Aber-OWL: a framework for ontology-based
             data access in biology

Robert Hoehndorf1 , Luke Slater2 , Paul N Schofied3 , and Georgios V Gkoutos2
 1
    Computational Bioscience Research Center, King Abdullah University of Science
          and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
                        robert.hoehndorf@kaust.edu.sa,
  2
    Department of Computer Science, Aberystwyth University, Aberystwyth, SY23
                                    3DB, UK,
                           {lus11,geg18}@aber.ac.uk,
3
  Department of Physiology, Development & Neuroscience, University of Cambridge,
                         Downing Street, CB2 3EG, UK,
                             pns12@hermes.cam.ac.uk




      Abstract. Many ontologies have been developed in biology and these
      ontologies increasingly contain large volumes of formalized knowledge
      commonly expressed in the Web Ontology Language (OWL). Compu-
      tational access to the knowledge contained within these ontologies re-
      lies on the use of automated reasoning. We have developed the Aber-
      OWL infrastructure that provides reasoning services for bio-ontologies.
      Aber-OWL consists of an ontology repository, a set of web services and
      web interfaces that enable ontology-based semantic access to biologi-
      cal data and literature. Aber-OWL is freely available at http://aber-
      owl.net. Aber-OWL provides a framework for automatically accessing
      information that is annotated with ontologies or contains terms used to
      label classes in ontologies. When using Aber-OWL, access to ontologies
      and data annotated with them is not merely based on class names or
      identifiers but rather on the knowledge the ontologies contain and the
      inferences that can be drawn from it.

      Keywords: ontology-based data access, ontology repository, semantic
      query


    While ontology repositories, such as BioPortal (8) and the Ontology Lookup
Service (OLS) (2), provide web services and interfaces to access ontologies, in-
cluding their metadata such as author names and licensing, the list of classes
and asserted structure, they do not enable computational access to the seman-
tic content of the ontologies and the inferences that can be drawn from them.
Access to the semantic content of ontologies usually requires further inferences
to reveal the consequences of statements (axioms) asserted in an ontology; these
consequences may be automatically derived using an automated reasoner. To the
best of our knowledge, no reasoning infrastructure that supports semantically
enabled access to biological and biomedical ontologies currently exists.
2

     Here, we present Aber-OWL, a reasoning infrastructure over ontologies con-
sisting of an ontology repository, web services that facilitate semantic queries
over ontologies specified by a user or contained in Aber-OWL’s repository, and
a user interface. The Aber-OWL infrastructure can not only enable access to
knowledge contained in ontologies, but crucially can also be used for semantic
queries over data annotated with ontologies, including the large volumes of data
that are increasingly becoming available through public SPARQL endpoints (6).
Allowing access to data through an ontology is known as the “ontology-based
data access” paradigm (1; 7), and can exploit formal information contained in
ontologies to identify possible inconsistencies and incoherent descriptions (4), en-
rich possibly incomplete data with background knowledge so as to obtain more
complete answers to a query (e.g., if a data item referring to an organism has
been characterized with findings of pulmonary stenosis, overriding aorta, ven-
tricular septal defect, and right ventricular hypertrophy, and the ontology – or
the set of ontologies it imports – contains enough information to allow, based on
these four findings, the inference of a Tetralogy of Fallot condition, then the data
item can be returned when querying for Tetralogy of Fallot even in the absence
of it being explicitly declared in database) (5; 1), enrich the data schema used
to query data sources with additional information (e.g., by using a class in a
query that is an inferred super-class of one or more classes that are used to an-
notate data items, but the class itself is never used to characterize data) (1), and
provide a uniform view over multiple data sources with possibly heterogeneous,
multi-modal data (1; 7).
     To demonstrate how Aber-OWL can be used for ontology-based access to
data, we provide a service that performs a semantic search over Pubmed and
Pubmed Central articles using the results of an Aber-OWL query, and a service
that performs SPARQL query extension so that the results of Aber-OWL queries
can be used to retrieve data accessible through public SPARQL endpoints. In
Aber-OWL, following the ontology-based data access paradigm (7; 1), we specify
the features of the relevant information on the ontology- and knowledge level
(3), and retrieve named classes in ontologies satisfying these condition using an
automated reasoner, i.e., a software program that can identify whether a class
in an ontology satisfies certain conditions based on the axioms specified in an
ontology. Subsequently, we embed the resulting information in database, Linked
Data or literature queries.
     Aber-OWL can be accessed at http://aber-owl.net. The Aber-OWL software
is freely available at https://github.com/reality/SparqOWL can be installed lo-
cally by users who wish to provide semantic access to their own ontologies and
support the use of their ontologies in semantic queries.
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