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      Building semantics in the domain of trait data: an OBO Library approach

      Proceedings of TDWG

      Pensoft Publishers

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          Abstract

          As the volume and diversity of digitised trait data grows with ever-increasing speed, there is a clear need to capture the knowledge which contextualises it. Many researchers are addressing similar challenges by using ontology-based approaches to represent knowledge and use it to better structure data across resources, however, there is immense variation in how and for what purpose these ontologies are built. While some approaches emphasise quick and lightweight deployment for specific projects, others spend considerable effort in creating "heavy duty", finely specified semantics for a wide user base. Effectively ontologising trait data collections is likely to require a hybrid of these strategies and must also consider how to meld emerging efforts with those that have matured into well-adopted, production-oriented systems. This contribution will provide an overview of existng ontologies linked to traits, as well as the best practices used to create and develop them within the Open Biological and Biomedical Ontologies (OBO) Foundry and Library (Smith et al. 2007). Specifically, it will outline a collaborative model for future, open development, based on the domain semantics of the Ontology of Biological Attributes (OBA), the Environment Ontology (ENVO; Buttigieg et al. 2013, Buttigieg et al. 2016b), the Population and Community Ontology (PCO; Walls et al. 2014), and recent work on bridging phenotypes and environments (e.g. Thessen et al. 2015). Finally, perspectives on linking trait semantics, and hence trait data, to societal goals via OBO-aligned efforts to represent the semantics of the United Nations' Sustainable Development Agenda for 2030 (e.g. Buttigieg et al. 2016a) will be offered as a means to bridge scientific data with global socio-ecological goals.

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          Most cited references 4

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          The environment ontology: contextualising biological and biomedical entities

          As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO; http://www.environmentontology.org) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s motivation, content, structure, adoption, and governance approach. The ontology is available from http://purl.obolibrary.org/obo/envo.owl - an OBO format version is also available by switching the file suffix to “obo”.
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            Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies

            The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.
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              The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation

              Background The Environment Ontology (ENVO; http://www.environmentontology.org/), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications. Methods We have updated our development suite to promote expressivity, consistency, and speed: we now develop ENVO in the Web Ontology Language (OWL) and employ templating methods to accelerate class creation. We have also taken steps to better align ENVO with the Open Biological and Biomedical Ontologies (OBO) Foundry principles and interoperate with existing OBO ontologies. Further, we applied text-mining approaches to extract habitat information from the Encyclopedia of Life and automatically create experimental habitat classes within ENVO. Results Relative to its state in 2013, ENVO's content, scope, and implementation have been enhanced and much of its existing content revised for improved semantic representation. ENVO now offers representations of habitats, environmental processes, anthropogenic environments, and entities relevant to environmental health initiatives and the global Sustainable Development Agenda for 2030. Several branches of ENVO have been used to incubate and seed new ontologies in previously unrepresented domains such as food and agronomy. The current release version of the ontology, in OWL format, is available at http://purl.obolibrary.org/obo/envo.owl. Conclusions ENVO has been shaped into an ontology which bridges multiple domains including biomedicine, natural and anthropogenic ecology, ‘omics, and socioeconomic development. Through continued interactions with our users and partners, particularly those performing data archiving and sythesis, we anticipate that ENVO’s growth will accelerate in 2017. As always, we invite further contributions and collaboration to advance the semantic representation of the environment, ranging from geographic features and environmental materials, across habitats and ecosystems, to everyday objects in household settings.
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                Author and article information

                Journal
                Proceedings of TDWG
                TDWGProc
                Pensoft Publishers
                2535-0897
                August 14 2017
                August 14 2017
                : 1
                : e20293
                Article
                10.3897/tdwgproceedings.1.20293
                © 2017

                http://creativecommons.org/licenses/by/4.0/

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