Matching disease and phenotype ontologies in the ontology alignment evaluation initiative
| العنوان: | Matching disease and phenotype ontologies in the ontology alignment evaluation initiative |
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| المؤلفون: | Yasmin Alam-Faruque, Martin Koch, Arild Waaler, Ernesto Jiménez-Ruiz, Martin Romacker, Andrea Splendiani, Scott Markel, James Malone, Ian Harrow, Peter Woollard |
| المصدر: | Journal of Biomedical Semantics, Vol 8, Iss 1, Pp 1-13 (2017) Journal of Biomedical Semantics |
| بيانات النشر: | Zenodo, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, Matching (statistics), Consensus, Ontology alignment, Computer Networks and Communications, Computer science, Health Informatics, 02 engineering and technology, Disease, lcsh:Computer applications to medicine. Medical informatics, computer.software_genre, QH301, 03 medical and health sciences, Semantic similarity, 0202 electrical engineering, electronic engineering, information engineering, Humans, Semantic integration, Evaluation, Equivalence (measure theory), business.industry, Research, OAE, Computer Science Applications, Biomedical ontology, 030104 developmental biology, Workflow, Phenotype, Biological Ontologies, lcsh:R858-859.7, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing, Information Systems, Data integration |
| الوصف: | Background The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease. Results Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results. Conclusions Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype. |
| وصف الملف: | application/pdf |
| تدمد: | 2041-1480 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a78f6fa4d0220bcb6bed363ae7c8b4e https://zenodo.org/record/1069877 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....4a78f6fa4d0220bcb6bed363ae7c8b4e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20411480 |
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