Interoperability between Biomedical Ontologies through
Relation Expansion, Upper-Level Ontologies and
Robert Hoehndorf1*, Michel Dumontier2, Anika Oellrich3, Dietrich Rebholz-Schuhmann3, Paul N.
Schofield4,5, Georgios V. Gkoutos1
1Department of Genetics, University of Cambridge, Cambridge, United Kingdom, 2Department of Biology, Institute of Biochemistry and School of Computer Science,
Carleton University, Ottawa, Ontario, Canada, 3European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, 4Department
of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom, 5The Jackson Laboratory, Bar Harbor, Maine, United States of
Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous
and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that
automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not
sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large
scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical
ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies
with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the
semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms
an important cornerstone for data integration, automatic inference and knowledge discovery based on formal
representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/
Citation: Hoehndorf R, Dumontier M, Oellrich A, Rebholz-Schuhmann D, Schofield PN, et al. (2011) Interoperability between Biomedical Ontologies through
Relation Expansion, Upper-Level Ontologies and Automatic Reasoning. PLoS ONE 6(7): e22006. doi:10.1371/journal.pone.0022006
Editor: Ioannis P. Androulakis, Rutgers University, United States of America
Received February 21, 2011; Accepted June 12, 2011; Published July 18, 2011
Copyright: ? 2011 Hoehndorf et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding for RH was provided by the European Commission’s 7th Framework Programme, RICORDO project, grant number 248502. Funding for MD
was provided by an NSERC Discovery Grant. Funding for AO and DRS was provided by the European Bioinformatics Institute. Funding for PS was provided by a
National Institutes of Health grant, number R01 HG004838-02. Funding for GG was provided by BBSRC grant BBG0043581. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Understanding the meaning of data is essential for accurate
scientific analysis and interpretation. Ontologies formalize the
meaning of terms in a vocabulary and provide a mechanism to
integrate knowledge from different sources through semantic
annotation of data. Interoperability of ontological resources is
required to automatically analyze data across different data
repositories and to enable automatic reasoning for knowledge
discovery. One milestone has been the development and
establishment of ontologies in the biomedical research community
with the goal of integrating knowledge from different scientific
resources and domains. In recent years, more emphasis has been
put on the standardization, formalization and interoperability of
the data resources and ontologies that characterize them .
However, the proliferation of species- and domain-specific
ontologies has resulted in an urgent need to develop an approach
to bridging the increasing gaps between these ontologies. It has
now become necessary to automatically resolve inconsistencies
across these resources to facilitate automated reasoning, formula-
tion of complex queries across a variety of data resources, testing of
hypotheses against the current body of knowledge and transla-
tional research .
Automated reasoning is the process of inferring automatically
information from an ontology that is not directly asserted but
implied by the axioms and definitions in the ontology. Substantial
progress has been made in enabling reasoning over part-whole
relations in biomedical ontologies [3,4] and using automated
reasoning over domain-specific upper-level ontologies to integrate
ontologies of different domains [5,6]. Automated reasoning has
further been applied to classify proteins , to verify and complete
the asserted axioms in biomedical ontologies [8,9] and to identify
relations between phenotype and disease . Despite significant
progress towards enabling automated reasoning over biomedical
ontologies, large-scale automated reasoning is often limited by the
size and complexity of the ontologies .
Questions of the type ‘‘Which genes are involved in abnormal-
ities of the vertebrate vascular system localized within abdominal
organs?’’ require in-depth knowledge of gene structure, taxonomy,
anatomy, development and disease. To answer this question
automatically, knowledge must be encoded in such a way that it
becomes accessible to machines and allows the integration of the
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increasing amounts of data encoded using a variety of data formats
and stored across numerous unconnected databases.
Biomedical ontologies, including the Gene Ontology (GO) ,
the Mammalian Phenotype Ontology (MP)  and the Human
Phenotype Ontology (HPO) , offer a set of terms and
descriptions in their domains, and considerable effort and resources
have been devoted to their construction . In order to realize
their potential, ontologies must provide rich, explicit and consistent
descriptions, so that automated systems are able to process and
distinguish the meaning of their terms and use them to infer new
information. Such descriptions are currently being created for
ontologies within the biomedical domain. In particular, formal class
definitions describe a class in terms of logical combinations of other
classes and relations. In contrast to informal descriptions of classes,
formal class definitions can be utilized for automated reasoning.
Formal definitions of classes in GO, MP and HPO were recently
introduced using a combination of manual and automated methods
[8,9,16]. Because these formal definitions are based on classes and
relations from several ontologies, they are called ‘‘cross-products’’,
and the cross-product definitions for GO, MP and HPO are called
GO-XP, MP-XP and HPO-XP, respectively.
However, these definitions do not always make their semantics
sufficiently explicit and accessible to automated reasoning, which
limits their ability to inter-operate with ontologies of other
domains and to facilitate knowledge discovery. In particular,
many biomedical ontologies are represented in the OBO Flatfile
Format for which the specification of an explicit semantics is
currently work in progress [17–19]. Here, we demonstrate how to
utilize biomedical ontologies in a formal representation based on
the Web Ontology Language (OWL)  and use this formal
representation for automated reasoning, consistency verification
and knowledge discovery. To achieve this goal, we extend a
method for formalizing biomedical ontologies using OWL ,
develop and apply an upper-level ontology  and derive an
ontology of relations from those used in biomedical ontologies. We
apply this method to the GO-XP, MP-XP and HPO-XP cross-
product definitions to identify unsatisfiable classes. An unsatisfiable
class is a class that could not possibly have any instances due to a
contradiction in the axioms and definitions that restrict the class.
The presence of an unsatisfiable class in an ontology is an
indication of a mistake either in the structure of the ontology or the
formal definition of the class. The consistent formulation of class
definitions is necessary to utilize biomedical ontologies for
answering powerful, cross-ontology queries and discovering new
knowledge. Here, we demonstrate how to remove contradictory
definitions and utilize the ontologies for expressive queries based
on reasoning over ontologies.
Materials and Methods
An ontology is a conceptualization of a domain of knowledge
 and is used to make the meaning of terms in a vocabulary
explicit and amenable to automated processing . Ontologies
contain classes which are arranged in a taxonomy and restricted
through axioms. Examples of classes are Bone, Apoptosis, Process or
Hypoplasia. Classes can have instances . For example, a
particular bone is an instance of Bone and a particular apoptosis
process occurring in one cell at a particular time is an instance of
Apoptosis. When classes in an ontology stand in an is-a relation,
every instance of one class is also an instance of the other class
. The class Apoptosis and the class Process can stand in such a
relation: every instance of Apoptosis is an instance of Process.
Furthermore, classes can be restricted through axioms . For
example, Apoptosis can be restricted by an axiom that requires
every instance of Apoptosis to have an instance of Cell as a
Upper level ontology
Ontologies from different domains may be integrated by
alignment to an upper level ontology. An upper-level ontology
provides a common foundation for classes and relations .
Typical classes found in upper-level ontologies include Process,
Material object, Quality and Function. Upper-level ontologies further
provide relations that can hold between instances of their classes.
Commonly included relations are has-part, has-participant
and quality-of. Several upper-level ontologies are well estab-
lished including the Basic Formal Ontology (BFO) , the
Descriptive Ontology for Cognitive and Linguistic Engineering
(DOLCE)  and the General Formal Ontology (GFO) .
For the purpose of this study, and to maximize compatibility
with different upper-level ontologies, we use a fragment of these
ontologies that consists of only four classes: Material object, Process,
Quality and Function. We declare these four classes as mutually
disjoint. The instances of Material object exist with all their parts at a
time point and need no other entity to exist. Processes, on the
other hand, are temporally extended and cannot exist at a single
time point. Functions are capabilities or potentials for the
occurrence of processes  and depend on material objects.
We treat qualities as attributes of other entities. In BFO, qualities
can only be attributes of independent continuants (Material object in
our upper-level ontology), while both GFO and DOLCE allow
qualities of material objects, processes and functions. In MP-XP
and HPO-XP, qualities are frequently applied to functions and
processes, and therefore we take the more liberal approach and do
not restrict the kind of entities which qualities characterize.
Figure 1 shows the taxonomy of this basic ontology and Table 1
shows the relations we include in our ontology.
The first step in our method creates a foundation of the domain
classes in this upper-level ontology. In the class definitions of the
ontologies we consider, PATO , the Foundational Model of
Anatomy (FMA) , the Adult Mouse Anatomy Ontology (MA)
, the Cell type Ontology (CL) , the Protein Ontology
(PRO) , the Mouse Pathology Ontology , the ChEBI
ontology of chemical structures , the UBERON cross-species
anatomy ontology  and the Gene Ontology (GO)  are used.
Figure 1. Taxonomy of the upper-level ontology. The four classes Material object, Process, Quality and Function are mutually disjoint.
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We assume that all classes in PATO are subclasses of Quality, while
FMA, MA, CL, PRO, ChEBI and the Cellular Component
branch of GO contain subclasses of Material object. We assume that
the biological process branch of GO contains subclasses of Process.
The Molecular function branch of GO may contain subclasses of
either Function or Process, a problem of which the GO curators are
aware . Consequently, we performed our analysis twice using
Relations in biomedical ontologies
Based on the upper-level ontology, we introduce a set of
relations that hold between the instances of the classes in our
ontology. We base our selection of relations on those that are used
in biomedical ontologies, such as those listed in Table 2. Each
relation in our ontology includes basic axioms pertaining to
reflexivity, transitivity and symmetry. In addition, each relation
determines the kinds of entities between which it is asserted
(domain and range restrictions). This ensures that employing a
relation in an axiom has consequences that can be inferred using
an automated reasoner. In particular, it allows for the automated
detection of inconsistencies and contradictory definitions such as
those arising from modelling errors. Table 1 shows the relations we
include together with their domain and range restrictions. A
similar assignment of domain and range restrictions for common
relations in biomedical ontologies can be found in bridging
ontologies available from the OBO Foundry . These bridging
ontologies use classes from BFO  to restrict the domain and
range of relations. The axioms we include for the relations are
compatible with the axioms for relations in RO and BFO . We
are more liberal in our axioms for the inheres-in relation in that
its range is Thing, because both MP-XP and HPO-XP use the
inheres-in relation for material objects, processes and functions.
Relations in the Open Biomedical Ontologies  are commonly
asserted between classes . For example, Nucleus part-of Cell is a
statement involving the classes Nucleus and Cell and the part-of
relation between classes. These relations between classes are then
defined using another relation between instances according to a
template provided by the OBO Relationship Ontology (RO) .
To illustrate the distinction between relations that hold between
classes and relations that hold between individuals, we use italic font
for relations between classes and bold font for instance-level
relations. We will further call relations between classes CC-relations
(for class-class) within this section to distinguish them from OWL
relations between instances.
To use template definitions for CC-relations in class definitions,
we must extend the method of defining CC-relations provided by
RO to accommodate the possibility of their application in class
intersections and unions . For this purpose, we treat CC-
relations in biomedical ontologies as templates that characterize a
class based on a single argument. For example, we treat the relation
part-of as a template which requires a single class as argument and
represents the description of a class. ‘‘part-of Cell’’ then becomes a
description of the class ‘‘part-of some Cell’’, i.e., the class of things
that are part of a cell. The statement ‘‘Nucleus part-of Cell’’ will then
be an assertion that the class Nucleus is a subclass of ‘‘part-of some
Cell’’. To formalize and implement this approach to defining CC-
relations, we modify the OWLDEF method and software .
OWLDEF provides a means to convert ontologies from the
OBO Flatfile Format  into OWL  while expanding CC-
relations according to the definitions provided by the RO .
While OWLDEF follows the RO approach in that CC-relations
expand to class axioms in OWL (either a subclass, equivalent class
or disjointness axiom), we modified this approach to expand CC-
relations to class descriptions. Instead of templates with two variables,
as in the original OWLDEF approach, we use templates with a
single variable. The advantage of this approach is that class
descriptions can be used in conjunction with intersections or
unions, while class axioms cannot . Additionally, we can
reproduce RO’s relation definitions by assuming that the first
argument of any relation will always be declared as a subclass of
the class description that results from use of the class construction
template. In general, the assertion of ‘‘C R D’’ is expanded to C
SubClassOf: E where E is the class resulting from expansion of R
D according to our method.
This method of defining CC-relations allows for their reuse and
therefore enables the integration and interoperability of ontologies that
method of defining CC-relations allows the application of this strategy
to term definitions while maintaining compatibility with RO .
Before the consistency of a biomedical ontology can be verified
with respect to the upper-level ontology, we must relate the relations
(between individuals)used in a biomedical ontology to the relations in
the upper-level ontology. This step is performed manually, based on
an analysis of the meaning of relations in the biomedical ontology.
For example, we assert that the relations labelled has_part, has-
part and has part in ontologies we examined are equivalent.
As a next, optional step, axioms for domain classes can be added
using the relations and classes available in the upper-level ontology.
For example, PATO distinguishes between qualities of processes
andqualitiesof physical entities .Afterconvertingtheexamined
ontologies to OWL and combining them with the upper-level
ontology, we can add axioms to their classes explicitly to ensure that
qualities of processes must inhere in processes, and qualities of
physical objects must inhere in material objects.
reasoner such as Hermit , Fact++  or Pellet . Based on
the resulting classification, we can perform queries, e.g., query for
unsatisfiable classes or classes satisfying complex conditions.
Using templates to repair ontologies
One common cause of contradictory class definitions is the
ambiguous use of relations, i.e., with different meanings. Using
relation definitions and OWL reasoning, we can disambiguate
these different meanings. For example, the relation has-central-
Table 1. Relations in our upper-level ontology.
RelationDomain RangeInverse relation
function-of FunctionMaterial objecthas-function
derives-from Material objectMaterial object
has-participantProcess Material objectparticipates-in
has-input ProcessMaterial object input-of
has-outputProcessMaterial object output-of
ProcessMaterial object central-
part-of Thing Thinghas-part
proper-part-of ThingThing has-proper-part
Relations in our upper-level ontology, implemented as OWL object properties,
along with their domain and range restrictions, super-relations and their inverse
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participant is a relation that holds between processes and
material objects. However, it is also sometimes used as a relation
between a quality and a material object, with the intended
meaning that the quality inheres in a process that has a material
object as participant.
To address this problem, we identify the different meanings in
which a relation in an ontology is used, and provide a relation
definition for each meaning. For example, the has-central-participant
relation can have the meanings has-central-participant some
?Y and inheres-in some (has-central-participant some ?Y).
Once all the possible ways in which a relation is used are
formalized, we connect the resulting definitions disjunctively. In
our example, the resulting statement would be:
has-central-participant some ?Y or
inheres-in some (has-central-participant some ?Y)
This statement is then used as the definition of has-central-
participant in MP-XP. If has-central-participant is used as a relation
between a process and a material object, the first part of the
definition will become true (and the second false). If it is used as a
relation between a quality and a material object, the second part of
the definition becomes true (and the first false).
This method allows us to remove contradictions when a relation
is used in a limited number of formally disjoint meanings. For this
purpose, the application of these disambiguation templates require
manual analysis and knowledge of the ontologies to which they are
applied. We performed a manual evaluation of the use of
disambiguation templates within HPO-XP and MP-XP, and found
that all relations were correctly disambiguated through the use of
We may still be interested in identifying the particular class
descriptions where a relation is used outside its intended meaning.
With an appropriate query, OWL reasoning can provide an
answer to this question. We defined ambiguous relations using a
disjunctive statement. Because one part of the disjunction will
always be unsatisfiable, the automated reasoner will eliminate this
possibility and automatically infer that the only remaining option
must apply. We can then query for the two distinct meanings of
relations and obtain a list of results, which can then be added to
the ontology’s class definitions.
In MP-XP, we identified two relations with ambiguous use that
lead to unsatisfiable class definitions. The first is has-central-
participant, the second inheres-in. In the resulting OWL ontology, we
use an OWL reasoner to perform a query for subclasses of:
inheres-in some (has-central-participant some Thing)
and obtain a list of 280 classes for which the second meaning in our
disambiguation step is the only satisfiable option. We can now define a
new class-level relation based on the template inheres-in some (has-
central-participant some ?Y) and replace the wrongly asserted inheres-in
or has-central-participant relations with this new relation.
Reasoners and software
To perform our experiments, we used the Protege Ontology
Editor  and the HermiT OWL reasoner (version 1.3.1)  on
a dual core 3.20 GHz Intel Xeon CPU with 3 GB memory.
We developed a set of scripts and prototypical software libraries
to prepare and analyze our data. The software and data we used,
including the specific versions of the ontologies and their
definitions, are available from our project website. The software
N a library to convert OBO files to OWL using the modified
OWLDEF templates we developed,
N scripts to automatically assign super-classes from our upper-
level ontology to classes in the used ontologies,
N scripts to count relations used both in formal definitions and
relationship statements in OBO ontologies,
N a script to count the number of defined terms in an OBO
The software we developed is written in Java and Groovy and
depends on the OWLAPI .
Our analysis has been performed with the MP, HPO and GO as
well as the formal definitions created for them. All ontologies and
their definitions were obtained from the OBO Foundry website
(http://obofoundry.org). The ontology files for the MP, HPO and
GO were downloaded on November 25, 2010 and we have made a
copy of the ontology files available on our project website.
The formal definitions of GO-XP, MP-XP and HPO-XP are
work in progress [8,9], as is most work on biomedical ontologies,
Table 2. 20 most frequent relations in OBO.
Number of times
used in ontologies
Number of times used
in formal definitions
The first column states the name of the relation, the second how often the
relation is used in any OBO ontology, while the final column indicates
occurrence of the relation in formal definitions. We performed the analysis on
the full OBO library of ontologies (as of Nov 25, 2010), excluding the NCI
Thesaurus, the BFO, the RO, the mappings of OBO ontologies to other
ontologies and databases and the logical definitions of OBO ontologies. The full
list is available on our project website.
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since the definitions are subject to change and future revision .
A method to detect contradictory definitions can improve the
quality of these definitions and improve the speed at which they
are created. Our analysis was performed with a version of the GO
definitions that contains 14,792 defined terms, while the HPO
definitions contain 3,746 and the MP definitions 5,428 defined
terms. Figure 2 provides an overview of our method and main
Formalizing the semantics of ontologies and their
In GO-XP, the most frequently used relations are part-of, has-
output, has-input and regulates. Slightly less than half of the relations
are composite relations of the type results-in, i.e., those that are
formed from the primitive results-in relation and classes. For
instance, results-in-binding-of is composed of results-in and the
class Binding. For some relations, we cannot yet identify
appropriate process terms. For example, we could not identify
an increase in mass process required to formalize results-in-increase-in-
mass-of. Similarly, formalizing transport specific relations like
results-in-transport-from or results-in-transport-along require a more fine-
grained framework of transport processes. For the purpose of this
study, we demonstrate the formalization using several examples
(shown in Table 3), but do not expand any relation in GO-XP. We
expand the relations has-function-realized-by and inheres-in-part-of in
MP-XP and HPO-XP according to the templates in Table 3.
Following this formalization of relations and classes, and using
reasoning in the Web Ontology Language (OWL) , we
identified 7,397 unsatisfiable classes in GO-XP under the
assumption that the Molecular function branch of GO contains
subclasses of Function. Assuming instead that the Molecular function
branch of GO contains subclasses of Process allows us to identify
1,139 unsatisfiable classes. We identified 3,487 unsatisfiable class
definitions in MP-XP and 1,017 in HPO-XP. Each unsatisfiable
class definition indicates either an incorrect definition or a
problem in the biomedical ontology for which the definitions
To identify specific definitions that cause class unsatisfiability,
we also performed our analysis after removing all explicitly
asserted is-a relations from the ontologies. Under these condi-
tions, we could identify 3,768 unsatisfiable classes in GO-XP when
treating the Molecular function branch as subclasses of Function and
30 when treating the Molecular function branch as subclasses of
Process. In MP-XP, we could identify 450 unsatisfiable class
definitions and 245 in HPO-XP.
Classification of contradictory class definitions
Among the identified contradictory class definitions, we can
distinguish between local and global errors. Local contradictions
arise from erroneous axioms within a single ontology. Global
contradictions are the result of combining axioms from multiple
ontologies. The unsatisfiable classes are identified using automated
reasoning, and since we reason over these ontologies in an
expressive formal language (OWL), we can identify many more
formal problems whose resolution would be helpful to the
developers of GO-XP , MP-XP and HPO-XP .
(GO:0042244) illustrates a local contradiction that results from
the contradictory definition of Spore. Spore is defined as the
intersection of Fungal cell and Prokaryotic cell. Fungal cell, however, is a
subclass of Eukaryotic cell which is disjoint from Prokaryotic cell
leading to the unsatisfiability of Spore (see also Figure 3). From the
unsatisfiability of Spore result the unsatisfiabilities of classes that use
Spore in its definitions: Sporulation resulting in formation of a cellular spore
is defined using Spore, Spore wall biogenesis is a part of Sporulation
resulting in formation of a cellular spore and Spore wall assembly is a part of
Spore wall biogenesis. All these classes are unsatisfiable due to the
unsatisfiability of Spore. These unsatisfiable classes have been
identified by an automated reasoner, leading to the conclusion that
consistency verification of the ontologies and automated reasoning
Figure 2. Overview of method and results.
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during ontology development can prevent such problems, as long
as sufficiently expressive formal languages are used. We submitted
the inconsistency of Spore to the developers of the Cell type
ontology, and the underlying problem has been resolved in recent
Global contradictions results from contradictory definitions that
arise from axioms constructed from multiple ontologies. One
example of such an unsatisfiable class is Leukocyte activation
(GO:0045321), which causes all of its subclasses to be unsatisfiable
aswell. Leukocyte activationis defined asa Cell activationthat has-input
some Leukocyte. In addition, Leukocyte activation is a subclass of Cell
activation and Immune system process. Furthermore, Cell activation is a
subclass of Cellular process while Immune system process is defined as a
biological process which has-agent an Immune system. Therefore,
through automated reasoning we find that Immune system process is a
kind of System process: System process is defined as Biological process and
has-agent some Anatomical system and Immune system is a subclass of
Anatomical system. System process, in turn, is a subclass of Multicellular
organismal process which is disjoint from Cellular process. Therefore,
Leukocyte activation is unsatisfiable.
Detecting this contradictory class definition relies on reasoning
over the UBERON cross-species anatomy ontology  (to infer
that Immune system is a type of Anatomical system) and reasoning over
the formal definitions in GO (to infer that Immune system process is a
System process). This is illustrated in Figure 4.
Another example of a global contradiction is found in MP-XP.
The class Liver inflammation, a subclass of Abnormal liver physiology, is
unsatisfiable. Abnormal liver physiology is defined as a Functionality that
inheres in the Liver, while Liver inflammation is defined as an Increased
rate that inheres in the Inflammatory response in which a Liver
participates. Inflammatory response is a process from the GO, while
Liver is a material object in MA. The inheres-in relation is
functional, i.e., a quality can inhere in at most one thing. As a
consequence, a quality that inheres both in Inflammatory response and
Liver will be inferred to inhere in something that is both a liver and
an inflammatory response at the same time. Since processes and
material objects are disjoint, the resulting class is detected as
Liver inflammation is further defined as a subclass of Abnormal liver
physiology, which is defined using the quality Functionality from
PATO. According to PATO, Functionality must be a quality of a
material object while Increased rate (used in defining Liver
inflammation) must be a quality of a process. Therefore, another
cause for the unsatisfiability of Liver inflammation is the definition of
qualities in PATO. Removing only one cause for the unsatisfia-
bility of Liver inflammation would therefore not remove the problem
with Liver inflammation.
Contradictions through homonymy: If two classes with
different definitions share the same label, then the label of the two
classes is called a homonym. Any homonym can be the cause of
contradictions due to incorrect class assignments in the ontological
framework founded in the polysemy of the homonym. One
example of a contradictory class definition arising from homon-
ymy is Mucus secretion. Mucus secretion is defined as the intersection of
Secretion (UBERON:0000456) and results-in-release-of some
Mucus. The class named Secretion in the UBERON ontology is a
subclass of Material object while the GO class Secretion (GO:0046903)
is a subclass of Process. Since the relation results-in-release-of
(and any other results-in relation) must have a process as its first
argument and Process and Material object are disjoint, we detect this
contradiction automatically. Such contradictions can be the result
of applying lexical methods to create formal definitions. Figure 5
illustrates this example.
Contradictions from improper and ambiguous use of
relations: The following example shows an improper use of a
(GO:0033655) is defined as Host cell part and inheres-in some
Cytoplasm. The inheres-in relation is a relation between a Quality
Host cell cytoplasmpart
Table 3. Exemplary definition templates for relations used in biomedical ontologies.
Relation nameDefinition template
part-ofpart-of some ?Y
inheres-in-part-ofinheres-in some (part-of some ?Y)
has-function-realized-byhas-function some (realized-by only ?Y)
capable-of has-function some (realized-by only ?Y)
inheres-in-has-central-participantinheres-in some (has-central-participant some ?Y)
has-inputhas-input some ?Y
realized-by-has-inputrealized-by only (has-input some ?Y)
Each template unfolds into a class description in OWL and is represented using a modified form of the Manchester OWL Syntax.
Figure 3. Local contradiction in the Cell type Ontology. The
contradictory class definition arises from the assertion that Spore is both
a Prokaryotic cell and a Fungal cell. Fungal cell is a kind of Eukaryotic cell
which is disjoint from Prokaryotic cell.
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and an individual, resulting in Host cell cytoplasm part becoming a
subclass of Quality. Quality and Host cell part (a subclass of Material
object) are disjoint and therefore Host cell cytoplasm part is
A principal mistake in the MP-XP and HPO-XP is the use of
the has-central-participant relation between qualities (instead of
processes) and material objects, and a mistake in the GO-XP is
the ambiguous use of relations between processes and functions
(assuming that functions are disjoint from processes). For example,
the relations has-output and has-input are frequently applied both to
functions and processes and therefore a source of numerous
unsatisfiable class definitions. Disambiguation templates can aid in
the automatic detection and correction of ambiguous relations
through a relaxation of a relation definition. The following
disambiguation templates can be applied to has-output, has-input and
N has-output X: either the processes that have X as output or the
functions that are realized through processes that have X as
output. For example, in the statement has-output D-glucose, the
resulting class is either the class of processes that have D-
glucose as output, or the class of functions that are realized
through processes that have D-glucose as output.
N has-input X: either the processes that have X as input or the
functions that are realized through processes that have X as
input. For example, in the statement has-input Lactose, the
resulting class is either the class of processes that have lactose
as input, or the class of functions that are realized through
processes that have lactose as input.
N inheres-in X: either the qualities that inhere in X or the qualities
that inhere in processes in which X participates. For example,
in the statement inheres-in Liver, the resulting class is either the
class of qualities inhering in a liver, or the qualities that inhere
in processes in which a liver participates.
Such templates allow the disambiguation of relations in
biomedical ontologies. For example, in the GO-XP, relations
such as has-input and has-output, which are applied to either
processes or functions can explicitly be distinguished: either the
relation is used in its intended meaning and the second part of the
disjunction will become unsatisfiable, or it is applied to a function
class that is realized by processes that satisfy the asserted condition,
in which case the first part of the disjunctive relation definition is
made unsatisfiable. Since one part of the disjunctive relation
definition will always become unsatisfiable, we are able to
disambiguate the relation through automated reasoning and query
for the cases where the relation is used in its intended meaning
(i.e., applied to a process class) and where it is used in its
unintended meaning. Based on the results of such queries, we can
then introduce new relations, e.g., realized-by-has-input, realized-by-
has-output or inheres-in-has-central-participant (see Table 3).
We applied the templates for has-input and has-output to GO-XP,
and the templates for inheres-in to MP-XP and HPO-XP, in each
case after removing the asserted is-a relations. The application of
the templates for has-input and has-output to the GO-XP removed
2,649 contradictory class definitions. Querying for uses of has-input
in its intended meaning (applied to a process) yields 669 class
definitions. On the other hand, when querying for the unintended
meaning, expressed by the realized-by-has-input relation, results in
2,390 class definitions. The has-output relation is used in its
intended meaning 462 times and in its unintended meaning
(realized-by-has-output) 1,632 cases. In MP-XP and HPO-XP, we
replaced both the has-central-participant and the inheres-in relations
with the disambiguation templates. As a result, we removed 280
contradictions in MP-XP and 157 in HPO-XP.
We added the asserted is-a relations to MP-XP and HPO-XP
after applying the disambiguation templates. In the MP-XP, 3,416
Figure 4. Global contradiction in the GO-XP. The contradiction arises from the inference that Immune system process is a kind of System process.
System process is a kind of Multicellular organismal process which is disjoint from Cellular process.
Figure 5. Contradiction in the GO-XP arising from faulty class
definition due to homonymy. Mucus secretion is asserted to be a
subclass of Secretion, an anatomical entity in the UBERON ontology
which is a kind of Material object. Due to the domain and range
restrictions of the relation results-in-release-of, Mucus secretion is
inferred to become a kind of Process, which is disjoint from Material
object. Use of the class Secretion from GO would have prevented this
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unsatisfiable classes remain and HPO-XP contains 1,016 un-
satisfiable classes after applying the disambiguation templates to
the ontologies including their is-a relations. This demonstrates that
in most cases, multiple causes lead to classes becoming unsatisfi-
able in these ontologies.
Knowledge integration and retrieval
The application of our method of formalizing the classes and
relations in ontologies makes it not only possible to detect and
repair some contradictions, but it can also be used to perform
more expressive queries over the ontologies.
For example, we may be interested in finding phenotypes in
mice that affect parts of the vascular system in any abdominal
organ or its parts. We find, however, that no class in the MP can
be retrieved, since none satisfies our query exactly. Although MP-
XP defines a number of phenotypes that involve abnormalities in
abdominal organs or the vascular system (such as liver abnormal-
ities, kidney abnormalities or vascular abnormalities), no shared
superclass ties these together. Our inference over both MP-XP and
mouse anatomy allows us to infer that a number of phenotypes
affect the vasculature of abdominal organs. Our retrieval yields 9
phenotypes: Abnormal Kupffer cell morphology, Abnormal liver sinusoid
morphology, Abnormal liver vasculature morphology, Abnormal renal plasma
flow rate, Decreased renal plasma flow rate, Increased renal plasma flow rate,
Enlarged liver sinusoidal spaces, Liver vascular congestion and Spleen vascular
We obtain these results because our method expands the
relation inheres-in-part-of. Without this expansion, inferences across
both the phenotype and mouse anatomy ontologies would not be
possible. The expansion of inheres-in-part-of using the primitive
relations part-of and inheres-in enables inference over the
parthood relations in the anatomy ontology. For example, Liver
vascular congestion is defined both as an abnormality that inheres-in-
part-of Liver and as an abnormality of a Blood vessel. After the
expansion, Liver vascular congestion is defined as an abnormality of a
Blood vessel which is part-of some Liver. Because, in the mouse
anatomy ontology, a Liver is a kind of Abdomen organ and Blood vessel
is a part-of the Vascular system, the definition of Liver vascular
congestion satisfies our query.
In the analysis of the query results, we find that references to
Kidney abnormalities are missing, although kidneys are abdominal
organs as well. A manual inspection reveals that this is due to a
missing assertion in the mouse anatomy ontology, i.e., that a Kidney
blood vessel is a type of Blood vessel. The addition of this assertion
enables us to retrieve kidney vascular abnormalities and has been
requested from the curators of the mouse anatomy ontology.
The formalization of the meaning of terms in biomedical
ontologies enables queries that can make reference to domain
terminology in entirely new and unforeseen ways. These queries
do not exclusively rely on specialized knowledge of the ontologies’
structure and term names, but enable access to domain knowledge
based on a term’s meaning. Such a generalizable method is
dependent on an upper level ontology that offers basic types and
At the moment, biomedical ontologies often focus on including
terms that are needed in different domains, adding natural
language definitions to these terms, and connecting them using
relations which are defined primarily in natural language.
Consequently, understanding the meaning of these terms (and
hence which inferences may be drawn from them) or performing
queries that refer to them, requires extensive domain knowledge
and a clear understanding of the structure of the ontologies in
terms of their classes and relations.
While the need for domain expertise is not only desirable but
essential, in the design of ontologies, modelling errors may not be
avoided unless consistency verification through automated rea-
soning becomes a part of the ontology design process. The
problem is even greater for ontologies or class definitions that are
The application of our method shifts the focus of ontology
development towards a knowledge-based perspective. From this point
of view, the importance of natural-language definitions and
explanations is matched by that of formalized and explicit
semantics of terms and relations. Our method allows the explicit
definition of the meaning of terms in more detail than before and
therefore enriches their utility in automated processing and
reasoning. The resulting definitions may then even be used to
derive natural language definitions of relations and classes ,
ensuring consistency between both.
Through the application of our method, one goal of ontologies
comes closer to realization: to improve knowledge discovery by
providing a uniform method for relating and accessing data
through formal semantics. The application of our method
enhances the capacity of biomedical ontologies to achieve this
goal. It benefits heavily from recent attempts to provide formal
definitions of classes in biomedical ontologies by combining classes
from multiple ontologies and expressive relations.
To provide a foundation for the classes and relations in
biomedical ontologies, our method utilizes an upper-level
ontology. To demonstrate the benefits gained through the use of
such an ontology, we developed a minimal upper-level ontology
that is applicable to the detection of mistakes and the inference of
new cross-domain knowledge. This minimal ontology is a
fragment of well-established ontologies such as the Basic Formal
Ontology (BFO) , the Descriptive Ontology for Cognitive and
Linguistic Engineering (DOLCE) , the General Formal
Ontology (GFO)  or the Suggested Upper Merged Ontology
(SUMO) . Therefore, no, or only minimal, changes to our
method are necessary when any of these ontologies is used as the
upper-level ontology. In addition to providing compatibility with
domain ontologies that are being developed using any established
upper-level ontology, we can also derive a means to empirically
evaluate upper-level ontologies based on how many incorrect class
definitions can be automatically detected and subsequently
repaired through their use.
We provide a method for improving formal term and relation
definitions in biomedical ontologies. Based on this method and
through the use of automated reasoning, we have identified several
thousand contradictory class definitions and could automatically
repair some of them. These contradictions indicate either incorrect
formal definitions or structural errors in the ontologies. The
formalization method we propose improves the utility of
automated reasoning over ontologies, so that it becomes possible
to ask and answer more questions across multiple domains. We
show that our motivating example of a query for all the genes in
mice that are involved in abdominal vasculature abnormalities can
be answered by applying our method. It is now possible to extend
the range of queries by adding further connections through explicit
relations between classes in ontologies. In particular, we can
exploit links between a classification of species using the NCBI
Taxonomy , and combine them with an ontology of species-
independent anatomy (UBERON)  in order to retrieve a set of
classes of vascular abnormalities in abdominal organs across all
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PLoS ONE | www.plosone.org8 July 2011 | Volume 6 | Issue 7 | e22006
vertebrates. Furthermore, employing the Sequence Ontology (SO) Download full-text
 in the query will allow us to identify gene and protein
sequences and their parts. These can then be related via the GO
and the phenotype ontologies to the functions and processes that
are involved in vascular abdominal abnormalities. All these
ontologies, SO, UBERON, the phenotype ontologies and GO,
are actively being developed to overcome the remaining barriers
by adding new relations and connecting more domains. Provided
that these ontologies focus on making their semantics explicit and
their definitions and axioms consistent, as described by our
method, more powerful questions will soon be answerable through
reasoning across ontologies alone.
Conceived and designed the experiments: RH. Performed the experiments:
RH. Analyzed the data: RH GG PS DRS MD. Contributed reagents/
materials/analysis tools: AO RH. Wrote the paper: RH GG MD PS DRS
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