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# Visualizing ontologies with AberOWL

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## Abstract and Figures

Ontologies are formal theories that specify the kinds of entities and relations found in a domain. To quickly gain access to the content and structure of ontologies, ontology visualization techniques are commonly used. Visualization of ontologies often uses representations of hierarchical structures that are extracted from ontologies, most notably representations of the taxonomic relationships between classes. These graph-based representations can also be used to visualize structural changes in ontologies. We have developed a novel visualization environment for ontologies in which automated reasoning is used to generate a graph-based representation of an ontology's deductive closure, and sub-class relations as well as description logic axioms that are entailed to hold between two classes are represented visually. The visualization environment can also be used to show differences between the entailed axioms of different ontology versions. The source code of the visualization environment is freely available, and we added our visualization environment to AberOWL (http://aber-owl.net), an ontology repository that contains over 400 ontologies, all of which can now be visually explored using our system.
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Visualizing ontologies with AberOWL
Miguel ´
Angel Rodr´ıguez-Garc´ıa1, Luke Slater1, Keiron O’Shea1,2, Paul N
Schoﬁeld3, Georgios V Gkoutos2, and Robert Hoehndorf1
1Computational Bioscience Research Center, King Abdullah University of Science
and Technology, Thuwal 23955-6900, KSA
{miguel.rodriguezgarcia,luke.slater,keironteilo.oshea,robert.hoehndorf}@kaust.edu.sa
2Aberystwyth University, Aberystwyth, SY23 3DB, Wales, UK,
geg18@aber.ac.uk
3University of Cambridge, Downing Street, CB2 3EG, England, UK
pns12@hermes.cam.ac.uk
Abstract. Ontologies are formal theories that specify the kinds of en-
tities and relations found in a domain. To quickly gain access to the
content and structure of ontologies, ontology visualization techniques
are commonly used. Visualization of ontologies often uses representa-
tions of hierarchical structures that are extracted from ontologies, most
notably representations of the taxonomic relationships between classes.
These graph-based representations can also be used to visualize struc-
tural changes in ontologies. We have developed a novel visualization envi-
ronment for ontologies in which automated reasoning is used to generate
a graph-based representation of an ontology’s deductive closure, and sub-
class relations as well as description logic axioms that are entailed to hold
between two classes are represented visually. The visualization environ-
ment can also be used to show diﬀerences between the entailed axioms of
diﬀerent ontology versions. The source code of the visualization environ-
ment is freely available, and we added our visualization environment to
AberOWL (http://aber-owl.net), an ontology repository that contains
over 400 ontologies, all of which can now be visually explored using our
system.
Keywords: biomedical ontology, visualization, automated reasoning
1 Introduction
In recent years, a large number of ontologies has been developed across many
scientiﬁc domains. These ontologies are often formalized in languages such as
the Web Ontology Language (OWL) [5] or an OWL-compatible language such
as the OBO Flatﬁle Format [12]. The major role of ontologies in biology and
biomedicine is in data integration, as they formally describe the kinds of biolog-
ical entities found within a domain, and their interrelations, and can therefore
be used to provide semantic annotations that can be shared across databases.
Along with the increase in the number of ontologies, the need to develop
tools that enable both ontology experts and domain experts to interact with
2
ontologies has grown as well. One crucial aspect of interacting with ontologies is
the ability to browse and visualize the content of ontologies. A widely used form
of visualization for ontologies are graphs that represent classes and the axioms
that hold between these classes. This form of representation is used in ontology
editors such as Prot´eg´e [22] or the (now abandoned) OBO-Edit [24], as well as
in ontology repositories such as BioPortal [21], OntoBee [31] or the Ontology
Lookup Service (OLS) [2]. There are two key features based on which methods
for visualizing ontologies as graph diﬀer: the kind of ‘relations’ that are shown
between classes as part of the graph structure, and whether only the asserted
axioms are used to generate the graph structure or the inferences that can be
drawn from these axioms.
Relations between classes [26] have traditionally been used for biological and
biomedical ontologies, with the intention to represent axiom patterns that hold
between two classes. In its simplest form, a relation is-a between two classes
Xand Yexpresses a subclass axiom that holds between the two classes. How-
ever, many ontologies employ more complex axiom patterns, such as the Part-of
pattern between two classes: Xand Yare said to stand in the relation Part-of
if and only if Xis a subclass of part-of some Y [8]. In ontology repositories
and ontology editors, these kinds of relations are rarely shown, with OLS and
OBO-Edit as exceptions.
Another key distinguishing feature is whether only asserted axioms in the
ontology are visualized or also inferred statements. In Prot´eg´e, and to some
degree in OBO-Edit, it is possible to explore inferred relations between classes
visually. In Prot´eg´e, these relations are limited to subclass axioms, while OBO-
Edit is also able to show other kinds of relations.
Finally, visualization can also aid to structurally identify diﬀerences between
ontologies, or between diﬀerent versions of one ontology. Similarly, a key compo-
nent in exploring and visualizing diﬀerences in ontology versions is whether only
syntactic changes are identiﬁed or whether diﬀerences are also identiﬁed based
on inferred axioms.
Here, we present an ontology visualization environment that provides a sim-
ple and intuitive way to represent classes in ontologies and their interrelations.
The visualization environment employs an automated reasoner to identify axiom
patterns that hold between two classes, thereby visualizing the inferences that
can be drawn from an ontology, including complex patterns that represent more
than simple subclass relations. The environment can also be used to visualize
multiple ontologies at the same time, thereby enabling the exploration of diﬀer-
ences between ontologies and ontology versions. The visualization environment
we developed is integrated in the AberOWL ontology repository [9], available at
thereby allows exploring these ontologies, and their diﬀerent versions, visually.
3
2 A brief overview of AberOWL
AberOWL is an ontology repository and framework for ontology-based data
through a web interface and a REST API. AberOWL is constituted of three
main modules: the AberOWL server, the AberOWL synchronization service,
and the AberOWL web repository.
The AberOWL server provides the core of the system. It uses the ELK rea-
soner [14], an OWL reasoner supporting the OWL EL proﬁle [18], to ensure
polynominal-time reasoning and querying. The ELK reasoner is fast enough for
many practical uses even when applied to large ontologies [25]. The reasoner
is used to classify each ontology and the server maintains a classiﬁed version
of each ontology in memory. From there, it provides a JSON-based REST API
for interacting with the ontologies loaded. In particular, the AberOWL server
oﬀers the possibility to query one or all ontologies by transforming a Manchester
OWL Syntax [10] query string into an OWL class expression using the OWL API
and retrieving its sub-, super- or equivalent classes. Additionally, the AberOWL
server uses Apache Lucene [29] to create an index of all class and relation labels,
synonyms, descriptions, and all other annotation properties, thereby allowing
fast retrieval of classes and relations through substring-based search.
The AberOWL synchronization module integrates a service that monitors
other ontology repositories for new ontologies as well as new versions of existing
ontologies, and incorporates them into the AberOWL server. Currently, only
the BioPortal repository [21] is monitored, which contains, amonst others, all
the OBO Foundry ontologies [27].
The AberOWL web repository provides a web-based front end which consti-
tutes the main user-interface for the repository. Its function is to allow users to
interact with the AberOWL server, providing the possibility to query, browse,
download, and visualize explore ontologies. It also makes a set of services built
on top of AberOWL available to users, such as SPARQL query expansion or
ontology-based PubMed searches.
3 Visualizing ontologies in AberOWL
We developed a visualization environment for ontologies in AberOWL that can
visualize inferences drawn from ontologies, visualize both the subclass hierarchy
as well as other types of relations between classes, and which can show the diﬀer-
ences between the inferences drawn from diﬀerent versions of an ontology. The
aim is to provide an intuitive and easy-to-use method to explore the structure
and inferences of ontologies in AberOWL, and visualization of the ontologies is
done in real time using the AberOWL reasoning infrastructure.
In AberOWL, ontologies are visualized as directed graphs in which nodes
represent classes and edges represent axioms that are inferred to hold between
two classes. The subclass hierarchy of an ontology is always shown, and gener-
ated by dynamically using the AberOWL reasoning services to query for direct
4
subclasses. A subclass edge is created between two nodes representing classes C
and Din ontology Oif and only if C SubClassOf: D can be inferred from the
ontology Oand there exists no other class Esuch that both C SubClassOf:
Eand E SubClassOf: D. The root of the subclass hierarchy is owl:Thing, and
ontologies are initially visualized by querying for direct subclasses of owl:Thing
using AberOWL. Whenever a user expands a node (by clicking on it) that rep-
resents class C, AberOWL is queried for direct subclass of Cand the results
of the query are generated dynamically as new nodes and linked to the node
representing Cthrough directed subclass edges.
To visualize axioms that represent more complex patterns, we follow the
relational patterns proposed in the OBO Relation Ontology [26] and its corre-
sponding approximation in OWL [8]. In particular, we identify the set of object
properties that occur in an ontology O, and for each object property Rin O,
we generate a pattern of the type X SubClassOf: R some Y, where Xand Y
are variables standing for classes [8]. Given a node representing the class Cin
the ontology O, we dynamically generate an R-successor Dof this node (with
an R-labeled directed edge) if and only if Dis a direct subclass of R some C
in O. For example, to show part-of successors of the class Apoptosis in the
Gene Ontology, we generate the class description part-of some Apoptosis,
use AberOWL to query for the direct subclasses of part-of some Apoptosis,
and dynamically generate a new node for each of the resulting classes together
with a part-of-labeled edge from Apoptosis to this new node.
In AberOWL, we can also simultaneously visualize multiple ontologies within
the same visualization environment. This is particularly useful to explore diﬀer-
ences between multiple versions of the same ontology. AberOWL maintains older
versions of ontologies in its repository; however, these versions are not, by de-
fault, accessible through automated reasoning. Therefore, when a user request is
made to visualize an older version of an ontology, the AberOWL server will ﬁrst
classify this version so that queries can be answered using an automated rea-
soner. To allow faster subsequent queries to ontology versions, a classiﬁed model
of these ontologies is kept in memory until it has not been queried for at least
90 minutes, at which time it is removed. We then use our visualization environ-
ment to show the subclass hierarchy as well as complex axiom patterns for two
or more ontology versions simultaneously. If classes are shared between ontology
versions, they are represented by the same node; if axiom patterns between two
classes hold in two versions of an ontology, they are represented by the same
edge. On the other hand, if axiom patterns or subclasses (of class descriptions)
diﬀer between versions, multiple diﬀerent nodes and edges are created and visu-
ally distinguished through colors. This allows to visually explore diﬀerences in
the inferences that can be drawn from diﬀerent ontology versions.
To visually diﬀerentiate the origin of the each node (i.e., the ontology version
in which it is present), we color-code ontology versions; we further color-code
object properties. To further improve usability of the interface should multiple
versions and object properties be selected, we add tooltips to nodes and edges
5
that show the ontology version in which they appear and the kind of axiom
pattern that is represented by the edge.
4 Implementation
Our visualization environment is implemented in JavaScript and utilizes the
AberOWL reasoning services. Ontologies are visualized through several recursive
functions, allowing accurate control over the growth of the tree. As inputs, the
implementation of the algorithm requires:
The root node of the ontology; by default, owl:Thing is used for all ontolo-
gies.
The ID (or URI) of the ontology to visualize.
A list of versions of the selected ontology that are visualized in parallel.
A list of the object properties in the ontology, from which we generate axiom
patterns and visualize them as additional edges.
Furthermore, the ontology visualization environment can be conﬁgured with
The number of children that are shown for each ontology level; in case a
node has many successors, only a subset of the successors is shown while the
other nodes can be shown on request. This allows us to limit the number of
classes that are shown in order to improve usability.
The number of levels that are expanded through a single request; this allows
us to show more than just direct successors of a node in a single request.
The number of hierarchical levels that will be pre-loaded from the AberOWL
server during the ontology visualization. The goal of this parameter is to
We use the JavaScript Promise pattern and AJAX together with the AberOWL
REST API to generate new nodes and edges based on user requests, and to pre-
load nodes that users may want to expand further. We use the D3˙
js graph li-
brary to generate the resulting graphs. In particular, we use node-link diagrams,
implemented in D3˙
js, to represent the ontologies in AberOWL. Node-link di-
agrams can be used to visualize both acyclic and cyclic graphs and therefore
allows us the ﬂexibility to visualize multiple types of relational patterns be-
tween classes. Whenever a user changes the choice of which relational patterns
or which ontology versions to display, the visualization environment interacts
with the AberOWL server in order to regenerate the graph based on the user’s
selections.
5 Discussion
5.1 Comparison to related work
The signiﬁcant increase in number of ontologies available online has stimulated
the need among the research community to develop visualization tools which
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Fig. 1. The image shows an use case example of the Semanticscience Integrated On-
tology (SIO) where one version and the object property has unit was selected. In
the image we can diﬀerentiate two kind of edges: gray edges represent subclass rela-
tions between two classes, while the yellow edge represents a has unit association (i.e.,
’dimensional quality’ is a direct subclass of ’has unit’ some owl:Thing).
support their navigation. However, the visualization of ontologies is not an easy
task, since ontologies are expressed as formal theories (i.e., sets of axioms) from
which inferences can be drawn, and visualizing the kind of inferences is challeng-
ing.
One way for classifying visualization methods is the number of dimensions
used to represent the ontology. Visualization approaches such as OntoSphere [1]
and Onto3DViz [6] use three dimensions to visualize ontologies, while methods
employed in most ontology editors like Prot´eg´e [22] or OBO-Edit [24], and spe-
cialized visualization methods such as KC-Viz [19] , OWLViz [11] and GrOWL
[17], utilize a two-dimensional representation.
The structure of ontologies can be visualized in two-dimensional space using
several diﬀerent methods [13]. However, the main aim of visualization of on-
tologies is often to eﬀectively present hierarchical structures to users [30], and,
consequently, the most widely used visualization forms are hierarchical graphs
or treemaps [11, 3, 28].
The graph representation in ontologies can be a taxonomy (induced by sub-
class relations between classes in the ontology), or a representation of other types
of relations (i.e., axiom patterns that hold) between classes [26, 8]. Strategies for
visualizing ontologies also diﬀer in the types of relations between classes that
can be visualized. In ontology editors, for most parts the subclass relations in
an ontology are shown while other types of axioms that hold between classes are
rarely visualized. A prominent exception has been OBO-Edit [24], an ontology
editor intended for use by biological domain experts and based on the OBO Flat-
7
ﬁle Format, which could show diﬀerent types of relations between classes beyond
subclass relations. However, development on OBO-Edit has recently been aban-
doned in favor of ontology development environments that are based more on
OWL, which rarely show relations other than subclass relations. Our approach
can be used to generate graphs that represent any kind of axiom pattern in
which two classes occur as variables.
A further distinction between visualization methods is whether they are able
to visualize the asserted structure of an ontology or if they can also visualize
the ontologies’ inferred structure. The Prot´eg´e ontology editor, for example, is
able to visualize both asserted subclass relations and inferred subclass relations.
In AberOWL, only the inferred (subclass or other types of) relations between
classes are visualized.
Table 1. Comparison of tools for visualizing ontologies
Tools Dimensions
Visualization
form
Diﬀ
techniques
Visualization
technique
Visualization of
axioms patterns
Aber-OWL [9] 2-Dimension Node-link Xsemantic X
Ecco [4] 2-Dimension XSLT X
semantic
and syntactic ×
GrOWL [17] 2-Dimension Non-planar graph ×
semantic
and syntactic X
KC-Viz [19] 2-Dimension Tree ×only syntactic ×
PROMPT-Viz [23] 2-Dimension
Horizontal tree layout
Treemap layout Xonly syntactic ×
Prot´eg´e [22] 2-Dimension Tree ×
semantic
and syntactic ×
OBO-Edit [24] 2-Dimension
Labeled hierarchical
graph ×
semantic
and syntactic X
Onto3DViz [6] 3-Dimension 3d-Tree ×semantic ×
OntoSphere3D [1] 3-Dimension Sphere ×only syntactic ×
OntoView [15] 2-Dimension RDF data model X
semantic
and syntactic ×
semantic
and syntactic ×
Ontologies are not static and will evolve due to extensions in their applica-
tion domain or changes in the shared conceptualization, changes in the scientiﬁc
knowledge of the domain, or correction of mistakes [16]. As a result of this evolu-
tion, diﬀerent versions of the same ontology arise, and it is often useful to visual-
ize the diﬀerences between diﬀerent versions to understand the changes that may
be necessary in applying the ontology within a use case. Research on ontology
versioning and ontology evolution has focused on providing collaborative tools
for editing ontologies. For instance, PromptDiﬀ [20] is an ontology-versioning
environment which, among others functions, is able to track structural changes
of diﬀerent versions of the ontology; OntoView [15] provides a methodology for
ontology versioning which allows users specify relations between versions of on-
tologies; PROMPT-Viz [23] which is a Prot´eg´e plugin which provides advanced
8
visualization of location, impact, type and extent of changes that have occurred
between versions on an ontology; COntoDiﬀ [7] tracks changes across multiple
versions of ontologies; and the diﬀ tool Ecco [4] that incorporates structural and
semantic techniques that allows to distinguish eﬀectual and ineﬀectual changes
between ontologies. In AberOWL, we visualize the changes of diﬀerent versions
of ontologies across multiple versions, and using the AberOWL system for au-
tomated reasoning, we can visualize not only the direct syntactic changes to
an ontology but also their impact of the inferences that can be drawn from
them. Table 1 provides an overview over the main features of diﬀerent ontology
visualization approaches.
5.2 Conclusions
We developed a novel visualization environment for biological and biomedical
ontologies, and integrated that environment in the AberOWL ontology reposi-
tory. Using this visualization environment, it is possible to visualize the inferred
structure of one ontology, including the structure induced by subclass relations
as well as arbitrary axiom patterns that hold between classes [8]. Furthermore,
we can visualize multiple versions of a single ontology at once, thereby allow-
ing users to explore structural changes between ontology version. All structural
relations between classes in the visualization environment are generated using
an OWL reasoner, thereby allowing users to explore the inferences that can be
drawn from the ontologies, or their diﬀerent versions. Our work also demon-
strates that the AberOWL system can be used as a service that enables the
development of novel kinds of semantic applications using automated reasoning
and semantic querying.
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Article
Full-text available
Background Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies’ semantic content remains a challenge. ResultsWe developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies’ semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph. Conclusions Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.
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