=Paper=
{{Paper
|id=Vol-1080/owled2013_5
|storemode=property
|title=Putting OWL into Production at the European Bioinformatics Institute
|pdfUrl=https://ceur-ws.org/Vol-1080/owled2013_5.pdf
|volume=Vol-1080
|dblpUrl=https://dblp.org/rec/conf/owled/MaloneBIJVWP13
}}
==Putting OWL into Production at the European Bioinformatics Institute==
https://ceur-ws.org/Vol-1080/owled2013_5.pdf
Putting OWL into production at the European
Bioinformatics Institute
James Malone, Tony Burdett, Jon Ison, Simon Jupp, Drashtti Vasant, Dani
Welter, and Helen Parkinson
European Bioinformatics Institute, Cambridge, CB10 1SD, UK
Abstract. The Experimental Factor Ontology (EFO) is an OWL based
ontology of experimental variables used in a wide range of biomedical
studies. EFO has been in use since 2007 and was the first OWL ontol-
ogy to be used in production ready data services at the EBI. As the
ontology content, application requirements and the OWL language have
evolved over time, this has presented several challenges in how to develop
EFO and how best to deploy this within the applications. In this paper
we describe our experiences of using OWL and the challenges we have
faced which include; managing imports from multiple ontologies, optimis-
ing axiomatisation for performance, displaying the ontology in domain
specific views, incorporating the ontology into a continuous integration
framework and deploying the ontology in applications and curation tools.
We also describe some of the tools we have developed and applications
EFO is now used in and, finally, discuss our future challenges.
1 Introduction
The European Bioinformatics Institute (EBI) provides freely available data from
life science experiments to help perform research in computational biology. One
of the domains for which EBI provides data is in functional genomics. At EBI,
two databases exist in this area; ArrayExpress Archive [1] and the Expression
Atlas [2]. The ArrayExpress Archive is a database of functional genomics exper-
iments that are either directly submitted by individuals or groups or imported
from the Gene Expression Omnibus. ArrayExpress is curated manually by a
team of highly trained experts to ensure a level of quality and consistency in the
data and the annotations describing them. The Expression Atlas uses a subset
of ArrayExpress to produce a meta-analysis about which genes are expressed
under which conditions (commonly called experimental factors).
A number of use cases were elicited which supported the development and de-
ployment of an ontology for these resources. These were as follows: To encourage
consistency in the annotation of these data; Integration across like experimental
conditions for performing meta-analysis; query support to allow simple subclass
and partonomy query expansion such that a query for brain returns experiments
for brain parts; visualization, to facilitate data exploration; consistency checking
to ensure otherwise disjoint concepts were not used in an inconsistent manner
such as marking the same sample as healthy and cancerous.
To support these we developed the Experimental Factor Ontology (EFO) [3].
EFO is an OWL based application ontology in that it is built towards a specific
application’s use cases distinct from reference ontologies which are considered
to be the de facto point of reference for a given domain. ArrayExpress and
Atlas contain a considerable diversity of variables, touching upon many difference
biomedical domains, as is summarised in Table 1. In order to provide coverage
of these variables, EFO consumes fragments of multiple reference ontologies and
enriches them by providing additional axiomatisation to satisfy specific queries.
For instance, EFO imports a class epithelial cell from the Cell Ontology [4] and
Homo sapiens from the NCBI Taxonomy and uses these in class descriptions
for a cell line which is derived from a human epithelial cell. These additional
axiomatisations do not exist elsewhere.
Table 1. Annotations within ArrayExpress and Atlas
Annotations ArrayExpress Atlas
Species 1,821 73
Samples 1,126,456 98,631
Annotations on sample 7,672,825 463,797
Unique sample annotations 223,650 22,339
Assay 965,638 100,654
Annotations on assay 3,248,298 265,971
Unique assay annotation 189,381 19,136
The development of EFO and deployment into applications has brought with
it a series of challenges for which we have had to develop new approaches and
tools. In this paper we highlight our experiences of building and deploying OWL
ontologies in real-world, high-usage biomedical applications. We outline the con-
tinuous integration system we use to ensure applications do not fail, the compro-
mises which we have had to face due to technology limitations and thoughts on
how our experience can lead to more adoption of OWL ontologies in applications.
2 Consuming the delicious OWL
Several applications now consume EFO in OWL. Both ArrayExpress and Atlas
use the ontology as a search index mechanism. No real time reasoning is un-
dertaken; instead an inferred version of the ontology is created and traversed
to browse the subsumption hierarchy to expand queries, as are classes used in
part of restrictions to fetch partonomies. This also uses annotation properties
to expand over synonyms. In Atlas, the ontology tree is displayed along the sub-
class axis to visualise the experimental variables and can be visually expanded
and explored by the user. Another application is the National Human Genome
Research Institute Catalog of Published Genome-Wide Association Studies (or
GWAS Catalog) which provides a quality controlled, manually curated, collec-
tion of published GWA studies [5]. The studies in the Catalog cover a wide range
of often context-dependent traits and phenotypes, diseases and clinical measure-
ments. These traits are annotated to EFO to improve the querying capabilities
and concepts in the Catalog, such as SNP, study and trait, and the relationships
between them are modeled in an OWL knowledge base which is used as the back
end for this application. This provides advanced querying capabilities and also
drives the new GWAS diagram.
3 Developing an OWL ontology for applications
The software engineering paradigm used within our developer team is that of
Agile Software Development. One of the underlying methods of Agile develop-
ment is that requirements can change over time and should be responded to
in a prompt fashion. The continuous integration and test-driven development
methods enable working version of software to be produced in iterative cycles,
whilst keeping user needs at the fore. When developing EFO we have adopted
the same approach and this has necessitated that we treat the OWL ontology as
a component of software, much like any other in our development framework.
3.1 OWL supporting tools, methods and version control
To actually populate the ontology we have developed several pieces of software
that support our production process. The first is an OWL importer1 which is an
implementation of the MIREOT specification [6]. For creating new classes native
to EFO, we have developed URIGen2 for controlling the minting of new URIs.
URIGen is a client server tool, available as a Protege plugin that serialises the
creation of a URI to ensure that multiple users, editing in Protege across multiple
machines, do not produce URI duplications. We have also introduced our own
bespoke rules for creating views of EFO due to the lack of a formal mechanism for
this in OWL. Because of the multiple applications which consume the ontology,
we have required a mechanism to produce different views to suit the different
user groups. Several mechanisms are used; an annotation property flagging if the
class should be included within a view set, inclusion of an alternative label to be
used for a given view set and an annotation property used as a flag for classes
which we wish to hide (such as upper level classes).
EFO is produced and edited by a multi-developer team, and as with any other
multi-developer project a version control system is critical to ensure developers
can synchronize their changes. We use a Subversion repository to store both our
current development version of EFO and archive previous releases. OWL does
not lend itself to analysis with conventional “diff” tools, since there is no ordering
requirement to each class description within a file. Nevertheless, understanding
the difference between one version of an ontology and another is of much use since
it can help debug an ontology when something has broken (such as the ontology
1
http://www.ebi.ac.uk/fgpt/sw/efoimporter
2
http://www.ebi.ac.uk/fgpt/sw/urigen
becoming inconsistent) and this also helps us to produce release notes for our
users so they can also be aware of changes. We developed and use the Bubastis
tool3 to perform a syntactic diff between named class descriptions within the
OWL to highlight axiomatic changes, deletions and additions of named entities.
Ontology Development
publishes
MIREOT to Website
ut
Importer EFO Web Publisher Plan
o
ks
ec
ed
ts
ch
i ts
edi
Branch:
depends on
Current release success of
Current inferred release Linked
Data
co
m
com
EFO Release Plan
m
its
m
to
its to
depends on
edits Trunk: success of
che
Development version
cks
out
EFO Validation Plan
Fig. 1. The development and deployment process of EFO in an agile framework.
3.2 OWL in an Agile framework
There are several aspects of OWL ontologies that lend themselves well to soft-
ware engineering approaches. The first is the use of test driven development. In a
continuous integration framework, it is necessary to test each commit of code to
ensure that it does not break previously working components and introduce new
bugs and we treat OWL with the same respect. We have developed a series of
automated tests using Bamboo4 that the ontology is ran against after each com-
mit which performs checks such as for: invalid namespaces; IRI fragments outside
accepted conventions; duplicate labels between different classes; synonyms du-
plicated between classes; obsolete classes used in axiomatisation; unit tests for
expected class subsumption (e.g. cancer should be subclass of disease).
Another aspect is performance and the OWL DL profile we restrict to. In
order to fully exploit the querying power of the ontology, we use reasoning to
infer various hierarchies of interest, such as classifications of cell lines by disease
and species, and we need this to happen in a time that is responsive. There
are several methods we use to ensure this remains the case. The first is the use
of design patterns. We restrict axiomatisation to a set of patterns that we have
developed to answer our priority competency questions. The second is to disallow
the addition of new object properties and characteristics on those properties. The
third is to classify the ontology on every commit.
3
http://www.ebi.ac.uk/efo/bubastis
4
http://www.atlassian.com/software/bamboo/
We also employ an automated release cycle to release a new version of EFO
monthly, in order to best coordinate with our application needs. The release is
programmatically performed using a Bamboo build plan which performs tasks
such as creating the inferred version of the ontology, converting the ontology to
OBO format, publishing files to the web, building the EFO website and creating
URLs for classes in the EFO namespace to ensure that concepts described in
EFO fully dereference.
4 Challenges and limitations of using OWL
Although we have managed to use OWL in several applications for a number of
years, we have had to make several compromises. One of the primary challenges
is in producing an ontology which relies on several external ontologies. OWL is
designed to import external resources, however there are implications in doing
so. Importing full ontologies into EFO such as the Gene Ontology would bloat
EFO and introduce redundancy. The MIREOT method [6] aims to bypass some
of these issues but ensures that the richness of axiomatisation is lost. OWL mod-
ule extraction can import fragments but it also suffers from bloat; a potentially
small number of imported classes can increase by ten fold or more. It is appar-
ent there is no simple solution to this approach. If we consider an example in
EFO - importing hierarchy from the NCBI Taxonomy, the subclass tree between
Eukaryota and Homo sapiens is 28 deep yet our needs are to import only a tiny
fragment of this. In one sense, importing all of this is not an OWL problem - it is
an artifact of the ontology, however it speaks to the issue of how one utilises such
ontologies within applications that a user will see. Producing views of OWL is a
much over-looked requirement and we believe the lack of an agreed specification
for generating them is a limitation. One proposal is to use SKOS [7] to indicate
subsets for creating user friendly visualisations.
Reasoners and serving queries such that they respond in subsecond times still
presents limitations for applications with large data stores such as GWAS. Due
to the size of the GWAS knowledge base (more than 18600 individuals in the
current release), and the strong reliance on data properties to represent variables
such as author name and publication date for studies or p-value for SNP-trait
associations, reasoning over the GWAS knowledge base can be slow. While large
reasoning times can be considered an acceptable cost at initial start-up, this
also affects querying and multi-factor DL queries are often unscalable. In order
to retain the power provided by reasoner inference while mitigating scalability
issues, we are currently exploring a hybrid strategy using the GWAS knowledge
base both in its original OWL format and as an RDF triple store.
5 Conclusion
The use of OWL in a production environment requires similar considerations to
using software components. One of the most challenging is in the coupling of an
application to imported ontologies. If we are to utilise the growing number of
OWL ontologies in areas such as biomedicine, then we need ways of coping with
external change which is outside the control of consumers. A recent study [8]
found that a large number of ontologies frequently delete named classes from
their ontologies which could have detrimental effects to applications relying on
them. This is a strong disincentive for application developers to reusing such
ontologies. Treating OWL classes as dynamic resources may be true to the nature
of the language, but it is a barrier to use in biomedicine where provenance and
persistence is of great importance. Similarly, methods for rendering OWL for
different applications is an under-developed area. A mechanism or guidance for
generating view sets would help in deployment of OWL into applications.
6 Acknowledgments
Funding: EMBL and The National Center for Biomedical Ontology, one of
the National Centers for Biomedical Computing supported by the NHGRI, the
NHLBI, and the NIH Common Fund under grant U54-HG004028.
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