Origins of The IEEE Standard Upper Ontology
Ian Niles, Adam Pease
Teknowledge[iniles,apease]@teknowledge.com

Introduction
Research and applications in computer science
are creating the need for precise definitions of
the concepts that make up our world. Web
searching is handicapped by the limitations ofspecifying search criteria in terms of keywords
rather than concepts.
Automated natural
language understanding, both oral and written, isseverely limited by the ambiguity of language.
Software engineering is limited by the need forengineers to define concepts to model the world.
Computers exist in a world similar to Europe
in the Middle Ages in which tiny principalities
each had their own language or dialect. Worse
yet, these dialects are impoverished and they
enable the computers to say only very specificand limited things. In order to enable continued
progress in ecommerce and software integration,
we must give computers a common languagewith a richness that more closely approaches thatof human language.
Integrating the meaning (or semantics) of
databases and programs is crucial for creating
software that is reliable and scalable. The use of
ontologies to specify semantics is emerging as apromising technique for software integration.
Creators of different components often assume
they understand the terms in the same way. The
reality is that is rarely the case. Even the bestdocumented code has implicit assumptions andambiguity in the definition and usage of terms.
Research in several areas including computerscience, artificial intelligence, philosophy,
library science and linguistics are helping tomeet these needs.
All these fields haveexperience with creating precise and standard
descriptions and terminology for the things thatmake up our world. (Sowa 2000)
Current research is hampered by several
issues. Computer scientists and philosopherslack consensus in their communities for creating
the very large, wide-coverage ontologies that areneeded, although they have the necessary formal
languages to do so. Librarians and linguists
have the charter to create large ontologies but
those ontologies have typically lacked the formal
definitions needed for reasoning and decisionmaking. In fact, it is probably fair to claim thatto date no group has taken full advantage of thevast body of historical work in this area One

group that has developeda large formal
ontology is Cycorp. However, ithas released
only a small part to the public,
retains
proprietary rights to the vast bulk of their
ontology, (Lenat 1995) and the contents of the
ontology have not been subject to extensive peer
review
In this paper we discuss the origins of theStandard Upper ontology effort (SUO, 2001), thestrategy used in created the current working draftof the merged ontology, and the current state of
the ontology .

The Standard Upper Ontology
Recognizing both the need for large
ontologies and the need for an open process
leading to a free, public standard, a diverse
group of people has come together to make sucha standard a reality.
The Standard Upper Ontology (SUO) will bean upper level ontology that provides definitions
for general-purpose terms and acts as afoundation for more specific domain ontologies.
It is estimated to contain between 1000 and 2500
terms plus roughly ten definitional statements foreach term.
Purpose of Project
The standard will be suitable to support
knowledge-based reasoning applications.
• This standard will enable the developmentof a large (20,000+) general-purpose
standard ontology of common concepts,
which will provide the basis for middlelevel domain ontologies and lower-levelapplication ontologies.
• The ontology will be suitable for"compilation" to more restricted forms suchas XML or database schemata. This will
enable database developers to define newdata elements in terms of a common
ontology, and thereby gain some degree ofinteroperability with other compliant
systems.
• Owners of existing systems will be able tomap existing data elements just once to acommon ontology, and thereby gain adegree of interoperability with other
•

•

•

representations that are compliant with the
SUO.
Domain-specific ontologies that arecompliant with the SUO will be able tointeroperate (to some degree) by virtue ofthe shared common terms and definitions.
Applications of the ontology will include:
E-commerce applications from different
domains which need to interoperate atboth the data and semantic levels.
Educational applications in which
students learn concepts andrelationships directly from, or expressedin terms of, a common ontology. This
will also enable a standard record oflearning to be kept.
Natural language understanding tasks inwhich a knowledge-based reasoning
system uses the ontology todisambiguate natural language terms
and structures.

The current working paper for the standard isan ontology in a version of KIF (Genesereth,1991) called SUO-KIF. It is a somewhat
simplified version of KIF that is itself a separate
standards effort proposed as a new work item inMarch 2001 under the ISO.
The SUO email list was created in May of2000 and quickly had over 150 subscribers. The
Project Authorization Request (PAR) which
details the scope and purpose for the SUO effort
was submitted to the IEEE in October and wasapproved as working group P1600.1 inDecember with James Schoening as chair.

Strategy - Reflective Equilibrium
Three common strategies are used in building
ontologies, viz. “top-down”, “bottom-up”, and“middle-out”. Each of these strategies has
advantages and limitations.
A “top-down”
approach begins by identifying all of therelevant,
philosophico-linguistic
concepts,
organizing them into a compact, high-leveltaxonomy and system of axioms, and thenproceeding from there to more specific concepts
and axioms. The main advantage of the topdown approach is that, if it is executed correctly,
it results in a structure which represents a bird’s
eye view of the world and which should makethe task of defining domain-specific content
relatively trivial.
The limitation of the “top-down” approach toontological engineering is a practical one. Thisapproach is embodied in the 2,500-year historyof western philosophy, and this discipline has
not yielded any conclusions that a majority ofphilosophers could agree on. It has given us anice knowledge representation language, thefirst-order predicate calculus, but it has not
resulted in any consensus about the nature of themost general categories of the world and themind. If some of the deepest thinkers of thewestern world still haven't come to any solid
conclusions after working through these
problems for 2,500 years, what hope is there that
we mere mortals can do any better?
Furthermore, traditional metaphysics andepistemology may not even be relevant to thetask of constructing an upper-level ontology.
This sounds paradoxical since computerscientists and philosophers use the same word torefer to very similar formal structures ofconcepts and axioms. However, in this case,appearances are misleading.
Ontologicalphilosophers have a radically different aim fromontological engineers. The latter are attempting
to construct an engineering artifact that will
promoteinformation
sharing
betweenheterogeneous automated systems, while theformer are trying to get clear about the nature ofultimate reality and what we can know about it.
Since the aims in the two cases are so different,much of the theoretical content of philosophy
will not be relevant to the task of defining an
upper-level engineering ontology. In the context
of the SUO project, we're not trying to stakeclaims on the nature of the world; we'reessentially trying to develop the semantic content
of a language that can express such claims (andmany other claims as well).
Opposed to the so-called “top-down”
approach to ontological engineering are the“bottom-up” and “middle-out” approaches. Bothof these latter approaches begin with domainspecific concepts and then develop the ontology
up or out from there. The advantage of this isthat it is, generally speaking, much easier tosecure agreement about the sorts of lower level
concepts that belong in an ontology.
Accordingly, these approaches are very usefulfor creating domain-specific ontologies fromscratch. However, they are not as applicablewhen the goal is to reuse a stock of variously
conceived conceptual content. These approaches
do not really lend themselves to the task ofmerging a collection of very different ontologies.
Without some sort of foundation to which thevarious ontologies can be tied, it is difficult torelate these ontologies to one another.
In constructing the merged ontology, we
followed an approach that might, after John
Rawls’ term, be labeled “reflective equilibrium”.

As we see it, this approach has all of theadvantages and none of the disadvantages of the“top-down”, “middle-out”, and “bottom-up”
approaches.
This novel approach can be
described as follows. We begin with a set ofhigh-level categories and then successively
incorporate new content under these categories.
The inevitable incompatibilities between newcontent and existing content are resolved on acase-by-case basis. If we find that some highlevel categories are not needed to support lowerlevel nodes, then they are excised from theconceptual structure. If we find that a lowerlevel structure can be simplified by locating partof its semantic endowment in a new higher-level
node (from which the lower-level structure caninherit this content), we can add that node to the
ontology. Self-consistent chunks of ontologicalcontent sheds some light on higher-level
concepts, while explicating the latter concepts
partially determines the nature of subsumed
concepts. Eventually (if we allow ourselves tobe optimistic) we will reach a stage where thedemands of both the high-level concepts and thelower-level chunks have been satisfied in thesimplest manner possible. At that stage, we will
have reached equilibrium.
This resulting
ontology will not contain all of the originalcontent in the axiomatic chunks or thedefinitions of high-level concepts, but it should
be comprehensive in the sense that it provides ahook on which to hang any domain-specificontology.

Tactics – Sifting through the Mound
After figuring out how we would approach thetask of creating the merged ontology, we
proceeded to the next level of detail and outlined
a four-step procedure for actually constructing
the ontology.
1.
2.
3.
4.

Identify relevant, freely available
content.
Translate content into the same KR
language.
Merge content into a single,
consistent, comprehensive ontology
by using “reflective equilibrium”.
Submit content to SUO mailing list forextension/revision.

The first step was to identify all high-level
ontological content that did not have licensing
restrictions. This content included the libraries
of ontologies available on the Ontolingua server
and from ITBM-CNR, John Sowa’s upper-level
ontology (Sowa, 2000), Russell and Norvig’s

upper-level ontology (Russell & Norvig, 1995),
James Allen’s temporal axioms (Allen, 1984),
Casati and Varzi’s formal theory of holes (Casati& Varzi, 1995), Barry Smith’s ontology ofboundaries (Smith, 1994, 1996), Nicola
Guarino’s formal mereotopology (Borgo et al,
1995, 1996), and various formal representationsof plans and processes including CPR (Pease,1997) and PSL (Schlenoff et al, 2000). After allof the relevant content was identified and linked
to
the
SUO
web
site
(http://suo.ieee.org/refs.html), it was translated
into a single knowledge representation language.
The language used is a simplified variant of KIF
as mentioned abouve and a specification ofwhich can be found at http://suo.ieee.org/suokif.html.
After the translation of the ontological content
into SUO-KIF (known as the “syntactic merge”)
had been completed, we were faced with themuch more difficult task of the “semantic
merge”, i.e. combining all of the various
ontologies into a single, consistent, andcomprehensive framework. The ontologies werefirst divided into two classes, viz. those defining
very high-level philosophico-linguistic concepts
and those defining lower-level notions. The firstclass contained John Sowa’s upper-level
ontology and Russell and Norvig’s upper-level
ontology, and the second class contained
everything else.
After this partition wascompleted, the two upper-level ontologies weremelded into a single upper-level ontology. Sinceboth ontologies are very compact and contain asignificant amount of overlapping content, thismerge did not pose any serious practicalproblems. This merged upper-level ontology
was then used as the foundation for aligning allof the other content that had been converted to
the SUO-KIF format.
One of the biggest challenges in merging andaligning ontologies is that many of the chunks
that have to be merged and aligned will be, to alesser or greater extent, incompatible. In somecases, the incompatibilities can be smoothed
over by tweaking definitions of concepts orformalizations of axioms; in other cases,
wholesale theoretical revision may be required.
In hooking the lower-level content into the tip of
the ontology, we came across four possible
cases.
1.
2.
3.

New content that is useful andrelatively noncontroversial
New content that is either
controversial or without anengineering application.
Perfect overlap with already existing

4.

content
Partial overlap with already existing
content

In the first case, nothing in the tip of the
ontology corresponds with the concept/axiom to
be mapped, and the concept/axiom is deemed to
be useful and not to violate any cherishedphilosophical principles. Once the decision wasmade to include a concept/axiom in the merged
ontology, it was simply a matter of finding aplace for it. In some cases, this involved thecreation of some intermediate levels of concepts
between existing concepts and new content. An
example of the first case is the subhierarchy ofnumbers that was taken from the KIF-Numbers
ontology on the Ontolingua server.
(subclass-of RealNumber Number)(subclass-of RationalNumber RealNumber)(subclass-of PositiveRealNumber
RealNumber)(subclass-of NegativeRealNumber
RealNumber)(subclass-of Integer RationalNumber)(subclass-of EvenInteger Integer)(subclass-of OddInteger Integer)(subclass-of NaturalNumber Integer)(subclass-of NonnegativeInteger Integer)(subclass-of NegativeInteger Integer)(subclass-of PositiveInteger Integer)(subclass-of PositiveNumber Number)(subclass-of NegativeNumber Number)(subclass-of ComplexNumber Number)In the second case, the new concept/axiom
was judged to be out of place in a schema that
we hope will have broad application andacceptance. This sort of judgement is of course
somewhat subjective, but this shouldn’t diminish
the importance of ontology merging/alignment
for the same reason that it doesn’t dull thesignificance of legal decisions. An example ofthe second case is the concept of “Mediating
Entity”, which appears in John Sowa’s upperlevel ontology. This concept is derived from thework of the philosopher Charles S. Peirce(Peirce, 1932), and it corresponds to his notion
of “Thirdness”, i.e. anything that brings twoother things into some sort of relationship.
Although this notion may be philosophicallyindispensable, it was difficult to justify its
inclusion in an engineering-oriented context,
and, for this reason, it was removed from themerged ontology.
In the third case, there is perfect overlap
between an element of the merged ontology andthe concept/axiom to be mapped - the terms may
differ but the new concept has the same semantic
content as a concept already in the merged
ontology or, with respect to axioms, there is alogical equivalence between the new axiom andan existing axiom. An example of this sort ofcase occurs with respect to the various
mereotopological theories that were referenced
in the merged ontology. Some philosophers use'part-of' as the primitive notion to frame theiraxioms, some use 'overlaps', and still others use
some notion of 'connection'. However, thesenotions are interdefinable, and thus axioms thatare framed in terms of one concept can be easilytranslated into the terms of the other concepts.
The final case that we encountered in merging
the tip of the ontology with the lower-level
content is a partial overlap in meaning between
the new content and existing concepts or axioms
in the merged ontology. An example is theoverlap between the concepts of ‘Class’ and‘Set’. The concept of ‘Class’ occurs in John
Sowa’s ontology, where it refers to a collectionof items that form something like a natural kind.
The concept of ‘Set’, on the other hand, occurs
in the set theory ontology available on the
Ontolingua server. To a large extent, these twoconcepts behave in the same way. Things can beelements of classes and sets, subclasses andsubsets are well defined, and classes and sets canbe partitioned, disjoint, etc. Despite this strong
similarity between the two concepts, it was
decided that both of them should be maintainedin the merged ontology, because there is a
crucial difference in meaning here. While anycollection of items qualifies as a set, a classincludes only items that share a property or set ofproperties.

Outstanding Challenges
The major outstanding problem with regard to
the merged ontology is inconsistency between
engineering-relevant chunks of theoreticalcontent. The clearest example of this sort ofclash thus far has been a discussion, on the SUO
mailing list, about whether the ontology shouldhave a 3D orientation or a 4D orientation or, as
the debate has also been framed, an endurantist
perspective or a perdurantist perspective. The
3D orientation assumes that there is a basic,
categorial distinction between objects andprocesses, while the 4D orientation does not. The
3D orientation posits that objects, unlike
processes, are completely present at any momentof their existence, while a 4D orientation regards
everything as a space-time worm (or a slice ofsuch a worm). On the latter view, paradigmatic
processes and objects are merely opposite ends
of a continuum of spatio-temporal phenomena.
Another example of substantive disagreement
concerns the representation of time.
Asmentioned above, we have aligned James Allen’stemporal axioms with the merged ontology.
Although these axioms have a clear engineering
application, they do assume an interval-based
representation of time, which is inconsistent witha point-based representation. Since this latter
representation is more applicable in somecontexts than the interval-based representation,
we apparently need some formal mechanism foraccommodating theoretical inconsistency.
The question, then, is how do we handle cases
like these, given that our goal is to construct asingle, consistent, and comprehensive ontology.
It will be unfortunate if we cannot reach thisgoal, but perhaps it is unattainable. If it isunattainable, then perhaps the best we can do is
to make clear the various representational
choices and bundle them up in consistent andindependent packages and, where possible, statemappings between corresponding packages.
One elegant suggestion was (Menzel) to create alattice from these various modules. The top
node of this lattice would be the merged
ontology, and each level below the top node
would represent inconsistent formal theories that
could be used in conjunction with the merged
ontology. Thus, each path through the latticefrom the top node to a lower-level node would
result in a formal theory that is self-consistent,
butinconsistentwithvariousotherrepresentational choices provided by the
ontological lattice.
Another challenge is in making the contents of
the ontology accessible and intelligible to thewidest audience. Considerable work has been
done on tools for browsing ontologies (Swartout
et al 1996), (Farquhar et al, 1996) and we expect
to make the merged ontology available in a morefriendly form that its current incarnation as avery long text file of KIF expressions. We alsoplan to release versions in different languages
which can be generated automatically from the
KIF. These additional languages will include
DAML (DAML, 2001), XML, and possibly
ACE (Fuchs & Schwitter, 1996).

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