=Paper=
{{Paper
|id=None
|storemode=property
|title=Populous: A Tool for Populating Templates for OWL Ontologies
|pdfUrl=https://ceur-ws.org/Vol-698/paper7.pdf
|volume=Vol-698
|dblpUrl=https://dblp.org/rec/conf/swat4ls/JuppHIKOSSW10
}}
==Populous: A Tool for Populating Templates for OWL Ontologies==
https://ceur-ws.org/Vol-698/paper7.pdf
Populous: A tool for populating Templates for
OWL ontologies
Simon Jupp1 , Matthew Horridge1 , Luigi Iannone1 , Julie Klein2 , Stuart Owen1 ,
Joost Schanstra2 , Robert Stevens1 , and Katy Wolstencroft1
1
School of Computer Science, University of Manchester, Manchester, UK
2
Institut National de la Santé et de la Recherche Médicale, Toulouse, France
Abstract. We present Populous, a tool for gathering content with which
to populate an ontology. Domain experts need to add content, that is of-
ten repetitive in its form, but without having to tackle the underlying
ontological representation. Populous presents users with a table based
form in which columns are constrained to take values from particular
ontologies; the user can select a concept from an ontology via its mean-
ingful label to give a value for a given entity attribute. Populated tables
are mapped to patterns that can then be used to automatically generate
the ontology’s content. Populous’s contribution is in the knowledge gath-
ering stage of ontology development. It separates knowledge gathering
from the conceptualisation and also separates the user from the stan-
dard ontology authoring environments. As a result, Populous can allow
knowledge to be gathered in a straight-forward manner that can then be
used to do mass production of ontology content.
Keywords: Ontology, OWL, Spreadsheet, Template
1 Introduction
Ontologies are being developed to provide controlled vocabularies for the anno-
tation of life science data. Annotating data with ontologies adds semantics to
the data that can facilitate data integration and enrich data analysis[18], [5],
[3]. For ontologies to have a faithful representation of a domain, experts from
that domain must have input to the authoring process. There are barriers that
prevent domain experts engaging in an ontology’s development; in particular,
the semantics of an ontology language, or the intricacies of the authoring tools.
To address this issue we have developed Populous that allows users to contribute
their knowledge by populating simple templates that are then transformed to
statements in the underlying representation language.
When designing an ontology it is often the case that repeating patterns occur
in the modelling. These patterns can be abstracted from the ontology and used to
specify simple templates that could be populated by domain experts [7, 6, 2]. As
an example of a pattern, consider an ontology about cells; eukaryotic cells can be
classified as being either anucleate, mono-nucleate, binucleate or multinucleate.
We can abstract over this pattern to say that every cell can be classified by
its nucleation. This pattern is repeated for all cell types; the only variables are
the cell name and the value for its nucleation. We can now use this pattern to
build a simple template that could be populated by a cytologist, without him or
her ever knowing about the underlying ontological representation. This type of
pattern is common in ontology development where you have one set of entities
being described in terms of another set of entities [17].
The tabular layout provides a simple and intuitive form fill-in style of user
interface for a user to populate such templates. Typically, each row corresponds
to a set of related entities and each column represents the type of relationship.
The intersection of row and column holds the ‘filler’ for the given entity’s rela-
tionship of that column’s type. By adopting templates ontology developers can
separate the pattern from its population; this allows the domain expert to fo-
cus on the knowledge without the distraction of the knowledge representation
language.
Templates are useful when data needs to be collected in a regular form. Ap-
plying constraints to the template reduces the amount of discrepancies in the
input data. A common tool for collecting data in this form is the spreadsheet;
spreadsheets provide a tabular interface, where columns and rows represent cer-
tain attributes, and individual cells capture the data. Tables help users to struc-
ture data in a logical way, that is useful for both its maintenance and processing.
In ontology development spreadsheets can be used to gather and organise infor-
mation about concepts and their relationships. Previous work in this area has
focused on the transformation of data into ontologies, but little attention has
been paid to supporting the population of the templates at the point of data
entry and this is where Populous’s main contribution lies.
1.1 Previous work
Various tools are available to support the conversion of spreadsheet data into
statements in a knowledge representation language. Excel2RDF3 , Convert2RDF4
and RDF123 [1] are three tools that allow users to generate Resource Description
Framework (RDF) statements from spreadsheets. Despite RDF being the refer-
ence syntax for the Web Ontology Language (OWL), its serialisation is complex
and not intended for humans, making it inappropriate for defining higher level
OWL construct in patterns.
The ExcelImporter plugin5 for Protégé 4.0 was a step up from these tools
and enabled users to transform spreadsheets content directly into OWL axioms.
It was, however, limited to only a small set of OWL constructs. The more re-
cent tools to support template data and pattern instantiation include Mapping
Master [14], OPPL 2 [11, 6] and the Protégé Matrix plugin6 :
3
http://www.mindswap.org/∼rreck/excel2rdf.shtml
4
http://www.mindawap.org/∼mhgoeve/convert/
5
http://protegewiki.stanford.edu/wiki/Excel Import
6
http://protegewiki.stanford.edu/wiki/Matrix
– The MappingMaster plugin for the Protégé 3.4 ontology editor is a more
flexible tool for transforming arbitrary spreadsheet data into OWL. Map-
pingMaster moves away from the row centric view of spreadsheets and has
an expressive macro language called M2 [15, 14] that can handle non-uniform
and complex spreadsheets. M2 combines a macro language for referring to
cells in a spreadsheet with a human readable syntax for generating OWL ex-
pressions called the Manchester OWL Syntax [10]. MappingMaster and M2
are primarily designed for the transformation of spreadsheet data to OWL,
but provides little in the way of support for populating and validating the
spreadsheet data.
– The Ontology Pre-Processing Language (OPPL) [6, 11] (version 2) is a script-
ing language similar to M2 . OPPL 2 is also Manchester OWL Syntax based
and allows for the manipulation of OWL ontologies at the axiom level. OPPL
2 has support for the use of variables and the addition and removal of log-
ical axioms from an ontology. OPPL 2 is a powerful scripting language for
OWL and a user interface is provided via the OPPL plugin for Protégé 4.0.
OPPL, however, does not currently support working with tabular data and
is decoupled from any knowledge gathering.
– The MatrixPlugin for Protégé 4.0 allows users to specify simple OWL pat-
terns in a tabular interface that can be used to populate repeating patterns
with existing concepts from an ontology. This plugin is useful for ontology
developers that have repetitive patterns to instantiate, and has the added
benefit of cell validation and auto-completion at the point of data entry.
The Matrix plugin is limited by the type of patterns that can be expressed
along with the fact that it is tightly integrated with the Protégé interface,
therefore, not suitable for all users. It does, however, combine knowledge
gathering and axiom generation.
1.2 Requirements
All of the previous tools developed in this area tend to focus on the transforma-
tion from the template to the ontology. They provide little or no support for pop-
ulating and validating template content. Furthermore, tools like ExcelImporter,
OPPL and MappingMaster are integrated into the ontology development tool,
so they are aimed at users that are already familiar with ontology development.
A table based tool for ontology authoring should shield the user from the under-
lying ontology and help guide the user when populating the template. Providing
validation at the time of authorship should significantly reduce the amount of
time required to debug and process the data captured in the spreadsheet. Here
we list some key requirements for an ontology based table editor:
1. Concepts may be described in terms of other concepts from other ontologies.
In setting up Populous the users must be able to load and browse ontologies.
2. The contents in the column of a table need to be restricted to concepts from
imported ontologies, or parts of imported ontologies.
3. To improve human comprehension the concept should be rendered using only
the URI fragment, or optionally a human readable label from the ontology.
4. Each time a concept is added to a cell within the table Populous needs to
check that the concept is valid according to the constraints resulting from
requirement 2.
5. A cell might have multiple values; for example, when the concept being
described has multiple parts.
6. Users should be free to suggest new concepts when an appropriate concept
is not available.
2 Populous
Populous is an extension of RightField[21]; RightField is for creating Excel doc-
uments that contain ontology based restrictions on spreadsheets content. Right-
Field enables users to upload Excel spreadsheets, along with ontologies from
their local file systems, or from the BioPortal [13] (a repository of biological
ontologies available at http://bioportal.bioontology.org). RightField sup-
ports OWL, OBO and RDFS ontologies. Using RightField, individual cells, or
whole columns or rows can be marked with the required ranges of ontology
terms. For example, they could include all subclasses from a chosen class, di-
rect subclasses only, all individuals, or only direct individuals. Each spreadsheet
can be annotated with terms from multiple ontologies. RightField is primarily
designed for generating spreadsheet templates for data annotation; Populous
extends RightField to support knowledge gathering and ontology generation.
Populous and RightField are both open source cross platform Java applica-
tion. They use the Apache-POI 7 for interacting with Microsoft documents and
manipulating Excel spreadsheets. Populous is available for download from here
http://www.e-lico.eu/populous.
Requirement 1 is already addressed using RightField functionality to upload
both OWL and OBO ontologies. In order to better serve the life science com-
munity, users can also browse and load ontologies directly from BioPortal. Once
the ontologies are loaded they are classified by a reasoner and the basic class
hierarchy can be viewed.
Requirement 2 is met by the ability to select terms from the ontology to create
validation sets. A data validation restricts the set of values that are valid for a
particular cell in the table. Validations can span multiple rows and columns and
be composed of classes, properties or individuals from the ontology. These data
validations are stored in hidden worksheets along with additional information
such as the full URI for the term, a label and the source ontology URI. These
templates can also be exported as Microsoft Excel documents, which include the
data validations on cells.
We address requirement 3 by allowing users to populate cells using ontology
labels. Once data has been entered the default will be to render the ontology
term using its label; if no label is specified the URI fragment is used. RightField
7
http://poi.apache.org
already supports reading Microsoft Excel workbooks so users are free to pop-
ulate the templates in external tools before importing them into Populous for
validation and transformation.
By using Populous directly users will benefit from having instant validation
of the input data, satisfying requirement 4, along with some advanced features
such as regular expression based auto-completion as they type into annotated
cells. Additionally Populous supports the addition of multiple values into a single
cell that are validated individually according to requirement 5. This can be
particularly useful for certain kinds of patterns where a conjunction of variables
is required to construct the axiom (see Section 3 for example). Populous also
allows the addition of free text values, even if the cell has an associated validation
range, thus satisfying requirement 6. These values are highlighted to the user
in red and can act as placeholders for new or suggested terms when no suitable
candidate could be found in the validation set.
Populous supports the use of OPPL patterns in order to generate new OWL
axioms from the populated template. OPPL scripts can be written directly in
Populous’s design mode or imported from scripts generated in the OPPL plu-
gin. Variables from the OPPL pattern must be mapped to columns from the
table using the column name. A pattern Wizard guides the user through the
generation and execution of the OPPL scripts. When the template is processed
new identifiers for unknown terms can be auto-generated and exported from
Populous.
Fig. 1. Populous workflow
2.1 Building an Ontology with Populous
Fig. 2. Screenshot of Populous showing template population for cell types and nucle-
ation
We can demonstrate Populous in building a simple ontology about cell types
as described in Section 1. The pattern in the ontology states that every cell must
have a nucleation. We need to create a template with two columns, column A
is for cell type concepts, whilst column B is for nucleation concepts. Ontologies
describing cells and their nucleation already exist that we can import into Popu-
lous. By connecting to BioPortal we can load the Cell Type Ontology (CTO) [4]
and Phenotype and Trait Ontology (PATO) [8]. In order to restrict column A
to terms from the CTO, we highlight all the cells in column A and restrict them
to all subclasses of the root class. Column B is restricted subclasses of the nu-
cleation concept from PATO. The template is now ready to be populated by the
domain expert.
Figure 2 shows a part populated template. The terms in green indicate a
valid term has been entered into the cell. The term in Column A5, Proximal
tubule epithelial cell is red because it is not a valid term from the CTO. Cell A6
is in the process of being edited with the auto-completer offering valid suggestion
for input.
The populated spreadsheet can now be transformed into an ontology. This
can be done using the pattern wizard in Populous (Figure 3). The first step in
Fig. 3. Screenshot of Populous Pattern Wizard showing the OPPL script editor
the pattern wizard asks the user to select the columns and rows that contain
populated data. In this example the pattern creates a restriction on each cell
stating that all cells have a relationship, called hasNucleation, to an instance of
the class nucleation. This pattern can be expressed in OPPL 2 with the syntax
shown in Figure 4.
?cell:CLASS,
?nucleation:CLASS
BEGIN
ADD ?cell SubClassOf hasNucleation some ?nucleation
END;
Fig. 4. OPPL 2 pattern for cells and nucleation
There are two variables in the pattern, ?cell and ?nucleation. These vari-
able are mapped to column A and B respectively. The pattern is to be instanti-
ated using data from rows one to six that must be specified in the Wizard. The
next step involves validating the pattern, given that Proximal tubule epithelial
cell is unknown by the validator, the user is given the option to assign a new
URI for this concept. The final step generated the full OPPL script for applying
this pattern. The Manchester OWL syntax generated for row one is shown in
Figure 5. A complete grammar for the OPPL 2 syntax is available here8 .
8
http://oppl2.sourceforge.net
Class: cto:CL 0000113
SubClassOf:
hasNucleation some pato:PATO 0001407
Fig. 5. Mononuclear Phagocyte described in Manchester OWL syntax generated form
the OPPL 2 pattern in figure 4 (PATO 0001407 is the identifier for mononucleate)
3 Use case and evaluation
In order to evaluate Populous in a real ontology building scenario, it has been
used to populate a template for gathering knowledge about the kidney and uri-
nary system. The kidney is a complex organ composed of several distinct anatom-
ical compartments that together enable the filtration of waste from the blood
in the form of urine. Each of the kidney compartments is formed from a wide
variety of cell types, and the specificity of the compartments relies on these spe-
cialised cell functions. The Kidney and Urinary Pathway Ontology (KUPO) [12]
describes kidney cells, their function and their anatomical locations. KUPO is
being built to annotate and integrate a variety of KUP related data held in the
Kidney and Urinary Pathway Knowledge Base (KUPKB)9 .
A simple template was designed for experts from the KUP domain to capture
the relationships between cell types, their anatomical location and their biolog-
ical functions. The template has three main columns; column A is for entering
cell type terms, column C is for anatomy terms and column D for biological pro-
cess terms. Populous was used to constrain the allowable values in columns A, C
and D to concepts from the Open Biomedical Ontology Cell Type Ontology [4],
subclasses or part of the Kidney or Urinary system concepts from the Mouse
Adult Gross Anatomy Ontology [9], and all subclasses of the Biological Process
concept from the Gene Ontology [20], respectively. The experts were instructed
that the relationship between concepts in column A and C was part of, and the
relationship between column A and D, participates in. For concepts that were
related to multiple concepts they were allowed to list concepts in a cell separated
by a comma. Figure 6 is a screen shot of Populous populated with data from the
domain experts.
In order to transform the tabular data into an OWL representation the OPPL
pattern in Figure 7 was created by the ontology engineers. This pattern states
that a cell type is equivalent to a cell that is part of an anatomy term and a
subclass of cells that participate in biological processes.10 For both restrictions
the existential (some) quantification is used. The two differentia in this pattern
for a cell genus are the anatomical location and the biological process, which
is retrieved from column A, B and C respectively in the template. The entire
KUP ontology is generated from the template data combined with the ontology
9
http://www.e-lico.eu/kupkb
10
Where possible we use the relationships from [19].
Fig. 6. Screenshot of Populous showing template population for KUP ontology
pattern. Figure 8 shows the Manchester OWL syntax generated from the pattern
and data from row 13 for the Juxtaglomerular complex cell.
Using this template approach the domain experts described over 140 cell
types, many of which are absent from the current CTO. Figure 9 shows the
inferred hierarchy after classifying the ontology in Protégé 4.0. Note the asserted
class hierarchy is simply a flat list of cell types, the partonomy of the mouse
anatomy is used to drive inferences about super/sub class relationships between
cell types. Leaving the reasoner to compute the class hierarchy allows the domain
experts to inspect the ontology for missing or incorrect inferences. These often
indicate some missing information in the template, or an error in the imported
ontologies. This methodology was successful in engaging the domain experts to
both contribute to the KUP ontology and generate new term requests for the
imported ontologies.
4 Discussion
Populous is designed for domain experts to gather knowledge that can be subse-
quently used to populate ontologies. Whilst previous tools have provided support
for transforming templates into ontologies, they lacked basic support to help the
user at the point of data entry. Populous was designed to fill this niche and
meet the requirements outlined in section 1. The simple tabular interface used
Pattern 1
?cell:CLASS,
?anatomyPart:CLASS,
?partOfRestriction:CLASS = cell and part of some ?anatomyPart,
?anatomyIntersection:CLASS = createIntersection(?partOfRestriction.VALUES)
BEGIN
ADD ?cell equivalentTo ?anatomyIntersection
END;
Pattern 2
?participant:CLASS,
?participatesRestriction:CLASS = ?cell and participates in some
?participant,
?participatesIntersection:CLASS = createIntersection(?participatesRestriction.VALUES)
BEGIN
ADD ?cell SubClassOf ?participatesIntersection
END;
Fig. 7. Two OPPL 2 patterns for describing cell types in KUPO
Class: kupo 000027
EquivalentTo:
cell:CL 0000000
and (ro:part of some MA:MA 0002580)
SubClassOf:
cell:CL 0000000,
ro:participates in some gene ontology:GO 0002000,
ro:participates in some gene ontology:GO 0002001
Fig. 8. Manchester OWL syntax for Juxtaglomerular cell (MA 0002580 = ‘part of
afferent arteriole forming juxtaglomerular complex’, GO 0002000 = ‘detection of renal
blood flow’ and GO 0002001 = ‘renin secretion into blood stream’
Fig. 9. Screenshot of KUPO loaded into Protégé 4.0 showing inferred class hierarchy
for Juxtaglomerular cell
in Populous is familiar to users who have already used a spreadsheet application.
Populous should lower the entry requirements for domain experts to contribute
to ontology development projects.
The release of Populous as presented is an early version; there remains many
possible additions, some of which are:
1. Populous can handle multiple values in a cell that maps to conjunctions of
properties. Extensions to Populous such that some minor syntax can be used
to extend the ability to use OWL’s syntax would be useful. In particular,
being able to specify numbers for cardinality and numbers and other literals
for datatype properties.
2. As already described, Populous uses a row centric model. We aim to use M2
to enable more variety in how tables are mapped to templates or patterns.
For example, only portions of columns may be required to be mapped to
certain axiom patterns and M2 enables this sort of mapping. OPPL and M2
together should cover our mapping needs.
3. Populous currently gathers domain knowledge for the ontology, but not
about the ontology. We aim to extend Populous to support various metadata
such as editorial metadata and definitional metadata etc.
4. Populous is a single user application. Making Populous collaborative such
that contributors may collectively add material to the same spreadsheet.
5. Feedback from the generated ontology to fix or extend data in Populous is
currently ad hoc. A tighter coupling of this feedback cycle, without having
to go into an axiom based editor, will increase the quality assurance aspects
of Populous.
We have demonstrated how Populous can be used to develop an ontology
describing cells of the kidney and urinary pathway system. This demonstration
highlights how domain experts managed to generate a real application ontology
without being exposed to an ontology language like OWL, or a tool like Protégé.
Populous’s main purpose is for knowledge gathering and not ontologising. By
shielded users from the ontology, except for review later in the process, they
are left to concentrate on the biology and not worry about the axioms needed
to represent it. This separation is particularly useful should the ontologist wish
to change the conceptualisation or experiment with different patterns for the
representation.
Our experience in developing the KUPO with domain experts provided inter-
esting insights into the benefits of developing an ontology in this way. Classical
approaches to ontology development in the life science have tended to focus on
building rich asserted hierarchies of concepts. The KUPO approach exploits the
expressiveness of an ontology language like OWL to describe the cells in such a
way that the class hierarchy is computed by the reasoner. This means we have
a logical explanation for all the subsumptions in the hierarchy, that is useful
for spotting erroneous or missing information. For example, there are cell types
for the vasa recta descending limb and the vasa recta ascending limb, both of
which have different functions. The imported anatomy ontology, however, only
describes a vasa recta. The domain experts were able to spot this and can now
submit a request for these two new concepts to be added to the anatomy on-
tology. Building normalised ontologies that facilitate the kinds of inferences we
see in KUPO are generally considered to be harder and more time consuming
than constructing class hierarchies manually, despite offering a clear benefit [17].
However, in cases where a repeating pattern can be abstracted from the ontol-
ogy, as in the case of KUP cells, we see that domain experts can rapidly produce
rich ontologies with considerably less investment using Populous.
The question now arises as to how far can you go with a tool like Populous?
Populous is by no means a replacement for full blown ontology editors, nor is
it intended to be. The scenarios where Populous is of benefit assume that the
ontology being developed has repeating patterns in the modelling. Furthermore,
specifying the patterns for new ontologies before they exist is particularly dif-
ficult and is often something that emerges later as the ontology matures. For
example, it was assumed with the kidney cells that we could describe them all
in terms of their anatomy, only to later find some exceptions to the pattern.
Renal principal and renal intercalated cells are currently indistinguishable by
anatomy and function alone. There are always going to be exceptions, especially
when modelling a complex domain like biology. We hope that Populous can bring
more domain experts into the ontology development process and engage them
in the development process.
The template approach can be particularly advantageous in scenarios where
the modelling needs to change. [16] showed how templates can be used to gen-
erate different ontological representations of the same data. The KUPO is also
being used to link data in an RDF store to support the KUP KB, where only
limited support for OWL inferences is possible. By developing a different set
of patterns we could generate a simpler version of the KUP ontology from the
same Populous data for use in such an application. This again highlights an
added benefit of separating the pattern from its population.
OPPL provides an expressive language for generating OWL patterns. OPPL’s
support for variables make mapping single columns from tabular data to vari-
ables convenient. The built in macros means we can create abstract pattern
where the axioms can be generated dynamically depending on the number of
values stored in a variable. Populous is currently limited to working with uni-
form spreadsheets and assumes a row-per-entity paradigm, where single columns
map to a particular variable. This structure keeps the template simple and should
cover the majority of use cases for populating an ontology in this way. The ex-
tension to support multi-values per cell offers some additional flexibility over
existing spreadsheet based approaches. In order to accommodate more complex
spreadsheets we plan to extend Populous to support more complex mappings
from columns to spreadsheets. We are also exploring integrating the M2 lan-
guage from MappingMaster directly into Populous, we note that templates cre-
ated in Populous can already be exported as Excel document and loaded into
MappingMaster for transformation should the user desire.
Populous offers a means of creating ontology content without the use of a
standard ontology development tool. Just as data-entry tools exist for populating
databases, so we also need such tools for populating ontologies. It is possible to
separate knowledge gathering from conceptualisation and axiomatisation and
Populous is one means of achieving this goal. Such a separation offers flexibility
and simple form fill-in style of knowledge gathering that should make generation
of axiomatically rich ontologies increasingly straight-forward.
Acknowledgements: We kindly acknowledge Mikel Egaña Aranguren for
advice, requirements and testing Populous. This work is funded by the e-LICO
project—EU/FP7/ICT-2007.4.4 and by SysMO-DB - BBSRC grant BBG0102181.
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