Goble, Carole

Gray, Alasdair J G

Jimenez, Rafael C.

English

The life sciences have a wealth of data resources with a wide range of overlapping content. Key repositories, such as UniProt for protein data or Entrez Gene for gene data, are well known and their content easily discovered through search engines. However, there is a long-tail of bespoke datasets with important content that are not so prominent in search results. Building on the success of Schema.org for making a wide range of structured web content more discoverable and interpretable, e.g. food recipes, the Bioschemas community aim to make life sciences datasets more findable by encouraging data providers to embed Schema.org markup in their resources

The University of Manchester - Institutional Repository

The University of Manchester ResearchBioschemas: From Potato Salad to Protein AnnotationDocument VersionFinal published versionLink to publication record in Manchester Research ExplorerCitation for published version (APA):Gray, A. J. G., Goble, C., & Jimenez, R. C. (2017). Bioschemas: From Potato Salad to Protein Annotation. In N.Nikitina, D. Song, A. Fokoue, & P. Haase (Eds.), ISWC 2017 Posters & Demonstrations and Industry Tracks:Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks co-located with 16th InternationalSemantic Web Conference (ISWC 2017) (urn:nbn:de:0074-1963-7 ed.). (CEUR workshop proceedings; Vol. 1963).RWTH Aachen University. http://ceur-ws.org/Vol-1963/paper579.pdfPublished in:ISWC 2017 Posters & Demonstrations and Industry TracksCiting this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. If citing, it is advised that you check and use thepublisher's definitive version.General rightsCopyright and moral rights for the publications made accessible in the Research Explorer are retained by theauthors and/or other copyright owners and it is a condition of accessing publications that users recognise andabide by the legal requirements associated with these rights.Takedown policyIf you believe that this document breaches copyright please refer to the University of Manchester’s TakedownProcedures [http://man.ac.uk/04Y6Bo] or contact uml.scholarlycommunications@manchester.ac.uk providingrelevant details, so we can investigate your claim.Download date:21. Oct. 2022Bioschemas:From Potato Salad to Protein AnnotationAlasdair J G Gray1,2, Carole Goble1,3, Rafael C Jimenez4, andThe Bioschemas Community51 ELIXIR-UK2 Heriot-Watt University, UK3 University of Manchester, UK4 ELIXIR-Hub, Hinxton Genome Campus, UK5 http://bioschemas.orgAbstract. The life sciences have a wealth of data resources with a widerange of overlapping content. Key repositories, such as UniProt for pro-tein data or Entrez Gene for gene data, are well known and their contenteasily discovered through search engines. However, there is a long-tail ofbespoke datasets with important content that are not so prominent insearch results. Building on the success of Schema.org for making a widerange of structured web content more discoverable and interpretable,e.g. food recipes, the Bioschemas community (http://bioschemas.org)aim to make life sciences datasets more findable by encouraging dataproviders to embed Schema.org markup in their resources.Keywords: Schema.org, metadata, dataset descriptions, data discovery1 IntroductionSchema.org provides a way to add semantic markup to web pages to enable thoseweb pages to become more interpretable by the search engines that index them,and therefore to improve search results [2]. Schema.org markup describes typesof information, which then have properties. For example, Recipe is a type forrepresenting cooking recipes that has properties like cookTime, nutrition, andrecipeIngredient for marking up the characteristics of the recipe. Schema.orgmarkup is increasingly being applied to web pages as it boosts a site’s rankingin search results [2]. Schema.org markup in web pages also enhances the searchexperience for end users; enabling them to make more informed decisions whendeciding between two search results. For example, when searching for a recipefor potato salad the result snippets contain information such as cooking timeand the number of calories (see Fig. 1). These have been extracted from theSchema.org markup of the underlying web pages and enable the user to make adecision without reading the whole web page. Another example of services beingbuilt over Schema.org markup include the content of the knowledge graphs ofthe search engines (also shown in Fig. 1).2 Gray et al.Fig. 1. Search result for potato salad showing rich snippets generated from Schema.orgmarkup as well as content from the Google Knowledge Graph that has been populatedwith Schema.org markup.The life sciences community have a wealth of data resources with a wide rangeof overlapping content. When gathering data about a particular gene or protein,scientists want the data to be aggregated from all available sources. Currentlydata from key repositories, such as UniProt (SIB/EBI) for information aboutproteins [3] or Gene (NIH) for information about genes [1], are well known andeasily gathered. However, there is a long-tail of bespoke datasets with importantcontent whose content are not so readily available. The Bioschemas community6aim to make life sciences datasets more findable by encouraging data providersto embed Schema.org markup in their resources. Thus enabling aggregation ofcontent through a common approach that will enable novel applications. Pre-vious work has added Biomedical terms7, but these are not sufficient for thebreadth of the life sciences community. The Bioschemas community are workingin conjunction with the wider Schema.org community.2 BioschemasWithin the life sciences there is demand to discover more than just generic typeslike Dataset and Event. The Bioschemas community have identified a widerange of discovery use cases searching for different types of biological resources.6 http://bioschemas.org (accessed Sept 2017)7 https://health-lifesci.schema.org/ (accessed Sept 2017)Bioschemas 3Fig. 2. The Protein Specification with the Bioschemas layers highlighting theSchema.org data model, expected cardinality of the property, minimum informationrecommendations, and controlled vocabularies.These include searching for data about a specific biological entity such as aparticular gene or protein, discovering a data repository to deposit experimen-tal results, and identifying the storage location of specific biological samples8.Currently biological types like genes, proteins, and samples are not representedin Schema.org. Bioschemas aims to engage with life science communities rely-ing on existing community agreements to bring forward new biological types toSchema.org.For any given entity type in Schema.org there are a large number of proper-ties available, many inherited from parent types. For example, Dataset has twoproperties (distribution and includedInDataCatalog) but inherits 78 prop-erties from CreativeWork and 11 from Thing. This is far more properties thancan be realistically expected from resource providers, c.f. [2]. Additionally, thiswide range of choice makes it difficult to develop tools to consume markup. Tosupport tools developed to exploit Schema.org markup in web resources, it isbeneficial if the markup is done in a consistent way, i.e. all resources describinga particularly type of entity provide the same set of properties.The Bioschema specifications are being developed in an example driven man-ner in a short timeframe – the ELIXIR Implementation Study runs for just one8 Links to documents containing these use cases can be found on the Bioschemaswebsite http://bioschemas.org/groups/ (accessed Sept 2017)4 Gray et al.calendar year (2017). The Bioschema specifications go beyond simply extendingSchema.org with new types and properties for biological entities. As shown inFig. 2, the Bioschemas specifications layer provides additional constraints overthe Schema.org model. These constraints capture (i) the minimal informationproperties agreed by the community which are mandatory (M), recommended(R), or optional (O), (ii) the cardinality of the property, i.e. whether it is ex-pected to occur once or many times, and (iii) associated controlled vocabularyterms drawn from existing ontologies. Following from the experience of the widerSchema.org community [2], the Bioschemas specifications aim to require just 6properties for any resource type. These properties are being selected based ontheir ability to support indexing and snippet generation to enable a consumerof the search result to discover and distinguish between resources.3 Future WorkThe Bioschemas Implementation Study is currently in the development/testingphase of its lifecycle. To ensure the viability of the specifications from the re-source providers perspective, example deployments are being developed. At thesame time, tools for consuming and exploiting the markup are also being devel-oped. The outcome of both these development processes will feed into the finalrevisions of the specifications and proposed extensions to the core Schema.orgvocabulary.While the Bioschemas community has a primary focus on life sciences data,prominent members of the community are involved with the European Open Sci-ence Cloud project9 with the aim to adopt the Bioschemas approach of definingcommunity agreed Schema.org markup profiles in other scientific disciplines.AcknowledgementsThe work presented here represents the contributions of the whole BioschemasCommunity (http://bioschemas.org/people/). The current work is fundedthrough an ELIXIR Implementation Study (https://www.elixir-europe.org/activities/bioschemas) and the EU ELIXIR-EXCELERATE grant within theResearch Infrastructures programme of Horizon 2020, grant agreement number676559.References1. Brown et al, G.R.: Gene: a gene-centered information resource at ncbi. NAR 43(D1),D36–D42 (2015), http://dx.doi.org/10.1093/nar/gku10552. Guha, R.V., Brickley, D., Macbeth, S.: Big data makes common schemas even morenecessary. CACM 59(2) (2016), http://dx.doi.org/10.1145/28445443. The UniProt Consortium: UniProt: the universal protein knowledgebase. NAR45(D1), D158–D169 (2017), http://dx.doi.org/10.1093/nar/gkw10999 European Open Science Cloud https://eoscpilot.eu/ accessed Sept 2017

Bioschemas: From Potato Salad to Protein Annotation

https://pure.manchester.ac.uk/ws/files/221774536/paper579.pdf

Bioschemas: From Potato Salad to Protein Annotation

Abstract

Similar works

Full text

Available Versions

The University of Manchester - Institutional Repository