
In this paper we evaluate the performance and usefulness of three semantic background knowledge sources for predicting synonymous anatomical terms across species boundaries. The reference sources under evaluation are UMLS, FMA-OBO and WordNet, which are applied to the anatomical ontologies of mouse and zebrafish. Our results show that the use of specialized knowledge sources leads to highly accurate predictions, verified through complete manual curation, which can be further improved by combining multiple of said sources. We argue that these three references complement each other in terms of granularity and specificity. From our results we conclude that these references can be used to create reliable ontology mappings with minimal human supervision.
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| http://dx.doi.org/10.2390/biecoll-jib-2010-124 | DOI Listing |
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