=Paper=
{{Paper
|id=Vol-1963/paper579
|storemode=property
|title=Bioschemas: From Potato Salad to Protein Annotation
|pdfUrl=https://ceur-ws.org/Vol-1963/paper579.pdf
|volume=Vol-1963
|authors=Alasdair Gray,Carole Goble,Rafael Jimenez
|dblpUrl=https://dblp.org/rec/conf/semweb/GrayGJ17
}}
==Bioschemas: From Potato Salad to Protein Annotation==
https://ceur-ws.org/Vol-1963/paper579.pdf
Bioschemas:
From Potato Salad to Protein Annotation
Alasdair J G Gray1,2 , Carole Goble1,3 , Rafael C Jimenez4 , and
The Bioschemas Community5
1
ELIXIR-UK
2
Heriot-Watt University, UK
3
University of Manchester, UK
4
ELIXIR-Hub, Hinxton Genome Campus, UK
5
http://bioschemas.org
Abstract. The life sciences have a wealth of data resources with a wide
range of overlapping content. Key repositories, such as UniProt for pro-
tein 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 (http://bioschemas.org)
aim to make life sciences datasets more findable by encouraging data
providers to embed Schema.org markup in their resources.
Keywords: Schema.org, metadata, dataset descriptions, data discovery
1 Introduction
Schema.org provides a way to add semantic markup to web pages to enable those
web pages to become more interpretable by the search engines that index them,
and therefore to improve search results [2]. Schema.org markup describes types
of information, which then have properties. For example, Recipe is a type for
representing cooking recipes that has properties like cookTime, nutrition, and
recipeIngredient for marking up the characteristics of the recipe. Schema.org
markup is increasingly being applied to web pages as it boosts a site’s ranking
in search results [2]. Schema.org markup in web pages also enhances the search
experience for end users; enabling them to make more informed decisions when
deciding between two search results. For example, when searching for a recipe
for potato salad the result snippets contain information such as cooking time
and the number of calories (see Fig. 1). These have been extracted from the
Schema.org markup of the underlying web pages and enable the user to make a
decision without reading the whole web page. Another example of services being
built over Schema.org markup include the content of the knowledge graphs of
the search engines (also shown in Fig. 1).
2 Gray et al.
Fig. 1. Search result for potato salad showing rich snippets generated from Schema.org
markup as well as content from the Google Knowledge Graph that has been populated
with Schema.org markup.
The life sciences community have a wealth of data resources with a wide range
of overlapping content. When gathering data about a particular gene or protein,
scientists want the data to be aggregated from all available sources. Currently
data from key repositories, such as UniProt (SIB/EBI) for information about
proteins [3] or Gene (NIH) for information about genes [1], are well known and
easily gathered. However, there is a long-tail of bespoke datasets with important
content whose content are not so readily available. The Bioschemas community6
aim to make life sciences datasets more findable by encouraging data providers
to embed Schema.org markup in their resources. Thus enabling aggregation of
content through a common approach that will enable novel applications. Pre-
vious work has added Biomedical terms7 , but these are not sufficient for the
breadth of the life sciences community. The Bioschemas community are working
in conjunction with the wider Schema.org community.
2 Bioschemas
Within the life sciences there is demand to discover more than just generic types
like Dataset and Event. The Bioschemas community have identified a wide
range 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 3
Fig. 2. The Protein Specification with the Bioschemas layers highlighting the
Schema.org data model, expected cardinality of the property, minimum information
recommendations, and controlled vocabularies.
These include searching for data about a specific biological entity such as a
particular 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 represented
in Schema.org. Bioschemas aims to engage with life science communities rely-
ing on existing community agreements to bring forward new biological types to
Schema.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 two
properties (distribution and includedInDataCatalog) but inherits 78 prop-
erties from CreativeWork and 11 from Thing. This is far more properties than
can be realistically expected from resource providers, c.f. [2]. Additionally, this
wide range of choice makes it difficult to develop tools to consume markup. To
support tools developed to exploit Schema.org markup in web resources, it is
beneficial if the markup is done in a consistent way, i.e. all resources describing
a 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 one
8
Links to documents containing these use cases can be found on the Bioschemas
website http://bioschemas.org/groups/ (accessed Sept 2017)
4 Gray et al.
calendar year (2017). The Bioschema specifications go beyond simply extending
Schema.org with new types and properties for biological entities. As shown in
Fig. 2, the Bioschemas specifications layer provides additional constraints over
the Schema.org model. These constraints capture (i) the minimal information
properties 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 vocabulary
terms drawn from existing ontologies. Following from the experience of the wider
Schema.org community [2], the Bioschemas specifications aim to require just 6
properties for any resource type. These properties are being selected based on
their ability to support indexing and snippet generation to enable a consumer
of the search result to discover and distinguish between resources.
3 Future Work
The Bioschemas Implementation Study is currently in the development/testing
phase of its lifecycle. To ensure the viability of the specifications from the re-
source providers perspective, example deployments are being developed. At the
same time, tools for consuming and exploiting the markup are also being devel-
oped. The outcome of both these development processes will feed into the final
revisions of the specifications and proposed extensions to the core Schema.org
vocabulary.
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 defining
community agreed Schema.org markup profiles in other scientific disciplines.
Acknowledgements
The work presented here represents the contributions of the whole Bioschemas
Community (http://bioschemas.org/people/). The current work is funded
through an ELIXIR Implementation Study (https://www.elixir-europe.org/
activities/bioschemas) and the EU ELIXIR-EXCELERATE grant within the
Research Infrastructures programme of Horizon 2020, grant agreement number
676559.
References
1. 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/gku1055
2. Guha, R.V., Brickley, D., Macbeth, S.: Big data makes common schemas even more
necessary. CACM 59(2) (2016), http://dx.doi.org/10.1145/2844544
3. The UniProt Consortium: UniProt: the universal protein knowledgebase. NAR
45(D1), D158–D169 (2017), http://dx.doi.org/10.1093/nar/gkw1099
9
European Open Science Cloud https://eoscpilot.eu/ accessed Sept 2017