www.elsevier.com/locate/eswa

Expert Systems with Applications 34 (2008) 541–550

Expert Systemswith Applications

Multi-perspective ontologies: Resolving common ontologydevelopment problems

John Kingston *

Joseph Bell Centre for Forensic Statistics and Legal Reasoning, University of Edinburgh, South Bridge,

Edinburgh, Lothian EH8 9YL, United Kingdom

Abstract

Ontology – the theory of objects and their relationships – has become a hot topic in recent years, with its use for indexing knowledgeand structuring knowledge in knowledge management and knowledge engineering. However, attempts to develop ontologies suffer froma number of problems in practical situations. This paper outlines an approach to ontology development based on multi-perspective mod-

elling – that is, dividing a single ontology into multiple ontologies according to the type of knowledge that is addressed. It is argued thatthis approach is able to resolve some of the common problems that arise in ontology development.� 2006 Published by Elsevier Ltd.

Keywords: Ontology; Knowledge management; Knowledge engineering

1. Introduction

Ontology – the theory of objects and their relationships– has become a hot topic in knowledge engineering andknowledge management. One reason is that organisationsthat have entered into knowledge management havediscovered the need to classify their knowledge in a mannerthat is both accessible to users and robust enough to repre-sent different types of knowledge in a coherent manner.Another reason is that knowledge engineers have longdesired that a knowledge base developed for one applica-tion should be standardised so that it is re-usable inanother application. And thirdly, object-oriented softwaredevelopment requires an understanding of ontologicalprinciples: authors in the field have claimed that ‘‘a clearunderstanding of ontology helps to avoid the introduction

0957-4174/$ - see front matter � 2006 Published by Elsevier Ltd.

doi:10.1016/j.eswa.2006.09.040

* Tel.: +44 131 650 9704.E-mail address: J.Kingston@ed.ac.ukURL: www.josephbell.org.

of accidental, as opposed to essential, objects’’, and ‘‘theexploding interest, both theoretical and practical, in thedevelopment of object-oriented languages. . . has ledcomputer science squarely into the business of doingresearch in ontology’’ (Graham, 1991; Smith, 1996).

However, ontologies – classifications or indices ofobjects and their relationships, often restricted to a partic-ular domain of knowledge – suffer from a number of prob-lems in practical situations. These problems do not preventusable ontologies from being developed, but they do makeit difficult to develop ontologies in a standardised manner;this reduces the extensibility and reusability of ontologies,and makes it particularly difficult to merge ontologies, evenif they address the same topics.

This paper outlines an approach to ontology develop-ment based on multi-perspective modelling – that is, divid-ing a single ontology into multiple sub-ontologiesaccording to the type of knowledge that is addressed.The type of knowledge is determined by the relation(s)used to link concepts. It is argued that this multi-perspec-tive approach is not only beneficial for correct and com-plete ontological modelling, but is also able to resolve

1 The ‘categories’ of a determinable concept are based on arbitrarilydefined points on one or more continuous dimensions. In the case ofcolour, there are three continua, consisting of levels of red, green and bluelight.

542 J. Kingston / Expert Systems with Applications 34 (2008) 541–550

some of the common problems that arise in ontologydevelopment.

2. Problems with ontology development

So what are the main problems that arise when develop-ing ontologies? A selection of problems is outlined below,drawn from Corazzon (2000) and other sources.

2.1. IS-A overloading

‘‘IS-A overloading’’ (Guarino & Giaretta, 1995) is theuse of the subclass–superclass relation (which is commonlyreferred to as is-a) to carry multiple meanings in a singleontology. Five such misuses are identified from a surveyof popular ontologies: confusion of senses (for example,in the Mikrokosmos ontology, a ‘window’ both is-a artifactand is-a place); reduction of sense (e.g. in Pangloss, aperson is-a physical object and is-a living thing, but asubclass–class link already exists between living thingsand physical objects); overgeneralization (a place is-a phys-ical object in both Mikrokosmos and WordNet); suspecttype-to-role links (in WordNet, a person is both a livingthing and a causal agent – the latter is a role, not a type);and confusion of taxonomic roles (both Pangloss and Pen-man offer a taxonomy of qualities, but qualities are betterrepresented as properties rather than as concepts). Whilethese misuses may reflect accepted practice in naturallanguage (for example, the term ‘window’ can refer eitherto a single window pane, a connected set of multiplepanes, or the space those panes occupy), they can causegreat difficulties in accurate ontological classification, andthey make logical inference across multiple ontologies verydifficult.

2.2. Inaccurate expert responses

Another problem that arises from loose use of naturallanguage is that ontological questions supplied to domainexperts may be answered incorrectly, either through theexperts misunderstanding the question or misunderstand-ing the ontological implications of their answer. For exam-ple, experts who are asked, ‘‘Please give a subclass of X i.e.tell me something that is a X’’ may answer with a super-class of X; they may provide a member of the class X ratherthan a subclass; or they may supply a concept that isrelated to X by some relation apart from is-a. This is a par-ticular problem when discussing a process-orienteddomain; for example, engineers who maintained foodprocessing equipment were asked to give subclasses of‘‘overheating faults’’, and answers given included ‘‘electri-cal faults’’ (a superclass), ‘‘faulty thermocouples’’ (a mem-ber of the class), or ‘‘food discoloration’’ (a consequence ofoverheating – this was a particularly common error). So thedifficulty for the ontology engineer lies in identifying onto-logical relations that accurately express the answers theexperts give, even when is-a relations are requested.

2.3. Levels of detail and inferencing bias

Inferencing bias occurs because it is not practical todefine all ontologies at a universally accepted ‘‘primitive’’level. This restriction exists because the only level that isuniversally accepted as primitive consists of atomic ormolecular descriptions of objects, and if an ontology needsto define a complex object (such as a person) in terms oftheir cellular structure and the atoms that make up thosecells, that ontology will become so large as to be unusable.For practical purposes, a higher level of abstraction mustbe used – i.e. low level concepts (such as atoms) are treatedas properties of higher level concepts (such as cells). Butmany ontologies choose to jump several levels of abstrac-tion, ignoring both atoms and cells, and even treatingcell-based structures (e.g. skin) as properties of conceptssuch as ‘‘a hand’’, rather than concepts in their ownright.

The difficulty for ontological re-usability lies in the factthat the level of abstraction chosen is usually determinedby the problem being tackled, rather than by inherenttask-independent properties of objects. So different ontolo-gies of the same domain may be incompatible, because ofovergeneralization, or because a key concept in one ontol-ogy may be a mere property in another.

As an example, let us consider ontological definitions ofcolour. In this case, there is probably general agreementthat a ‘primitive’ definition of colour should consist ofthe intensity of light of different wavelengths that isreflected or emitted by the ‘coloured’ objects. However,while an ontology for physicists may require this level ofdetail, photographers and artists only need an ontologythat specifies colour as a concept with properties such ashue, brightness and intensity, and a car salesman proba-bly views colour merely as a property of the cars hesells.

These varying levels of detail would be less of a prob-lem if there was agreement on how to combine ‘primitive’definitions into higher level concepts. This is definitelynot the case for colour, however; in the GRASP projectwhich aimed to build an ontology of art objects for Inter-pol (GRASP, 1999), the researchers discovered that col-our terms used to describe paintings are entirelydifferent to the colour terms used to describe ceramics.While colour is one of the more difficult concepts toobtain agreement on, being a determinable rather thana determinate concept,1 little has been published on theproblem of combining lower level ontological definitionsinto higher level concepts. One approach that has beensuggested, by Guarino (1997), is discussed later in thispaper.

J. Kingston / Expert Systems with Applications 34 (2008) 541–550 543

2.4. Dependence relations

As stated above, experts who are asked to give sub-classes of concept X will often answer with concept Y,which is related to X in some other way. Typically, the rea-son is that concept Y depends on X in some fashion; forexample, food discoloration has a causal dependence onoverheating faults. Other examples of dependence wouldinclude existence (e.g. a publication depends on the exis-tence of the published document); precedence (e.g. heatmust be applied before overheating can occur); and mereo-nomic relationships (the function of many machines, andperhaps the existence of the machine itself, depend on thepresence and interaction of certain components). Corazzon(2000) identifies many kinds of dependence relations,including dependencies between levels of reality, withinand between wholes, parts, and their environment, andbetween particulars and determinations. It requires carefulthought to model these relationships clearly in any ontol-ogy, and many different types of relationship may beneeded for accurate representation.

2.5. Particulars

The is-a relationship is able to define particulars (i.e.individuals, or individual objects) well, but struggles withother types of concept, such as Processes, Groups, oruncountables (substances described as ‘‘an amount of’’rather than ‘‘a collection of’’ – water is a good example).But the need to represent these concepts in ontologies hasbeen clearly identified (see e.g. Lenat, 1998).

So what is to be done about these issues? The next sec-tion will describe an approach to ontology developmentthat can address at least some of these problems.

Fig. 1. The Zachman framework for Information Systems Architecture (from

3. Multi-perspective ontologies

Imagine that you have been asked to rent a DVD towatch that will be appropriate for yourself and five friends.When asked for their preferences, the first friend wants tosee action or science fiction; the second wants innovativespecial effects; the third wants something that was notfilmed in the USA; the fourth asks for a film made fairlyrecently; and the fifth wants a film that features her favour-ite actor, who turns out to be Keanu Reeves. You havebeen given a multi-perspective classification problem. Eachof your friends classifies films in a different way; one careswhat genre of film it is, another how the film was made,another where it was made, the fourth when it was made,and the fifth who is in it. As you stroll away from the shopwith a copy of The Matrix in your hand, it might occur toyou that these different classifications map neatly to the dif-ferent perspectives on information and knowledge pro-posed in another matrix – the Zachman framework forInformation Systems Architecture (see Fig. 1).

3.1. The Zachman framework

The Zachman framework, proposed by Zachman(1987), suggests that a full description of an informationsystem requires six different perspectives on representation– who, what, how, when, where and why – at up to six dif-ferent levels of detail, ranging from organisational pro-cesses through system design to final implementation.Kingston (2005) shows how the framework can be appliedto knowledge assets as well as to information assets, thusproviding support to knowledge management and knowl-edge engineering. Since ontologies describe knowledge,and are often used to support knowledge management or

Hokel (2006), or see www.zifa.com/framework.html, Zachman, 1987).

Table 1Relations typically associated with particular perspectives

WHAT is-a (taxonomy), part-of (mereonomy)HOW achieves (goal), transforms, creates/destroys, any term

544 J. Kingston / Expert Systems with Applications 34 (2008) 541–550

knowledge engineering, then it is worth investigatingwhether the structure provided by the Zachman frameworkmight be beneficially applied to the structuring ofontologies.

reflecting a specific action (selects, matches, etc.)WHO plays-the-role-of, responsible-for, has-rights-to

WHEN precedes/follows, possible with time intervals specifiedWHERE location, connected-to, or terms reflecting geographical

relationships (close-to, south-of, etc.)WHY causes, justifies

3.1.1. The Zachman framework: perspectives

The thesis that underlies the Zachman framework is thatinformation or knowledge assets are too rich to be repre-sented by a single term within a single classification scheme.Instead, understanding these assets fully requires knowl-edge of their content, their procedures, their provenance,related information, and so on. As a result, a multi-per-spective representation2 is proposed, with each perspectivebeing represented by the columns of the Zachmanframework.3

One of the key goals of multi-perspective modelling is tosimplify ontological diagrams by removing much of theneed for multiple inheritance or other multiple linkingwithin an ontology. To facilitate this, it is recommendedthat individual sub-ontologies are devised that use only asingle relation – an idea drawn from the ‘‘domain models’’proposed by the CommonKADS methodology for knowl-edge engineering & knowledge management (Schreiberet al., 2000). See Fig. 3 for some examples of domain mod-els based on separate relations.

How can an ontology engineer decide which perspectivea concept belongs to? Normally, the relation(s) which linkto that concept should be the guiding factor. Relations thatare typically associated with each of Zachman’s six per-spectives are suggested in Table 1. If difficulties arise, how-ever, a formal set of meta-properties has been proposed(Welty & Guarino, 2001, reviewed in GRASP, 1999), deter-mining which meta-properties hold of a concept can help todetermine its perspective. For example, roles can be identi-fied by their lack of rigidity (i.e. they do not persist for theentire lifetime of the object that plays that role) and bytheir dependence (i.e. their existence is dependent on theexistence of at least one other concept). Roles shouldappear in the ‘who’ perspective of a multi-perspective

2 Guarino (1997) highlights the fact that the term ‘‘multi-perspective’’has two different interpretations in previously published literature – andboth interpretations have claimed support from the Zachman framework.The ‘‘negotiation’’ approach considers that the rows of the frameworkrepresent (possibly conflicting) views of different professions on anartifact. The ‘‘crystallography’’ approach considers the columns of theframework to represent different viewpoints on the same artifact, with theaim of building a complete picture of its internal structure. In this paper,the latter interpretation is followed.

3 The need for multiple perspectives has been backed up in practice bycompanies who have tried to develop knowledge management systems todistribute knowledge within their organisation, only to find that theknowledge is not trusted, and therefore not used, unless the person whocreated the knowledge is identified. It’s also supported by the frequentrequirement for knowledge based systems to provide explanations of theirreasoning in order to justify their procedural knowledge; by the need forcause-and-effect reasoning to solve some problems where recognisedprocedures fail; and by the use of multiple inheritance within existingontologies.

ontology. Other meta-properties have also been proposedwhich can be used to draw more detailed distinctions, ifrequired.

3.1.2. The Zachman framework: levels of detailAs for the rows of the Zachman framework, these are

intended to represent successively greater levels of detailon the artifact being developed. One concept at any levelcan be decomposed to form the basis for an entire (sin-gle-perspective) model at the next level down.4 For exam-ple, a single activity in a process at one level may beexpanded into a process diagram or flowchart at the nextlevel down.

The nature of the decomposition varies according to theperspective being considered. For the HOW perspective,the decomposition will represent a process that achievesthe same goals as the upper level process step; that is, theinputs and outputs of the process match those of the upperlevel process step. For the WHERE perspective, thedecomposition will describe the same ‘location’ that theupper level concept describes, but in greater detail – thisis analogous possessing a small-scale map and severallarge-scale maps of the same area. For the WHAT perspec-tive, the most likely decomposition is a taxonomic break-down – subclasses and perhaps instances of a superclass– though it may also be a mereonomic breakdown (compo-nents of an assembly) instead. The WHO and WHEN per-spectives behave in a similar fashion. In short, the linkbetween two levels can often be described by the relationssuggested in Table 1.

The WHY perspective can decompose in two differentfashions, however. One decomposition would consist of aprocess or method that achieves a particular goal, similarto the HOW perspective. This would appear in the WHYperspective because the purpose of that process of methodis to achieve a particular goal. The other possible break-down consists of justifications or supporting arguments;these may include publishes regulations or other conceptsexternal to the artifact under discussion. The Zachman

4 While it can be argued that these rows actually represent three levels ofdetail on two different dimensions – the upper three levels representinganalysis of an organisation and its knowledge, while the lower three levelsrepresent and increasingly detailed design specification of a system thatembodies that knowledge – the principle is established that knowledgeneeds to be represented at different levels of detail in order to understand itfully.

J. Kingston / Expert Systems with Applications 34 (2008) 541–550 545

framework seems to assume the first of these two break-downs, but knowledge acquisition for the WHY perspec-tive may well result in the second type of knowledgebeing produced. So it should be borne in mind that theWHY perspective may decompose in a different fashionto the other perspectives.

Fig. 3. Two alternative ontologies for ‘strut’.

4. Multi-perspective ontologies – examples

4.1. Example 1: scientific knowledge management

A worked example of multi-perspective ontologies isdrawn from an upper level ontology of ‘‘Scientific Knowl-edge Management’’ (i.e. academics, their projects and pub-lications), whose development was discussed in Kingston(2002). The ontology in its current form is shown in Fig. 2.

This ontology was designed based on several principles,but the main ones were to fit in with two existing upperlevel ontologies: one from the Open University, whosetop level categories were Tangible Thing, Intangible Thing,and Temporal Thing, and one from the OntoClean ontol-ogy, the top level of which consisted of six categories:Abstraction, Quality, Aggregate, Feature, Object, andEvent. It was decided that the OU’s three categories shouldrepresent the top level of the ontology and the Ontocleancategories should represent the second level. The concepts

Fig. 2. Current ontology of Scientific Knowledge Management.

that had to be classified, and their classifications in theabove schemes, are as follows:

• Documents, publications, etc. – Objects (TangibleThing);

• Conferences, workshops, seminars – Events (TemporalThing);

• Research groups, universities, funding bodies – Organi-sations (Tangible Thing);

• Students, professors, supervisors – People (TangibleThing);

• Research areas (Generic Areas of Interest) – Abstrac-tions (Intangible Thing);

• Projects, grants – Tasks (Temporal Thing).

These top level categories are reasonably well separatedfrom a multi-perspective viewpoint. Objects address theWHAT perspective, People and Organisations addressthe WHO perspective, and Tasks (which are a subcategoryof Event, not shown in Fig. 2 for reasons of space) addressthe HOW perspective. Much of this separation is due to thecareful design of the OntoClean ontology. The WHEN andWHERE perspectives are also addressed to some extent, bythe categories of ‘‘Temporal Things’’ and ‘‘Aggregates’’,respectively.

However, the ontology of Scientific Knowledge Man-agement is not perfect, because it attempts to use taxo-nomic links to represent relationships that are not trulytaxonomic. For example, Publications are not really asubclass (i.e. in a taxonomic WHAT relationship) to Doc-uments but are dependent on (the existence of) a document.This could be resolved by developing a second ontology,based on a WHERE perspective, which states where docu-ments have been published into the public domain. Thisallows the dependence to be expressed correctly, as wellas allowing for easy representation of multiple publicationsof the same document. Similarly, Methods are taxonomi-cally classified as Abstractions but are more commonlythought of in terms of the goal that they can achieve,and method–goal relationships could be captured in anontology based on the WHY perspective.

An example of IS-A overloading can also be seen in themultiple inheritance links of Person and Organization as

Fig. 4. Representing ‘strut’ with two multi-perspective ontology models.

546 J. Kingston / Expert Systems with Applications 34 (2008) 541–550

Guarino highlighted in his discussion on IS-A overloading,these concepts actually play the role of agents (a WHO link)rather than being a kind of agent (a WHAT link).

This ontology would therefore be better if divided intoat least four different perspectives. The majority of thedetail would remain in the taxonomy (the WHAT perspec-tive), but the existence of the other perspectives shouldprompt the knowledge engineer to acquire more knowl-edge. Do Recorded_Audio or Recorded_Video objectshave publications in a similar fashion to Documents? IfMethods are linked to Goals in a WHY ontology, is therea need to represent Actions that are similarly linked toStates in a HOW ontology? Can all LifeForms or Social-Groups play the role of a LegalAgent?

The level of detail of an upper ontology should alwayscorrespond to the uppermost (‘scope’) level of the Zach-man framework. Following Cook’s heuristic, this doesseem to be the case; even the lowest levels of the ontologyabove can be seen to be applicable to multiple business pro-cesses in most organisations.

4.2. Example 2: struts example

Swartout, Patil, Knight, and Russ (1996) highlight aproblem with ontological classification in which the con-cept ‘strut’ can be linked to the top level concept ‘Thing’in two different ways. In one way, ‘strut’ is a subclass of‘support’, which is a subclass of ‘‘decomposable object’’,which is a subclass of ‘Thing’; in the other, ‘strut’ is a sub-class of both ‘‘durable good’’ and ‘‘load bearing member’’,which are both subclasses of ‘‘physical object’’, which is asubclass of ‘Thing’.

From a multi-perspective viewpoint, there are severaldifficulties with these apparently simple ontologies. Web-ster’s dictionary defines ‘strut’ as ‘‘a structural piecedesigned to resist pressure in the direction of its length’’,while a ‘support’ is simply ‘‘something that carries outthe act of supporting’’. So ‘strut’ is indeed a subclass of‘support’, since they are both playing a similar role – thatof supporting some part of the building. However, thedirect is-a link between these two concepts is overgenera-lised; ‘load bearing member’ should lie between them in ataxonomy, because buildings include not only struts butalso rafters, purlins and other load bearing members, andall of these are supporting the building in one way oranother.5

We can also see that a strut is a physical object – usuallya beam of wood or metal – that plays the role of a structuralcomponent in a building. This implies that the ontologicallink between ‘strut’ and ‘durable good’ is an example of asuspect type-to-role link. It would be better to link ‘beam’

5 Some may suggest that ‘load bearing member’ and ‘support’ are thesame concept, but this is not the case – something can be a ‘support’without necessarily being a ‘member’ of a building, or even without beingbe a physical object at all, for gravity, magnetism or other forces can act assupports.

or ‘member’ to ‘durable good’ instead – noting that, sincebeams are not always classed as ‘durable goods’,6 this linkis also a plays the role of link rather than an is-a link.

The last point in this critique is the concept of ‘‘decom-posable object’’, which is criticised by Guarino and Gia-retta (1995) due to confusion of senses; they believe thatthis should only ever be a property rather than a conceptin a taxonomy.

Having deconstructed this ontology, we are left with athree-level taxonomy (Zachman’s WHAT perspective)and a two-level multiple-inheriting model based on therelation plays the role of (part of the WHO perspective) –see Fig. 4. Given that the semantics of plays the role of rec-ognise that an object may play different roles at differenttimes, the multiple inheritance is considered to be accept-able. As for the appropriate levels of detail in the Zachmanframework, Cook (1996) proposes a rule of thumb that theHOW perspective of the top three levels should referrespectively to all processes within an organisation, a singlebusiness process, and a task within that business process.The business process of ‘constructing a building’ includestasks such as ‘designing the building’, ‘select componentsthat match the design’ and ‘assemble the components’.The concepts described in this ontology are relevant to sev-eral of these tasks, which raises the issue of whether theconcepts ‘belong’ to the tasks or to the higher level businessprocess. After some consideration, it seems that the formermakes the most sense, so Cook’s rule of thumb suggeststhat these concepts should be applied at the System levelof detail.

4.3. Example 3: the ACM classification scheme

The top level of the ACM classification scheme for com-puter-related topics is shown in Table 2. An extension tothe Artificial Intelligence classification has been designedby Scientific Datalink, and part of this extended classifica-tion is shown in Table 3.

6 Beams are only goods (Webster: ‘‘something that has economic utilityor satisfies an economic want’’) as long as they are in demand, or as longas they are offered for sale.

Table 2Top level of the 1998 ACM classification scheme

A General literatureB HardwareC Computer systems organisationD SoftwareE DataF Theory of computationG Mathematics of computingH Information systemsI Computing methodologiesJ Computer applicationsK Computer milieux (philosophy, legislation, administration)

J. Kingston / Expert Systems with Applications 34 (2008) 541–550 547

The ACM classification covers several of the multipleperspectives. The perspectives covered include WHAT isneeded for a computer system (hardware and software),HOW to build a computer system (techniques), andWHY systems are built (computing milieux). The catego-

Table 3Part of the Scientific Datalink AI classification scheme

Subcategories

I.2.1. Applications and expert systems

1.0 Cartography1.1 Games Chess, checkers, ba1.2 Industrial applications Automatic assembly1.3 Law1.4 Medicine and science Medical application1.5 Natural language interfaces1.6 Office automation1.7 Military applications Autonomous vehicl

communication1.8 Business and financial Tax, investment, fin1.9 Natural language processing applications1.10 Mathematical aids1.11 Education Tutoring systems, in

curriculum design1.12 Library applications1.13 Engineering automation Computer system d1.14 System troubleshooting1.15 Expert systems Expert system langu

plausible reasoning,systems based on simexpert systems

1.16 Prosthetics1.17 Aviation applications1.18 Applications, other

I.2.4 Knowledge representation

4.0 Frames and scripts Defaults, stereotypedriven systems, inhe

4.1 Predicate logic First order predicat4.2 Relational systems Relational data bas4.3 Representation languages4.4 Representations (procedural/rule-based) Production rule sys4.5 Semantic networks4.6 Connectionist systems4.7 Multiple agent/actor systems4.8 Constraints4.9 Discrimination trees and networks4.10 Belief models4.11 Representation of the physical world4.12 Rep. of natural language semantics

ries also cover different levels of abstraction: some catego-ries consider the contents of the computer itself (hardware,software, computer systems organisation, data, informa-tion systems) while other categories consider the computeras a single concept in the context of applications (comput-ing methodologies, computer applications, computing mili-eux). There is also a third viewpoint on computers to befound in the two theoretical categories (Theory of Compu-

tation and Mathematics of Computing), which provide thefoundational techniques for computer systems organisa-tion, data and information systems.

The Scientific Datalink extension also uses a formulawhere formalisms/resources (WHAT knowledge) are mixedwith methods/techniques (HOW knowledge) to generatesubcategories. For example, most of the subcategories ofKnowledge Representation are concerned with differentknowledge representation formalisms – the WHAT ofknowledge representation – but two (Representation of

ckgammon, bidding, wagering, war, other, parts handling, inspection, welding, planning for production, inventory

s, chemical applications, biological applications, geological applications

es, integration of information, decisions aids, target tracking,

ancial planning, info storage & retrieval

telligent computer-aided instruction, aids to learning programming,

esign, VLSI design aids, CAD/CAM, programming aids

ages and aids for building expert systems, acquisition of expert knowledge,representation of expert knowledge, generation of explanations, expertulation and deep models, user interfaces for expert systems, validation of

s and prototypes, generation of expectations, frame languages, frame-ritance hierarchy

e calculus, Skolem functions, second order logic, modal logics, fuzzy logices, associative memory

tems, knowledge bases

548 J. Kingston / Expert Systems with Applications 34 (2008) 541–550

the Physical World and Representation of Natural Language

Semantics) are primarily concerned with knowledge repre-sentation as a task rather than a formalism – i.e. withHOW rather than WHAT. Similarly, most of the subcate-gories of Applications and Expert Systems are concernedwith different domains in which expert systems have beenapplied (similar to the ACM’s taxonomic breakdown ofComputer Systems Applications into different disciplines),but I.2.1.15 (‘‘Expert Systems’’) and I.2.1.5 (‘‘Natural Lan-guage Interfaces’’) are more concerned with techniques forexpert system construction, and I.2.1.14 (‘‘System Trouble-shooting’’) focuses on a particular task rather than on adomain. And one of the subcategories of ‘‘Problem Solv-ing, Control Methods and Search’’ – the category of ‘‘PlanExecution, Formation & Generation’’ – is arguably con-cerned primarily with WHEN knowledge.

In short, the ACM classification scheme and theScientific Datalink extension would be better structuredif they were split into at least two ontologies, one reflect-ing the WHAT perspective (i.e. a taxonomy) and theother representing the HOW perspective (i.e. methodsand techniques). This would allow more completerepresentation of (e.g.) goals for knowledge representation,techniques for expert system construction, and task-focused categories. Further perspectives might also provebeneficial; for example, the top level ACM category ofHardware is broken down by computer components (i.e.a part-of decomposition), and this might be broken offto form a separate ontology rather than being linkedwith the is-a links of the remainder of the WHATperspective.

The ACM and Scientific Datalink ontologies exist atseveral levels of detail, and so we must consider whetherour principles of decomposition or dependence can beapplied in order to resolve any other ontological issues. Itturns out that this is a difficult task to carry out; tryingto determine whether ‘‘Data Communications Devices’’and ‘‘Input/Output Devices’’ (two levels down from Hard-ware) should be at the same level as ‘‘Office Automation’’and ‘‘Information Systems Applications’’ (two levels downfrom Information Systems) is a near-impossible task. Per-haps the main message to draw from this ontology, there-fore, is that it is wise to consider levels of detail after

the ontology has been partitioned into Zachman’sperspectives.

5. Multi-perspective ontologies: dealing with ontologyproblems

In this section, the ontology problems listed above willbe considered in turn, with indications on how or whethermulti-perspective ontologies can resolve the problem.

5.1. IS-A overloading

Five types of ontology problem due to IS-A overloadingwere identified by Guarino and Giarretta. Multi-perspec-

tive ontologies can deal with two of these directly: suspecttype-to-role links should be eliminated if a WHO ontologybased on the relation plays the role of is developed; andconfusion of taxonomic roles should also be eliminated(since qualities do not appear in any of the recommendedperspectives it is unlikely that a taxonomy of them willbe developed; and the concept of a ‘decomposable object’can be better represented by a mereonomic diagram thatshows the relevant part-of relations than by a taxonomy).The remaining problems are due to weak considerationof the ontological links that can be introduced into anontology; multi-perspective ontologies do not deal withthis problem directly, but by breaking down ontologiesinto a number of single-perspective ontologies, they shouldmake it easier to identify the implications of assigningcertain relationships. Indeed, by separating different rela-tions into different diagrams, it becomes much simpler toidentify concepts that should have a particular relationbut do not, or sets of concepts that have missing members,thus assisting in the creation of complete and correctontologies.

5.2. Inaccurate expert responses

Multi-perspective ontologies can be of great help indealing with inaccurate expert responses, particularly ifthey are combined with a software tool that allows swiftrepresentation and display of acquired knowledge. If, forexample, a knowledge engineer asks ‘‘what causes A?’’and the expert inaccurately replies ‘‘B’’ when in fact Acauses B, an ontological diagram based on a single perspec-tive, or (even more narrowly) on a single relation can becreated or opened, the relationship can be entered, andthe results displayed to the expert. Experience suggests thatdisplaying a relationship in the context of other knowledgeof the same type makes it much easier for people who arenot experts on ontology to identify their errors in ontolog-ical understanding. However, if the diagram is too crowded(as many semantic networks, which display fragments ofseveral different perspectives, are), these contextual benefitsare often lost. Multi-perspective ontologies are designed toavoid this problem.

5.3. Levels of detail and inferencing bias

Multi-perspective ontologies do not provide a solutionto the problems of inferencing bias, but they (or more pre-cisely, the levels of detail represented by the Zachmanframework) do provide a conceptual framework withinwhich these problems can be discussed and analysed.

Given this potential cascade of diagrams that mightresult from applying these levels of detail fully, some guid-ance on deciding what level a particular concept should berepresented at might be helpful. Unfortunately, little guid-ance is available. Cook’s rule of thumb, quoted above, ishelpful but the distinctions it required are still left to theintuition of the modeller.

J. Kingston / Expert Systems with Applications 34 (2008) 541–550 549

Some guidance may be derived from the ‘‘ontologicallevels’’ proposed by Guarino and Giaretta (1995), summa-rised in Guarino (1997). Guarino proposes that ontologicalconcepts can be assigned to one of nine different levels ofdetail, ranging from the social level down to the atomiclevel. The levels are each dependent on the level below,and are primarily individuated by topological connected-ness. For example, the intentional level (‘‘e.g. a person orrobot, individuated by persistence of intentional behav-iour’’) is dependent on the next level down, the biologicallevel (‘‘e.g. a human body, individuated by the persistenceof life’’).7 Taking the example given above of defining col-ours, wavelengths of light would correspond to the‘‘atomic’’ level, with coloured light probably correspondingto the next level up (the ‘‘static’’ level, defined as a mere-ological sum of atoms with properties that are persistentlythe same), while a coloured car probably belongs some-where between the third (mereological) and sixth (func-tional) levels.

By making decomposition reliant on dependence andconnectedness, Guarino’s approach brings the problem ofdetermining levels of detail within the scope of the meta-property analysis that was suggested for determiningperspectives, which holds out the promise that in future,formal or semi-formal analysis may be used to determinethe appropriate level of detail for concepts.

5.4. Dependence relations

The problem of representing dependence relations isgreatly simplified by using multi-perspective ontologies.The reason is that the most common dependence relation-ships can be expressed as other relations that fit into themulti-perspective framework. One example appears inthe worked example of scientific knowledge management:the existence of a Publication depends on the existence ofa Document, but can be re-expressed by saying that a Pub-lication gives a (public) location of a document – a relationthat can be found in an ontology based on the WHEREperspective. Similarly, the existence of a car can be saidto depend on the continued existence of (most of) its parts,but these can be modelled using part-of relationships; andthe continued existence of a procedure depends (or oughtto depend) on there being continued justification for itbeing performed, which can be modelled in a WHYperspective.

7 It is far from clear whether the ‘‘best’’ way of assigning levels within anontology is to follow the principle of decomposition used by the Zachmanframework or to use Guarino’s dependence-based approach. Furtherresearch is needed in this area. As a pragmatic guide, decompositionshould probably be used where possible, since this fits more closely withthe class-subclass distinctions of the levels of a typical taxonomy. In someperspectives, though, such as the WHY perspective where arguments andjustifications are not some much composed of sub-arguments as supported(or opposed) by sub-arguments, it may be better to assign levels to anontology according to the principle of dependence.

5.5. Particulars

The representation of processes and groups fits very wellwith the philosophy of multi-perspective modelling. Forprocesses, it is to be expected that the majority of conceptsin a HOW ontology will be processes or events rather thanindividuals. Similarly, a WHO ontology should containsome details of organisational structures, thus representinglinks between individuals and groups. The distinctionbetween uncountables and discrete objects is a high levelconcept better represented using definitional properties ingeneral ontologies rather than with relations in domain-specific ontologies, and therefore this issue is not addressedby the use of multi-perspective ontologies – though Weltyand Guarino’s formal meta-properties (Welty & Guarino,2001) should be able to support this distinction.

6. Discussion

It seems that the ability to search for a concept or cate-gory by more than one route is highly prized by users, andmulti-perspective ontologies are ideal for supporting this.We have seen that the creation of multi-perspective ontol-ogies is capable of resolving several of the most commonproblems that arise in ontology development.

How, then, should multi-perspective principles beapplied to the development of future ontologies? A recom-mended method is as follows:

• Carry out some bottom-up terminological analysis ofthe domain to identify concepts and, more importantly,the relationships between them. This can be done man-ually or semi-automatically using a lexical tagger. Analternative approach would be to carry out near-auto-matic lexical analysis that has been ‘bootstrapped’ usingan ontology of the expected relations (in the mannerdescribed in Maedche & Neumann, 2002) and then usea lexical tagger on those sections of the text that hadnot yet yielded much ontological content. The predic-tion of the multi-perspective ontology approachdescribed in this paper is that any relation that appearsin an ontology will be assignable to one of the six per-spectives, often because they are derived from relationsthat appear in Table 1. For example, the relationshipTASK is-a-priority-for AGENT implies that in any planthat is generated for this agent, performing this taskshould precede other tasks; and in a typical diagnostictask, the fact that TEST indicates FAULT actuallyimplies that some OBSERVABLE VALUE is caused

by that FAULT, and the TEST measures the OBSER-VABLE VALUE.

• Sort them so that all relations of the same type appeartogether (each set of such relationships is termed a‘‘domain model’’ by the CommonKADS methodology).Then sort them again into concepts of the same type;this ‘‘domain dictionary’’ is not only a first stage inontology building, it is also useful as a check on the

550 J. Kingston / Expert Systems with Applications 34 (2008) 541–550

completeness of knowledge acquisition, for it quicklybecomes obvious if several concepts of the same typeare linked by a particular relation, but others are not.Domain dictionaries can easily be represented in taxon-omy-based ontology tools such as Protege; domainmodels are better represented as individual node-and-arc (box-and-arrow) diagrams, such as those supportedby some CASE or computer-aided knowledge engineer-ing (CAKE) tools, or simple flowchart/diagram tools.

• Use these domain models to consider whether the iden-tified relations are correct and/or necessary. For exam-ple, a domain model showing all known is-a relationsthat includes three relations of the form A is-a B, B is-

a C and A is-a C probably highlights an example ofIS-A overloading (overgeneralization) in the A is-a C

relation. Similarly, identifying D is-a E and D is-a F

where E and F are separate high-level concepts mayindicate confusion of senses. Analysis tools that underliesome CASE or CAKE tools should be able to identifysome problematic relations automatically, as was dem-onstrated during the development of TOPKAT, a proto-type CAKE tool (Kingston, 1994).

• Match the relations against Table 1 above to determinewhat perspectives on knowledge are prominent here. Ina legal domain, for example, the relations justifies andhas-rights-to may support large domain models, whilein a manufacturing domain, transforms, creates, pre-

cedes and connected-to may be more important.• At this point, a judgement is required. If a domain

model is very large, it should probably be transformedinto a single-perspective ontology. This may result inhaving more than one ontology for a single perspective;it’s entirely possible to have both a taxonomy (an ontol-ogy based on the IS-A relation) and a mereonomy (anontology based on the PART-OF relation) within thesame domain, for example, even though they both cor-respond to the WHAT perspective. If a domain modelis small, however, it’s probably feasible to merge it withother ontologies from the same domain; process ontolo-gies, for example, typically include relations such as con-

sumes, produces, creates, transforms, and uses within thesame ontology.

• Finally, levels of detail should be determined. As sug-gested above, current guidance is in the form of rulesof thumb or analysis of topological connectedness.

Once a multi-perspective ontology has been developed,the question of how it is to be applied arises. The answeris that single perspectives can be applied in exactly the sameway that taxonomies, process descriptions, or organisa-tional charts are applied today; the main goal of multi-per-spective ontologies is for each perspective to inform thedevelopment of other perspectives in order to produce a

model of the domain that is a s complete and accurate aspossible. However, there are occasions where it is necessaryto draw on more than one perspective; the success of theUnified Modelling Language (UML), which is a multi-per-spective modelling tool for supporting software develop-ment, and of the CommonKADS methodology forknowledge based systems development, demonstrate this.

References

Cook, M. (1996). Building enterprise information architecture: Reengi-neering information systems. Prentice-Hall.

Corazzon, R. (2000). Descriptive and formal ontology. Available fromhttp://www.formalontology.it.

Graham, I. (1991). Object oriented methods. Wokingham: Addison-Wesley P.C., p. 252.

GRASP (1999). Global retrieval, access and information system forproperty items. Available from http://www.ejeisa.com/nectar/teiss/6.1.3/fact_sheets/pdf/grasp.pdf.

Guarino, N. (1997). Semantic matching: formal ontological distinctionsfor information organization, extraction, and integration. In M. T.Pazienza (Ed.), Information extraction: A multidisciplinary approach to

an emerging information technology (pp. 139–170). Springer-Verlag.Guarino, N., & Giaretta, P. (1995). Ontologies and knowledge bases:

towards a terminological clarification. In N. Mars (Ed.), Towards very

large knowledge bases: Knowledge building and knowledge sharing

(pp. 25–32). Amsterdam: IOS Press.Hokel, T. A. (2006). The Zachman framework for enterprise architecture:

an overview. Available from http://www.frameworksoft.com/web-site-downloads/FSI-Enterprise-Architecture-Overview-Paper.pdf.

Kingston, J. (1994). Linking knowledge acquisition to commonKADSknowledge representation. In Proceedings of BCS SGES expert systems

’94, Cambridge, 12–14 December.Kingston, J. (2002). Merging top level ontologies for scientific knowledge

management. In Proceedings of the AAAI workshop on ontologies and

the semantic web, AAAI-02 conference, Edmonton, Canada, 29 July2002.

Kingston, J. (2005). Multi-perspective modeling: a framework for knowl-edge representation and knowledge management, Joseph Bell CentreReport Series No. 45, http://www.cfslr.ed.ac.uk/publications/k0046.pdf, extended version of a paper in the Proceedings of AI-

METH 2005, Silesia University of Technology, Gliwice, Poland,November 2005.

Lenat, D. (1998). The dimensions of context space. Available from http://www.cyc.com/doc/context-space.pdf.

Maedche, A., Neumann, G., & Staab, S. (2002). Bootstrapping anontology based information extraction system. Studies in fuzziness and

soft computing: Intelligent exploration of the web. Springer/PhysicaVerlag, pp. 345–359.

Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt,N., Van de Velde, W., et al. (2000). Knowledge engineering and

management: The CommonKADS methodology. 0262193000. MITPress.

Smith, B. C. (1996). On the origin of objects. MIT Press, p. 44.Swartout, W., Patil, R., Knight, K., & Russ, T. (1996). Toward distributed

use of large-scale ontologies. In: Proceedings of KAW ’96, Banff, 1996.Welty, C., & Guarino, N. (2001). Supporting ontological analysis of

taxonomic relationships. Data and Knowledge Engineering, 39(1),51–74.

Zachman, J. (1987). The Zachman framework. Available from http://www.zifa.com.