Performance assessment of ontology matching systems for FAIR data

© The Author(s). 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application from the rare disease domain. Additionally, we present a method for analyzing top-level classes to improve precision. Results: We included three ontologies (NCIt, SNOMED CT, ORDO) and three matching systems (AgreementMakerLight 2.0, FCA-Map, LogMap 2.0). We evaluated the performance of the matching systems against reference alignments from BioPortal and the Unified Medical Language System Metathesaurus (UMLS). Then, we analyzed the top-level ancestors of matched classes, to detect incorrect mappings without consulting a reference alignment. To detect such incorrect mappings, we manually matched semantically equivalent top-level classes of ontology pairs. AgreementMakerLight 2.0, FCA-Map, and LogMap 2.0 had F1-scores of 0.55, 0.46, 0.55 for BioPortal and 0.66, 0.53, 0.58 for the UMLS respectively. Using vote-based consensus alignments increased performance across the board. Evaluation with manually created top-level hierarchy mappings revealed that on average 90% of the mappings’ classes belonged to top-level classes that matched. Conclusions: Our findings show that the included ontology matching systems automatically produced mappings that were modestly accurate according to our evaluation. The hierarchical analysis of mappings seems promising when no reference alignments are available. All in all, the systems show potential to be implemented as part of an ontology matching service for querying FAIR data. Future research should focus on developing methods for the evaluation of mappings used in such mapping services, leading to their implementation in a FAIR data ecosystem

Tropical Ungulates of Argentina

Argentina has an extensive and diverse terrain classified into 11 ecoregions. Seven of these ecoregions, occupying the north and north-central parts of the country, house the 11 tropical ungulate species found here. The ecoregions are lowland and subtropical, some beginning in the tropics, some extending to temperate climates. The principal topographical characteristics, hydrology, climate, vegetation and fauna are described for these seven ecoregions. Each of the 11 species is then treated in detail with respect to its ecology and conservation. Emphasis is placed on distribution, habitat and density, feeding ecology, threats and conservation in Argentina, based on the most recent studies. Data on reproductive biology and behaviour are included where information is relatively recent and unlikely to be covered elsewhere. The species include the following: the Brazilian tapir (Tapirus terrestris), found in northern subtropical ecoregions, three species of peccary (Tayassu pecari, Pecari tajacu and Parachoerus wagneri) from northern subtropical and drier regions, of which the Chacoan peccary (P. wagneri) is endemic while the other two species have more extensive distributions. The guanaco (Lama guanicoe) occurs only in relict populations in the ecoregions considered. The taruca (Hippocamelus antisensis) occupies the eastern boundary between the Yungas and drier, high altitude ecoregions. Three species of brocket deer (Mazama americana, M. gouazoubira and M. nana) occupy the northern tropical, subtropical and Chacoan areas. The marsh deer (Blastocerus dichotomus), the largest South American deer, has small populations occupying wetlands from the northern border to the Parana delta, while the pampas deer (Ozotocerus bezoaticus) is found in four isolated populations from Ibera to Buenos Aires province. Argentina represents the southern limit to the distribution of all these species and thus threats are often magnified. Ongoing conservation activities include the maintenance of protected areas, promotion (difusion, education, sensitization), investigation and the reintroduction of some species of formerly extinct ungulates into the Ibera wetlands area.Fil: Black Decima, Patricia. Universidad Nacional de Tucumán; ArgentinaFil: Camino, Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; ArgentinaFil: Cirignoli, Sebastian. Centro de Investigaciones del Bosque Atlántico; ArgentinaFil: de Bustos, Soledad. Fundación Biodiversidad; ArgentinaFil: Matteucci, Silvia Diana. Universidad de Buenos Aires. Facultad de Arquitectura y Urbanismo. Grupo de Ecología del Paisaje y Medio Ambiente; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Perez Carusi, Lorena Cynthia. Administración de Parques Nacionales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Varela, Diego Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Universidad Nacional de Misiones. Instituto de Biología Subtropical; Argentina. Centro de Investigaciones del Bosque Atlántico; Argentin