USING FORMAL ONTOLOGIES FOR THE DEVELOPMENT
OF CONSISTENT AND UNAMBIGUOUS FINANCIAL
ACCOUNTING STANDARDS
Marthinus C. Gerber
1
, Aurona J. Gerber
2,3
and Alta J. van der Merwe
2
1
Department of Accounting, University of Pretoria, Pretoria, South Africa
2
Meraka-CSIR, Pretoria, South Africa
3
Centre for Artificial Intelligence Research, University of Kwazulu Natal, Durban, South Africa
Keywords:
Formal ontology, Accounting ontology, Accounting framework, Conceptual framework, Accounting stan-
dards.
Abstract:
The purpose of accounting is to gather financial data of a business or entity, to interpret this data and to
report the results in financial statements to the different users thereof. The interpretation of financial data is
regulated by financial accounting standards including a conceptual framework that were developed to facilitate
the reporting of financial information of entities. Due to the history of standards development as well as the
mechanisms used, inconsistencies in the standards, framework and interpretations are part of the common
legacy accountants are confronted with every day. The development of unambiguous and principle based
financial accounting standards is therefore a key initiative at present of international accounting standards
bodies such as the FASB and the IASB. This paper is concerned with the question of how recently developed
computer science technologies could assist in dealing with and eliminating inconsistencies and ambiguities
within and between different financial accounting standards. In our research we developed a formal ontology
for some of the basic elements, and in this paper we report on our findings as well as make some suggestions
for a formal approach to the conceptual framework and financial accounting standards development.
1 INTRODUCTION
“Inconsistencies cannot all be right; but, im-
puted to man, they may all be true“ (Samuel
Johnson).
Inconsistencies are part of the common legacy ac-
countants are confronted with every day and some-
thing that is part of their daily lives (Adamides, 2008;
FASB, 2009). Non-accountants often think that ac-
counting is as clearcut as debit and credit or the age
old and proven double entry system (Brief, 1996).
With this view, ambiguity cannot be a problem: what
can be ambiguous or inconsistent about debit and
credit? However, a clear, consistent and unambigu-
ous world is not the reality accountants experience
when they compile financial reports. The Financial
Accounting Standards Board (FASB) acknowledges
this as is evident in this quotation: ”The Board be-
lieves that financial reporting is both simplified and
improved by removing obsolete standards, eliminat-
ing inconsistencies, providing certain clarifications to
reflect the Board’s intent ....“(FASB, 2009, p.24).
2 BACKGROUND
The information provided in an entity’s financial
statements is compiled using an interpretation of its
financial data (actual transactions). This interpreta-
tion is regulated by Financial Accounting Standards
(FAS). These financial accounting standards include
an accounting framework and were developed to fa-
cilitate the provision or disclosure of financial infor-
mation of entities so that investors, analysts, credi-
tors as well as the entities themselves can make in-
formed financial decisions (Camfferman and Zeff,
2009; IFRS, 2011).
At present the two international bodies that are the
main drivers behind global financial accounting stan-
dards (the United States based FASB and the London-
based IASB) acknowledge that the task of setting
global financial accounting standards that report un-
419
C. Gerber M., J. Gerber A. and van der Merwe A..
USING FORMAL ONTOLOGIES FOR THE DEVELOPMENT OF CONSISTENT AND UNAMBIGUOUS FINANCIAL ACCOUNTING STANDARDS.
DOI: 10.5220/0003658404190424
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 419-424
ISBN: 978-989-8425-80-5
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

ambiguously on all possible financial situations for a
wide variety of audiences is enormous in scope and
extremely difficult, and therefore exhibit ambiguities
and inconsistencies (Adamides, 2008; FASB, 2009).
There are also a lack of semantic tools and formal
techniques that could assist standard setters in elim-
inating inconsistencies and ambiguities.
Since 2004, a collaborative initiative between the
FASB and IASB aims to jointly issue financial ac-
counting standards and a common conceptual frame-
work because of the acknowledged need for inter-
national unification and a sound foundation for fu-
ture financial accounting standards that are principles-
based, internally consistent and internationally con-
verged (FASB, 2010; Booth, 2003; FASAC, 2004).
This paper reports on an investigation into the use
of ontologies as a mechanism to detect and manage
ambiguities and inconsistencies. We argue that the
creation of a formal ontology could be a starting point
to develop an unambiguous conceptual framework for
financial accounting standards.Ontologies and their
associated technologies made an appearance within
Computer Science during the past ten to fifteen years
mainly due to advances in reasoning and modeling
technologies (Wolstencroft et al., 2005; Hahn and
Schulz, 2007). A formal and widely used definition
is that of Gr
¨
uber who defines an ontology as a formal
specification of a conceptualisation (Gr
¨
uber, 1993).
An ontology formally describes a domain model in a
way that attaches meaning to the terms (concepts) and
relations between these concepts used for describing
the domain.
Ontologies allow for the construction of complex
and consistent conceptual models, but more signif-
icantly, ontologies can assist with the unambiguous
formalisation of the terminology of a domain, en-
abling not only people, but also computers to un-
derstand, share and reason using knowledge. Exam-
ples where ontologies were applied for standardisa-
tion with substantial benefit in the recent past include
the Gene Ontology (GO) (GO, 2011) and Snomed CT
(IHTSDO, 2011). In both these examples formal on-
tology technologies were used with great benefit to
create an unambiguous and consistent terminology of
a domain of discourse, which lead to further benefits
such as information integration across sub-disciplines
in the domain.
The construction and maintenance of formal on-
tologies is possible due to the availability of on-
tology languages such as the class of logics called
description logics or DLs, equipped with a well-
defined semantics and powerful reasoning tools
(Baader et al., 2003). The W3C’s Web Ontology
Language (OWL) standard is based on a family of
expressive Description Logics (W3C, 2006; McGuin-
ness and van Harmelen, 2004). One of the conse-
quences of the standardisation of OWL by die W3C
is the development of several tools and reasoners that
support the OWL standard such as Prot
´
eg
´
e 4 and
SWOOP (Protege, 2011; SWOOP, 2009), Fact++ and
Pellet (Fact++, 2009; Pellet, 2009)
1
.
This paper is concerned with the question of how
formal ontology construction using available tools
could assist in dealing with and eliminating incon-
sistencies and ambiguities within and between dif-
ferent financial accounting standards. Definitions are
used as foundations in any standardisation effort and
should, especially in an environment such as account-
ing where data should be interpreted correctly by any-
one accessing the data, not be inconsistent or am-
biguous. The first step in our research was to deter-
mine whether formal ontology technologies could be
used to formalise the accounting conceptual frame-
work definitions that has to guide the setting of stan-
dards.
3 ONTOLOGY CONSTRUCTION
The approach followed was roughly based on an on-
tology engineering methodology as defined by both
Horridge (2009) and Noy (2000) . We used Prot
´
eg
´
e 4
to develop an OWL 2.0 ontology for the basic defini-
tions of the core elements necessary in the conceptual
framework (Protege, 2011). Problems encountered,
modeling decisions as well as our solutions are dis-
cussed in detail in a report available at http://url-to-
be-provided. In this paper we depict the formal defin-
tions in DL notation in Table 1.
For the purpose of this paper we consider the defi-
nitions of the elements directly related to the measure-
ment of financial position as defined in the Concep-
tual Framework Document Chapter 4 (IASB, 2011).
The elements directly related to the measurement of
financial position are assets, liabilities and equity.
These are defined as follow:
(a) An asset is a resource controlled by the entity as
a result of past events and from which future eco-
nomic benefits are expected to flow to the entity.
(b) A liability is a present obligation of the entity
arising from past events, the settlement of which
is expected to result in an outflow from the entity
of resources embodying economic benefits.
(c) Equity is the residual interest in the assets of the
entity after deducting all its liabilities.
1
A summary of a substantial num-
ber of DL reasoners can be found at
http://www.cs.man.ac.uk/∼sattler/reasoners.html
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
420

We investigated each definition and constructed a
formal ontology. The first modeling decision resolved
was with regards to the modeling of time, specifically
the concepts Past, Present and Future as used in the
definitions. In standard ontology development, an as-
sertion is a Boolean-valued sentence that is either true
or false. An approach to representing time-dependent
statements is to associate them with time elements
(i.e. instantaneous points or durative intervals). In the
discussion of Ma (2007), the theoretical foundations
for such an approach are discussed. For this paper it
is sufficient to note that three time intervals exist be-
cause a statement or report is compiled in the Present,
based on Past events and with a perspective on the Fu-
ture. It may be necessary to refine these time intervals
further in future. For now, the concept TimeSlot was
modelled to consist completely of the disjoint con-
cepts Past, Present and Future. Other concepts in
the ontolgy necessary to define the elements (such as
Past Events) are refined through their participation in
relationships with the timeslots. Past, Present and
Future are disjoint concepts and TimeSlot is fully
described by the union of Past, Present and Future
as depicted in Table 1.
3.0.1 Modeling an Asset, Liability and Equity
When we decomposed these definitions, we
identified the following concepts: Resource,
Entity, Event, Benefit with sub-concept
EconomicBenefit, Obligation, Settlement,
Interest and ResidualInterest which is a type
of Interest.
Several refinements on the concepts due to the rela-
tionships they participate in, are identifiable:
• PastEvent is an Event refined using a Past
timeslot through the happenIn object prop-
erty and FutureEconomicBenefit is a
EconomicBenefit refined using a Future
timeslot through the happenIn object property.
Similarly, a PresentObligation happens in the
Present.
• ControlledResource is a type of Resource.
However, Control is more than simply a bi-
nary relation, meaning that we have to model it
as a concept. There are for instance different
types of Control and Control is the result of
PastEvents. Modelling Control as a concept
implies the introduction of object properties that
relates Entity to Control via hasControl and
Resource to Control via isControlledBy.
• The use of expected is problematic as
it is not really clear whether it refines
FutureEconomicBenefit or flow (which is
a relation). We made the modeling decision to
create an Expected Future Economic Benefit or
the EFEB concept as a FutureEconomicBenefit
that is expectedBy an Entity.
• An Asset is a ControlledResource
fromWhichInflow.EFEB.
• An Obligation is a result of at least
one PastEvent and an Obligation has a
Settlement.
• An Entity has at least one PresentObligation.
• The outflow of resources embodying eco-
nomic benefits refines Settlement. How-
ever, this implies the addition of a con-
cept ResourceEmbodyingEconomicBenefit
which is a Resource that embodies some
EconomicBenefit. However, the whole notion
of a resource embodying economic benefit is un-
clear. Surely this definition should be integrated
with the definition of Asset since an outflow
from an entity can not be of any resource, only a
resource complying with the definition of an asset
can flow from the entity.
• The use of expected is problematic as it is not
really clear whether it refines Settlement or
flow (which is a relation). We made the choice
consistent with the previous one to create an
ExpectedSettlement concept as a Settlement
that is expectedBy an Entity.
• A Liability is a PresentObligation and it
hasSettlement ExpectedSettlement.
• ResidualInterest has to refined as it is inter-
est in Assets after deducting Liabilities. DL
is formally based on set theory and a way to for-
malise the notion of deduction in a DL ontology
is through set difference or formally: B\A = {x ∈
B | x /∈ A}. In this case, Equity is Asset and
not Liability.
• However, the modeling of Equity remains prob-
lematic because, from the previous definitions, an
Asset is a refined Resource and a Liability is
a refined Obligation. Assets and obligations are
derived from different and disjoint concepts and
therefore no concept can be created that is a com-
bination of them (e.g. deducting Liability from
Asset or AssetInterest). It would be an in-
consistent concept implying that no instantiation
exists. The definitions as presented formally
translates into an inconsistent concept. Equity
is Asset and not Liability and the reasoner
inferred that this concept is inconsistent.
• To remove this inconsistency, we did further anal-
ysis of the definition of Equity. The definition im-
plies a value, or in the terminology of the defini-
tion, an interest to be associated with both assets
and liabilities because residual interest is the re-
USING FORMAL ONTOLOGIES FOR THE DEVELOPMENT OF CONSISTENT AND UNAMBIGUOUS FINANCIAL
ACCOUNTING STANDARDS
421

sult. We therefore add
– AssetInterest and LiabilityInterest as
types of Interest specifically to be able to
show that Asset and Liability hasInterest
AssetInterest and LiabilityInterest re-
spectively.
• Using set difference for deduction in this ontol-
ogy:
– ResidualInterest is the set difference be-
tween Interest and LiabilityInterest.
– Another decision necessary in order to formally
model the set difference, is that all Interest is
AssetInterest or LiabilityInterest.
• Equity is Interest and NOT
LiabilityInterest.
Table 1: Assertions in the Ontology.
Past v Timeslot
Present v Timeslot
Future v Timeslot
Timeslot = Past t Present tF uture
PastEvent v Event u ∃happenIn.Past
Control v ∃isResultO f .PastEvent
Entity v ∃hasControl.Control
ControlledResource v Resource u ∃isControlledBy.Control
EconomicBene f it v Bene f it
FutureEconomicBene f it v
EconomicBene f it u ∃happenIn.Future
EFEB v FutureEconomicBene f it u ∃expectedBy.Entity
Asset = ControlledResource u ∃ f romW hichIn f low.EFEB
Obligation v
∃isResultO f .PastEvent u ∃hasSettlement.Settlement
PresentObligation v Obligationu ∃happenIn.Present
Entity v ∃hasObligation.PresentObligation
ResourceEmbodyingEconomicBene f it v
Resource u ∃embodies.EconomicBene f it
Settlement v
∃ f romW hichOut f low.ResourceEmbodyingEconomicBene f it
ExpectedSettlement v Settlement u ∃expectedBy.Entity
Liability = PresentObligation u
∃hasSettlement.ExpectedSettlement
ResidualInterest v Interest
AssetInterest v Interest
LiabilityInterest v Interest
Interest v AssetInterest tLiabilityInterest
Asset v ∃hasInterest.AssetInterest
Liability v ∃hasInterest.LiabilityInterest
ResidualInterest v Interest u ¬LiabilityInterest
Equity = ResidualInterest
The Prot
´
eg
´
e 4 ontology editor allows the us-
age of several reasoners to assist with ontol-
ogy construction (Fact++, 2009; Sirin et al.,
2007) and the inferred concept hierarchy depicts
all the consequences of asserted statements such
as ResidualInterest concept which is defined
as Interest and not LiabilityInterest. The
reasoning technologies were used to detect in-
consistencies such as that of Equity. Further-
more, Equity = ResidualInterest and therefore
ResidualInterest = AssetInterest. This is an
example of the advantages of reasoning technologies
supporting formal ontology development and it pro-
vides evidence of the benefit of these technologies for
tasks such as the creation of unambiguous definitions
in the accounting framework, especially when the on-
tology is large and complex.
4 FINDINGS
The construction of a formal ontology for the ele-
ments necessary for the measurement of financial po-
sition in the conceptual framework resulted in a first
version ontology with ALCH DL expressivity for-
mally defining asset, liability and equity unambigu-
ously. The refinement and usefulness of such an on-
tology require further research. Ontology engineering
is also in essence a collaborative exercise and an on-
tology should reflect consensus about a domain. From
this proof of concept it is necessary to create an ini-
tiative including all stakeholders for the further devel-
opment of a formal ontology representing the concep-
tual framework.
The most significant finding is that the approach
allowed us to detect significant assumptions in the
definitions of the elements, which is not evident at
first glance. Any accountant using these text-based
definitions will have to make decisions based on as-
sumptions, and different decisions could lead to con-
tradictory financial interpretations and ultimately, fi-
nancial reports.
4.1 Findings: Approach and Use of
Prot
´
eg
´
e 4
In the following list the findings with regards to the
approach we used as well as the use of Prot
´
eg
´
e 4 with
the bundled reasoners are presented.
• A formal ontology of the element defintions could
only be constructed after making several mod-
eling decisions about aspects that were unclear.
Anybody intending to use the definitions will be
confronted with the same ambiguities and lack of
information and clarity. However, it is useful to
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
422

construct a formal model with explicit meaning
that could be refined later rather than to use un-
clear definitions with ambiguous results.
• Familiarity with the DL languages remains a pre-
requisite for formal DL-based ontology construc-
tion, irrespective of the tools used. An example of
this is the modelling necessary for the notion of
deduction.
• Prot
´
eg
´
e 4 is easy to use and enabled us to create a
formal ontology without too much effort. Ontol-
ogy editors such as Prot
´
eg
´
e 4 definitely could as-
sist standard setters to define a conceptual frame-
work in a standardised formal language (such as
OWL). A drawback of Prot
´
eg
´
e 4 remains graph-
ical rendering tools. Graphical displays will al-
ways remain important for modeling and ontology
comprehension. In addition, the lack of tools that
could assist with ontology debugging such as ex-
plaining an inference result, remains a significant
drawback, especially when models are complex.
• There are still at present no firmly established
methodologies for ontology engineering. It is
generally recognised that this is a research topic
that warrants urgent attention (G
´
omez-P
´
erez et al.,
2004). When constructing a formal ontology for
the conceptual framework and financial account-
ing standards, this is even more important and will
probably have to be tailored towards the specific
requirements of standard setters.
• Prot
´
eg
´
e 4 is open source software. The source
code is freely available and there is an active in-
ternational developer community. An applica-
tion can therefore be developed to fulfill the re-
quirements of a specific initiative based on the
Prot
´
eg
´
e 4 environment, for instance by crating
special graphical displays of an ontology.
4.2 Findings: The Use of Ontology
Technologies for the Formalisation
of the Conceptual Framework
The following list summarises our findings with re-
gards to the formalisation of the defintions:
• The most significant advantage is that the use of
formal ontology technologies allow for clear and
consistent definitions because the ontology is con-
structed with assertions that has specific meaning.
The assertions are unambiguous and their mean-
ing is clear. Even if domain experts do not agree
completely with an assertion, the meaning thereof
is clear and could be altered to reflect consensus.
• The use of ontology technologies allowed us to
detect assumptions that could lead to inconsisten-
cies in the current defintions of the basic elements
needed for financial reporting.
• The use of this approach allows for the specifica-
tion of concise definitions of elements, their com-
ponent concepts and relations. This could be used
by standard setters to construct standards and in-
terpretations that adhere to the formal core frame-
work specification.
• The use of precise and formal definitions of ele-
ments could assists with detecting inconsistencies
between definitions, financial accounting stan-
dards and interpretations.
• Concepts and relations were identified within the
definitions of the basic elements to be used in
the formal ontology without a clarification of the
meaning of those concepts and relations. Exam-
ples of such assumptions and modeling decisions
made include:
– Resource. The concept resource was identi-
fied and used in the ontology, but what exactly
is a resource from an accounting perspective?
For the concept to be reusable, the meaning
within the accounting domain must be clearly
indicated.
– Past, Present and Future Events. The de-
cision was made to identify these notions of
time as concepts, but are they really concepts or
should they be modeled as relations? Further-
more, are these concepts necessary in the def-
initions of the basic elements, or can the basic
elements be defined without reference to these
time indicators?
– Possible and Expected. These terms are vague
and were used in the formal model because they
form part of the original definitions. The mean-
ing of these terms is not clear and neither is it
clear what the contribution of these terms in the
definitions of the basic elements are.
– Economic Benefit. The concept economic ben-
efit is used in the definitions without any indi-
cation to the meaning of the concept.
5 CONCLUSIONS
From the proof of concept ontology construction there
is evidence that formal ontologies and the associated
technologies can play a substantial role to enhance
the quality of the conceptual framework and the as-
sociated definitions. Ontology statements are explicit
and precise, and consequences of assertions can be
exposed using reasoning technologies. Ontologies
can represent the required definitions of elements in
a much more precise and unambiguous manner than
the text format used at present. The formal languages
USING FORMAL ONTOLOGIES FOR THE DEVELOPMENT OF CONSISTENT AND UNAMBIGUOUS FINANCIAL
ACCOUNTING STANDARDS
423

used for ontology construction are international stan-
dardised languages and this should promote unam-
biguously, clarity and consistent financial accounting
standards and interpretations globally.
Given the results of the investigation into the use
of formal ontologies for the development of consis-
tent and unambiguous financial accounting standards,
we may perhaps alter Samuel Johnson’s quote to:
“Inconsistencies cannot all be right; and, im-
puted to the true representation of knowledge,
only one consensual truth remains.“
REFERENCES
Adamides, A. (2008). Fair value project. Charter, 79: Ac-
counting and Tax(4):64–65.
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., and
Patel-Schneider, P. (2003). The Description Logic
Handbook: Theory, Implementation, and Applica-
tions. Cambridge University Press.
Booth, B. (2003). The conceptual framework as a coherent
system for the development of accounting standards.
Abacus, 39(3),:310.
Brief, R. P. (1996). Accounting History from the Renais-
sance to the Present: a Remembrance of Luca Pacioli.
New York: Garland.
Camfferman, K. and Zeff, S. A. (2009). Financial Report-
ing and Global Capital Markets: A History of the
Inernational Accounting Standards Committee 1973-
2000. Oxford University Press.
Fact++ (2009). Fact++ ontology reasoner. Web.
FASAC (2004). Financial accounting standards advisory
council: Joint conceptual framework project.
FASB (2009). Financial accounting series: Expo-
sure draft: Proposed statement of financial ac-
counting standards. file reference no: 1670-100.
http://www.fasb.org/draft/ed technical corrections.pdf.
FASB (2010). News release 09/28/10: IASB and US
FASB Complete First Stage of Conceptual Frame-
work. FASB Website. http://www.fasb.org/.
GO (2011). The gene ontology. http://www.geneontology.
org/.
G
´
omez-P
´
erez, A., Fern
´
andez-L
´
opez, M., and Chorco, O.
(2004). Ontological Engineering. Springer.
Gr
¨
uber, T. (1993). A translation approach to portable on-
tology specifications. Knowledge Acquisition, 5:199–
220.
Hahn, U. and Schulz, S. (2007). Ontological founda-
tions for biomedical sciences. Artificial Intelligence
in Medicine, 39(3):179–182.
Horridge, M. (2009). A practical guide to
building owl ontologies using protege 4
and co-ode tools: Edition 1.2. WWW.
http://owl.cs.manchester.ac.uk/tutorials/protegeowltut
orial/resources/ProtegeOWLTutorialP4
v1 2.pdf.
IASB (2011). The Conceptial Framework. IASB.
http://eifrs.iasb.org/eifrs/bnstandards/en/framework.p
df.
IFRS (2011). Ifrs. http://www.ifrs.org/Home.htm.
IHTSDO (2011). Snomed ct. http://www.ihtsdo.org/snome
d-ct/.
Ma, J. (2007). Ontological considerations of time, meta-
predicates and temporal propositions. Applied Ontol-
ogy, 2(Number 1/2007):37–66.
McGuinness, D. L. and van Harmelen, F. (2004). Owl web
ontology language overview. W3C Web site. Last
accessed 13/9/2006.
Noy, N. F. and McGuinness, D. L. (2000). On-
tology development 101: A guide to cre-
ating your first ontology. Technical report,
Stanford University, Stanford, CA, 94305.
http://protege.stanford.edu/publications/ontology dev
elopment/ontology101.pdf - Last accessed 16/9/2006.
Pellet (2009). Pellet: The open source owl dl reasoner. Web.
Protege (2011). The prot
´
eg
´
e Ontology Editor.
http://protege.stanford.edu/.
Sirin, E., Parsia, B., Grau, B. C., Kalyanpur, A., and Katz,
Y. (2007). Pellet: A practical owl-dl reasoner. Journal
of Web Semantics.
SWOOP (2009). Swoop - semantic web ontology editor.
Web.
W3C (2006). The world wide web consortium (w3c). W3C
Web site.
Wolstencroft, K., Brass, A., Horrocks, I., Lord, P., and
U. Sattler, e. a. (2005). A little semantic web goes a
long way in biology. Lecture Notes in Computer Sci-
ence: The Semantic Web ISWC 2005, 3729:786–800.
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
424