a system architecture for knowledge-based hypermedia

Int. J. Human-Computer Studies (1999) 51, 1007}1036Article No. ijhcs.1999.0250Available online at http://www.idealibrary.com on

A system architecture for knowledge-based hypermedia

ANNELI EDMAN AND ANDREAS HAMFELT

Department of Information Science, Division of Computer Science, Uppsala University,Box 311, S-751 05 Uppsala, Sweden.e-mail: [email protected]; [email protected]

Hypermedia systems and knowledge systems can be viewed as #ip-sides of the same coin.The former are designed to convey information and the latter to solve problems;developments beyond the basic techniques of each system type requires techniques fromthe other type. In this paper, we introduce the concept of knowledge-based or intelligenthypermedia and analyse various constellations of merged hypermedia and knowledgesystems. A hypermedia system deals with informal and formalized theories and therelations between and within these. Therefore, the corner stones of our analysis are thevery basic notions involved in formalizing domain knowledge: an informal domaintheory, a formal object theory axiomatizing the informal theory and a metatheoryanalysing the properties and interrelations between and within these. We integrate thesenotions into a system architecture which is to serve as a programmable system schemafor supporting the composition of actual intelligent hypermedia systems. Programmingin the large is supported by the schema which de"nes the overall system structurewhereas programming in the small is supported by knowledge modelling techniques. Theapplication of the system architecture is illustrated by the construction of an interactivediagnosis system which involves knowledge-based reasoning both for navigation inhyperspace and problem-solving within the domain.

( 1999 Academic Press

1. Introduction

Today, computer systems incorporate many di!erent media. There are both fundamentaland pragmatic reasons for this. Firstly, fully automatizing information is seldom feasibleand thus not even an option. Secondly, the computer is an excellent means of incorporat-ing and presenting information already available on various media. Together this hasgiven rise to the notion of &&multimedia'' and as a special case &&hypermedia'' when thedata on various media are associated by means of links.

The links of a hypermedia system form a so-called hyperspace which expands quicklywith the addition of new nodes. Both the construction and searching of such a space isa complex task. Without an advanced and #exible support, the user easily gets lost ordisoriented (Conklin, 1987; Waterworth, 1992). Therefore, to fully exploit the opportuni-ties of hypermedia, advanced programming techniques with a high degree of expressivityare needed, e.g. for supporting search by means of knowledge-based computer reasoning.Knowledge technology o!ers methodologies for program and system development forcomplex domains. Therefore, it has been a natural step to apply these techniques to

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1008 A. EDMAN AND A. HAMFELT

hypermedia, (cf. e.g. Bench-Capon, Soper & Coenen, 1991; Hamfelt & Barklund, 1990;Rada & Barlow, 1989a; Soper & Bench-Capon, 1992; Woodhead, 1991), resulting inwhat may be termed &&knowledge-based hypermedia'' (Heath, Hall, Crowder, Pasha& Soper, 1994).

Knowledge technology o!ers various programming techniques with a preference forfunctional and relational programming languages. These come with structured method-ologies for constructing programs in a compositional manner by means of operatorstaking the form of program schemata (cf. e.g. Bird & Wadler, 1988; Hamfelt & Nilsson,1997). These methodologies are intended for programming in the small. Concerningprogramming in the large*software engineering*there is ongoing work for de"ningoperators for combining and integrating information and knowledge from di!erentsources. A prerequisite for such techniques is that the formal properties of the compo-nents are well-de"ned such that semantic-preserving operations can be guaranteed.

It seems intuitively appealing and quite generally adopted that support for program-ming and system development should rely on some schema notion. In this article, wepropose a schematic system architecture for knowledge-based hypermedia. The para-meter components of this architecture will not be fully formal objects since theirsemantics is dependent on an external observer. Therefore, rather than guaranteeinga composition of the components that respects formally de"ned semantics, the proposedarhitecture is to serve as a means for promoting generally agreeable properties such asmodularity, comprehensibility and maintainability.

The paper is organized as follows: In Section 2, we introduce the basic notions offormalization and argue why these may serve as a basis for a system architecture; inSection 3 these components are further analysed considering especially their interre-lationships; in Section 4, we relate hypermedia to notions in computational intelligenceand categorize various constellations of hypermedia and computational intelligence; inSection 5 we evaluate a range of increasingly expressive programming methodologies forknowledge modelling from the perspective of versatility and tractability, between whichthe most reasonable compromise appears to be non-ground metalogic programming; inSection 6, we consider a sample domain from the perspective of implementing it in thesystem architecture; in Section 7, we propose an implementation of the system architec-ture; in Section 8, we compare our proposal to related work; and "nally in Section 9 weconclude.

2. Basic notions of formalization

A hypermedia system consists of information expressed at various media and a computa-tion mechanism, which caters for the interrelations between the media. Each mediarequires its own particular tool for recording and reproducing information. The role ofthese tools is thus essentially di!erent from that of the computation mechanism.

How should we understand the computer mechanism part and the information part ofa hypermedia system? To serve as a basis for a system development methodology andsubsequently an implementation tool, the analysis must be carried out at a level thatbrings out the essential properties for obtaining a computable formalization for thesystem. Although automatization is not always appropriate, one should at least strive toprovide this option whenever feasible.

A SYSTEM ARCHITECTURE FOR KNOWLEDGE-BASED HYPERMEDIA 1009

A hypermedia system deals exactly with informal and formalized knowledge and therelations between these. The perspective on formalization put forward by Kleene isgenerally accepted and extracts properties concerning the relations between informaland formalized knowledge in a system. Therefore, Kleene's perspective serves well asa basis for understanding the formalization of a hypermedia system.

Kleene (1971) introduces the following three separate and distinct &&theories'' involvedin the process of formalization.

(a) The informal theory (IT) of which the formal system constitutes a formalization.(b) The formal system or object theory (OT).(c) The metatheory (MT), in which the formal system is described and studied.

Normally, MT analyses the formal properties of OT but it can also study propertiesand relations within and between IT and OT. Moreover, MT can be formalized andimplemented as a program. IT describes the application domain that OT formalizes. Intraditional information systems, attention is restricted to OT and the informal part ingeneral is minimal, e.g. a rudimentary user interface. In contrast, most often a hyper-media system has only an IT but no formalization of the domain, i.e. no OT, so thecomputation mechanism will correspond to MT. If however a formalization of thedomain theory is included there are two computation mechanisms, one corresponding toMT and one to OT.

MT should promote the following attributes of well-engineered software: maintaina-bility (facilitating updating and altering of the system), usability (adequate user interfacesand adequate documentation) and e$ciency (Sommerville, 1996). These ends are met ifthe metatheory gives a high degree of modularity and allows the respective system partsto be expressed in their natural way, thus promoting clarity and making the system easyto survey.

An important property of the formal theory is its relation to the informal theory, whichis handled by the metatheory and which depends on the purpose of the system. Theremay be various degrees of correspondence between IT and OT. In most hypermediasystems, there is no formalization of domain knowledge, so no OT exists. But OT canalso formalize domain knowledge as rules corresponding to the informal propositions ofIT. OT must in this case be sound with respect to IT, which is the responsibility of MT.A minimum requirement is that OT is formally sound, i.e. logically consistent.

The informal part may have a passive role in a hypermedia system, i.e. it does not a!ectthe computations of the system at all. Most often, however, it has an active role in thesense that it inspires input. What input is given depends on the external observer'sinterpretation of the informal information. If a comprehensive system perspective isapplied the user1s input must be considered as a part of the system, not just a side-e!ect.Accordingly, the system should enable the user to perform sound interpretations yieldinghigh-quality input. In addition, the input has to be integrated into the system such that itcan be understood as a part, either of the formal or the informal parts or of both.

Moreover, one must take into account that the informal part corresponds to a coher-ent domain theory. This coherence should be respected by the system.

Apparently, a system development methodology for hypermedia systems must bebased on some general understanding of the structure of such systems and their


constituents*a system structure architecture. Above we have put forward a metaper-spective where MT serves to relate the system parts. Such a metaperspective is not new;some authors, e.g. Woodhead (1991), have re#ected upon such an approach. However, tothe best of our knowledge, no attempts have been made at an elaborate systemarchitecture for the metalevel connecting the various parts of a hypermedia system. Inthe sequel, we propose such an architecture and illustrate its application to a domain.

3. The constituents of the system architecture

In our system architecture for hypermedia systems, we have now identi"ed three mainparts: OT, IT and MT.

OT is an axiomatization of the domain knowledge. This kind of knowledge can beformalized as logic clauses. Apart from these non-logical axioms, OT has an implicit partnamely the theorems logically deducible from these axioms.

IT is composed of informal descriptions of domain knowledge. The knowledge may,e.g. consist of information about classes and sub-theories within the domain. Thus, theknowledge in IT need not have a formalized counterpart in OT but can be a complementto it (see Figure 1).

The intersection of IT and OT is the &&corresponding'' overlapping knowledge in thetheories. OTCIT contains, e.g. theorems logically implied by the non-logical axioms ofOT. ITCOT contains informal knowledge not directly related to the formalization.

MT formalizes knowledge about the relation between OT and IT and knowledgeabout the respective structures of OT and IT (see Figure 2). MT may also containinformation about how OT was formalized from IT, the strategy chosen and whatapproximations were made.

MT also carries out the reasoning in the system both in OT and IT. The reasoning willbe based upon inferences in OT together with the informal knowledge the user supplies.Therefore, a task for MT is to carry out this communication between the user and thesystem (See Figure 2). Except for integrating the user's contributions, this communica-tion includes a presentation of the informal theory to the user, enabling inspection of theknowledge in the system, either for learning from the system or for cooperation duringthe reasoning. MT also supports the user's navigation in IT. Apparently, this requiresthat MT carries out formal reasoning about the informal knowledge stored in the system.To summarize, MT may incorporate knowledge concerning the following.

(i) The structure of OT.(ii) The structure of IT.

FIGURE 1. The relationship between IT and OT.

FIGURE 2. The relationship between OT, IT, the user and MT.


(iii) The relation between OT and IT.(iv) The interaction with the user and the integration of user input.(v) The reasoning, both with formal rules in OT and informal descriptions of domain

knowledge in IT.

4. Hypermedia and computer intelligence

Multimedia systems are built on some underlying computation mechanism whichlike MT connects the various parts of the system. This mechanism treats reproduc-tions at various media but does not necessarily make explicit the linking betweenthese. A relatively low level programming method could do for implementing thismechanism. Hypermedia systems on the other hand are centred around the notion ofa link.

A hyperlink is strictly at the metalevel to the semantic or content level (cf. Woodhead,1991). A link can be understood as a relation between components, e.g. cards, frames,documents or articles (Halasz & Schwartz, 1994) and within-component linkmarkers,i.e., anchors. Relations are extensions of logic predicates. The predicate name givesa so-called &&intensional'' meaning to the predicate. Although the extension is the same,di!erent names will give di!erent intensions. Similarly, di!erent intensions are attributedto the same predicate when it is de"ned in terms of other predicates and severalalternative de"nitions exist. Typing of links gives an intensional description of a relationand have been implemented in so-called &&second-generation hypermedia systems''.Naming of links is a simple typing technique. Knowledge-based methods yield morepowerful typing by allowing de"nitions of relations as production rules or logicalpredicates.

As far as links are concerned, the corresponding relations appear to be binary. Unaryrelations (properties) are needed for expressing properties of individual nodes. On theother hand, for the purpose of representing links, there seems to exist no immediate needfor n-ary relations.


Semantically then, the spectrum of links spans from "nite sets of pairs to in"nite sets.The former may be explicitly de"ned (enumerated) whilst the latter need implicitde"nitions allowing, e.g. recursion. Knowledge-based methods allow the de"nition of allkinds of links. Explicitly given pairs correspond informally, e.g. to the intended associ-ations of words, pictures and whole documents. This gives a straight-forward analogybetween semantic networks and hypertext (Rada, 1990; Wang & Rada, 1995) where MTformalizes associations of terms in IT. This also suggests natural extensions such as typednodes, semistructured nodes (frames) and inheritance hierarchies of node and link types(Conklin, 1987).

We call a hypermedia system intelligent if and only if its links are set up by a know-ledge based computer reasoning. This de"nition covers a notion one does perhaps notimmediately associate to hypermedia: &&canned text'' or rather &&canned information''since other modalities than text are involved. Here there is a knowledge-based inferenceengine that exploits static informal knowledge, normally expressed on the text medium.Canned information serves to communicate the system's knowledge to the user and doesnot directly in#uence the system's reasoning.

Canned information is a "rst step towards intelligent hypermedia. The system containsinformation and links between these are obtained by rule-based reasoning. Althoughcanned information was never intended as a hypermedia technique, experiences with thisnotion might prove useful for a better understanding of intelligent hypermedia.

The purpose of canned information is to present the informal theory to the user bymeans of natural language texts, pictures or whatever. When the theory has beenformalized, the canned information is associated with parts of the knowledge base oreven with each rule in the base, explaining their meaning. During the reasoning, thesystem on demand merely displays the information associated with its current state.

The advantage with such an approach is simplicity. The information is not generatedat run time but prepared carefully at programming time. However, the price is that allquestions and answers must be anticipated in advance. For large systems, that is a nearlyimpossible task (Swartout, 1981). Furthermore, the canned information and the formaltheory can be changed independently and no support is provided for preserving consist-ency between them. Neither is there any support for maintaining coherence within theinformal theory when updating it or extending it through input. The latter is necessaryfor a comprehensive system perspective where the user's knowledge is taken as a part ofthe system.

Thus, canned information cannot represent a coherent informal domain theory, it canonly represent it as fragments. This makes it di$cult to analyse the relation between theobject theory and the informal one. The lack of coherence yields an in#exible system. It isnot possible to use the information in di!erent ways so as to adapt and reuse it indi!erent contexts. Canned information can be viewed as static hypermedia.

Intelligent hypermedia systems must be able, not only to guide the user to staticinformal knowledge, but also to compose dynamically pieces of informal knowledge. Thisis needed for presenting informal knowledge corresponding to di!erent states in thereasoning. Needless to say, all these states cannot be anticipated and described inadvance as is required by the canned information approach.

An intelligent hypermedia system must perform computer reasoning about the infor-mal presentation of the domain knowledge in contrast to traditional knowledge systems


that derive conclusions from a formalization of domain knowledge. The knowledge basesof the respective system types represent thus fundamentally di!erent kinds of knowledge.Since the two types might be combined, intelligent hypermedia divides into two catego-ries: unary intelligent hypermedia systems which have one knowledge base representingknowledge about the links, and dual intelligent hypermedia systems which in additionhave a knowledge base representing problem solving for a domain.

Unary intelligent hypermedia requires that the relations between pieces of informalknowledge are made programmable instead of hard-wired in the system. This enables thecomputation of a coherent presentation of the informal theory. Dual intelligent hyper-media requires, in addition, that the relation between the object theory and the informaltheory is made explicit. Moreover, some notion of soundness is to be promoted betweenOT and IT, at least by arranging that both theories are updated simultaneously. All theseaspects are to be taken into account at the level of MT.

Intelligent hypermedia belongs to the class of information systems that communicatesknowledge, not only data. The table in Figure 3 below summarizes the re#ections madein this section and put intelligent hypermedia into context.

Some attempts have been made to exploit knowledge-based methods for hypermediasystems. These systems employ various strategies for coupling the knowledge systemmodule and hypermedia module. The hypermedia system may be completely indepen-dent of the knowledge system and serve only as its interface (Bender, Edman & Sundling,1995). The knowledge system may support navigation by setting up the links betweendocuments in hyperspace through computer reasoning (Hamfelt & Barklund, 1990).Other systems appear between these extremes see e.g. (Akselsen, 1991; Bender-OG berg& Edman, 1996, 1997; Nanard & Nanard, 1995; Neal & Shapiro, 1994; Pivec & Rajkovie,1997).

The purpose of a system development methodology is to promote well-structured,comprehensive and easily maintainable systems. To this end, the methodology shouldprovide an adequate architecture in which the system can be constructed. Our contribu-tion is a system structure which explicitly extracts di!erent types of knowledge into threedistinct modules (cf. Edman & Hamfelt, 1997): (1) a knowledge system representing theapplication domain, (2) a hypermedia system with informal descriptions of the domain,

Knowledge systems Hypermedia Unary Intelligent Dual Intelligenthypermedia hypermedia

Automatized problem-solving in a formaltheory axiomatizinga domain

Metaprogram forsearching amongdocuments givinginformal descriptionsof some domain; thesedocuments arefragments of aninformal domain theoryNo formal theory

Metatheory forreasoning about therelations between theinformal documentsof the informal theoryNo formal theoryaxiomatizing thedomain

Metatheory for reasoningabout the informal theoryMetatheory for reasoningin a formal theory whichaxiomatizes the knowledgein the domain described bythe informal theory

FIGURE 3. Di!erent system types and their characteristics.


and (3) a metalevel knowledge system that may reason about some or all of the two othermodules and the user input. An early example of a system with modules (2) and (3) isfound in Hamfelt and Barklund (1990).

5. Programming methodologies for computer intelligence

We have considered the above four di!erent classes of systems. In this section, we willconsider programming methodologies appropriate for the most advanced class, the dualintelligent hypermedia system. The programming language supported by the methodo-logy must be expressive enough to represent the modules of such a system, yet becomputationally tractable.

5.1. KNOWLEDGE MODELLING STRUCTURES

To support program development, ready-made program structures are essential. Confer,for instance, the tradition of structured programming where parameterized programschemes are supplied to facilitate programming (Backus, 1978). At the system architec-ture level, this corresponds to providing a programmable schema for the whole system,parameterized with schemata for the modules of the system. In the case of intelligenthypermedia system, according to the above analysis, the system scheme corresponds toMT and should be parameterized with two schemata of the modules IT and OT.

Depending on the expected needs of the modules, the schemata should supportsuitable programming techniques, e.g. techniques for computer reasoning, object-oriented programming etc. Let us now consider what techniques are reasonable toincorporate for the respective modules of a dual intelligent hypermedia system.

MT expresses relations between objects within the respective theories IT and OT orbetween them. MT requires thus a representation language capable of expressingrelations. Preferably, the language should have a well-de"ned formal semantics and alsoan intelligible external semantics. Moreover, both OT and IT may have a complexinherent structure. MT should therefore have adequate data structures for modelling thisstructure.

OT requires a language expressive enough for formally approximating the knowledgeof the actual domain. Most domains require at least relations (cf. e.g. Muggleton & DeRaedt, 1994).

First-order logic is a relational language with a well-de"ned formal semantics and anexternal semantics close to natural language. The relations are set up between terms thatmay be atomic or compound. Compound terms are data structures that connect otherterms by functors.

An advanced form of data structures are concepts that roughly correspond to objectsin object-oriented programming, i.e. terms arranged in inheritance hierarchies andcoupled with methods, etc. Concepts may be represented in monadic logic (logicrestricted to unary predicates). Conceptual logic languages set up relations between suchconcepts and allow reasoning with them. A conceptual logic is thus a metalanguage withmonadic logic as its object language. In ordinary logic, the formulas are extensional, i.e.in the formal semantics two syntactically di!erent formulas are indistinguishable if theyhave the same extension. Concept logic, in contrast, may be intensional also in the formal

TABLE 1

Links Objects Formal logical Externalsemantics semantics

Plain hyperspace Passive Passive NoneStatic semantic networks Passive Passive None(Quillian, 1967)Object-oriented paradigms Passive Active NoneRelational databases Active Passive Extensional ExtensionalRelation based languages:i) First-order logic Active Passive Extensional Intensionalwith ordinary termsii) First-order logic Active Active Intensional Intensionalwith concepts


semantics. The metalanguage has a term-level description of the formulas representingconcepts, so syntactically di!erent concepts may be considered semantically distinctregardless of coinciding extensions. Thus, conceptual languages allow a more richrepresentation of objects depending on their semantics.

In database terminology, explicit de"nitions (enumerations of the instances of a predi-cate) are often called extensional since they directly represent individual tuples ofa relation. Implicit de"nitions as rules, on the other hand, are called intensional since toan external observer they describe the meaning of the predicate. They have an intensionalexternal semantics.

Table 1 classi"es programming approaches based on the role of the links and objects.We say that a link is active if it supports reasoning with the objects, otherwise it ispassive. An object is active if it communicates with other objects and other externalstimuli, otherwise it is passive.

The highest degree of modelling support is a!orded in conceptual logics. A rathernatural application for this paradigm would be for dealing with IT since IT in factappears as a structured collection of various pieces of informal information. Relationsbetween these pieces are dealt with by MT. OT which formalizes domain knowledgeneeds an expressivity adequate for the actual domain. This should at least includerelations and preferably also concepts. MT represents the relation between OT and IT.In conclusion, MT necessarily needs a relational language and could bene"t, especiallyfor dealing with IT, if support for modelling concepts was provided.

We can thus conclude that a conceptual language would be suitable for MT but today,no well-established logic programming languages with concepts exist. However, "rst-order logic programming languages with ordinary terms are available. These languagesallow restricted metaprogramming facilities which is required here since MT is a meta-theory with respect to OT and IT.

5.2. METALOGIC PROGRAMMING

The most generally deployed relational programming paradigm is logic programming.Logic programming languages are subsets of logic languages. Unrestricted "rst-order


predicate calculus is computationally intractable and therefore most logic programminglanguages are based on a subset of "rst-order calculus known as Horn clauses (Kowalski,1974). De"nite clauses are Horn clauses with only one consequence formalized as

CQA1&2& A

n

with the informal reading &&C if A1

and 2 and An'', n*0, and C and A

iare atomic

formulas.An atomic formula is written as P (t

1,2 , t

m) where m*0, and P is a predicate

denoting a relation between the terms t1,2 , t

m. A term may be either a variable,

a constant or a compound term f (s1,2 , s

k) where kz1 and f is a functor and s

iare terms.

All variables in a Horn clause are implicitly universally quanti"ed.In logic programming, a program % takes the form of a "nite collection of de"nite

clauses, and program execution is conducted as a logical inference process for verifyinga goal clause:

%@ goal

The goals here are (conjunctions of ) atomic formulas, where variables are existentiallyquanti"ed. The computation of a proof provides term instances of the variables, whichare then construed as a result of the computation.

The logical inference is usually conducted as a resolution proof, say using SLD-resolution, applying a depth-"rst search of the space of resolvents. In the refutation proofthe hypothesis goal is negated (hence the variables become universally quanti"ed);a successful proof is reached when the empty clause resolvent is obtained.

Prolog (Colmerauer, Kanoui, van Caneghem, Psero & Roussel, 1973) was the "rstmore widely used logic programming language.

In metalogic programming (Bowen & Kowalski, 1982) a theory in the form of a logicprogram % comprising clauses is encoded as a term v%w and the provability relation &&@ ''is formalized as a predicate proof ( , ). This predicate is de"ned by clauses %

1300&constitut-

ing a logic program, often referred to as a metainterpreter.Accordingly

% @ goal i! %1300&@ proof(v%w , vgoalw )

which is sometimes regarded as a pair of opposite re#ection principles for passingbetween object level and metalevel. The ways of encoding a formula p into a correspond-ing term vpw (cf. GoK del encoding) are discussed extensively in the metalogic program-ming literature (see e.g. Eshghi, 1987). Basically, there are two options; (1) ground-termrepresentation in which variables of the object language formula become ground (i.e.variable-free) terms in the encoding and (2) non-ground encoding in which objectlanguage variables become variables of the metalanguage. The latter representationmakes it easier to exploit the &&built-in'' object-level reasoning mechanism (includinguni"cation) at the risk of causing confusion between variables of the di!erent languagelevels.

The explicit availability of the proof (computation) predicate proof through theinterpreter clause program %

1300&in metalogic programming renders it possible to

&&customize'', monitor and control the deduction process. Moreover, the metalevelencoding of object-level formulas makes it possible to de"ne schemata as partially


instantiated descriptions of programs. Such schemata functions e!ectively as classes ofprograms. The readymade program structures forming the basis of a structured pro-gramming methodology represent really classes of programs. Metalogic programming isthus convenient for de"ning program development methodologies (see e.g. Hamfelt& Nilsson, 1997).

6. A sample domain for intelligent hypermedia

We will now consider a small prototype dual intelligent hypermedia system so as toillustrate knowledge reproduced in IT, OT and MT, respectively. Dual intelligenthypermedia systems are intended for domains that require problem solving as well asintelligent hypermedia. Suppose we are developing a system for diagnosis of childhoodillnesses. The medical domain has good potentials for the knowledge-based hypermediaapproach. The reason is that medical knowledge often has to be communicated throughpictures, sounds and animation, which can be supported by hypermedia. Moreover, theprofessionals carry out problem-solving in the form of diagnosis, which can be modelledas knowledge-based computer reasoning. The diagnosis involves everything from pro-viding sampling data to making sophisticated assessments of symptoms. The user mustbe enabled to contribute within the whole spectrum. Computing informative presenta-tions aiding the assessments of symptoms calls for intelligent hypermedia. Consequently,combining computer reasoning and hypermedia techniques is quite an obvious way ofdesigning a powerful medical support system. Our prototype system helps diagnosingchildhood illnesses, which we exemplify below with measles and mumps.

Below we start modelling the domain as in a traditional knowledge system. As a meansof making the user's knowledge a part of the system, we then introduce notions fromunary intelligent hypermedia systems to obtain a dual intelligent hypermedia system.Finally, we analyse what kinds of knowledge appear at the MT-level in such a system.

6.1. RUDIMENTARY INTELLIGENT HYPERMEDIA

We now model the problem-solving of the domain as a traditional rule-based knowledgesystem coupled with canned information. The formalized knowledge in OT is repre-sented as Horn clauses (see Figure 4). The clauses are associated to canned informationwhich communicates informal knowledge from IT.

The canned information describes the content of a rule or a group of rules. On demandthis prerecorded information is simply displayed. Links between the documents areobtained by knowledge-based reasoning, but the rules model problem-solving know-ledge within the domain rather than associations between the documents. Therefore, thesystem is unable to present a coherent informal description of the domain.

6.2. TOWARDS A MORE FLEXIBLE SOLUTION

A rule de"nes relations between di!erent objects. See for instance rule (6) in Figure 4,where the objects are early symptoms for measles, cough, cold and temperature. A "rststep from the rigid canned information approach is to add a metalevel to the system fordynamically composing canned information into presentations adapted to the current

Informal theory, IT The formal theory, OTas canned information

(1) It is very probable that a patient has got themeasles if it is very probable that thesymptoms show mealses and the patient isnot immune to it.

disease (P,measles, &very probable') Qsymptoms(P,measles, &very probable') &not immunity (P,measles).

(2) It is possible that a patient has got themeasles if it is possible that the symptomsshow measles and the patient is not immuneto it.

disease(P,measles, possible)Qsymptoms(P,measles, possible) &not immunity(P,measles).

(3) Mumps has only early symptoms and theseare quite typical. Therefore it is very probablethat the patient has got mumps if thesymptoms match. Mumps does not givelife-long immunity.

disease(P,mumps, &very probable') Qearly

}symptoms(P,mumps).

(4) It is very probable that a patient has thesymptoms for measles if both the earlyand the late symptoms match thosewith measles.

symptoms(P,measles, &very probable')Qearly

}symptoms(P,measles)

& late}symptoms(P,measles).

(5) It is possible that a patient has thesymptoms for measles if the early symtomsmatch those with measles.

symptoms(P,measles, possible) Qearly

}symptoms(P,measles).

(6) The early symptoms satisfy those formeasles if the patient in the beginning of thedisease has hacking cough or a cold andin both cases high fever.

early}symptoms(P,measles) Q

(e}symp(P,cough, &hacking cough') s

e}symp(P,cold, yes))

& e}symp(P,temperature, &high fever').

(7) The late symptoms for measles are skineruption after 3 days beginning in the neck.

late}symptoms(P,measles) Q

l}symp(P,rash, &skin eruption after 3 days

beginning in the neck').(8) Early symptoms for mumps are that the

patient in the beginning of the disease hasswelling ear salivary gland and/or tonguesalivary gland and in both cases high fever.

early}symptoms(P,mumps) Q

(e}symp(P,swelling, &ear salivary gland')s

e}symp(P,swelling, &tongue salivary gland'))

& e}symp (P,temperature, &high fever').

(9) A patient has immunity to measles, eitherif the patient has had it before or is youngerthan 7 months and the mother has hadmeasles.

immunity(P,measles)Qhave

}had(P,measles) s

(age(P,A) & A(&7 months' &mother(P,M) & have

}had(M,measles)).

FIGURE 4. Knowledge concerning two childhood illnesses.


situation. For instance, the separate objects of a rule can be described by texts. Then if anobject is used in several di!erent rules, such as &&temperature'' above, the same informa-tion can be reused in di!erent contexts, and most importantly the metalevel candynamically generate a presentation of informal domain knowledge from the informa-tion coupled to the various objects.

The inferential pattern of OT can be used to generate an informal display of thesystem's inferential context. Still, this only gives a fragmentary reproduction of the


informal domain theory, however. A more comprehensive reproduction requires, inaddition, informal knowledge without any direct counterpart in OT such as conceptualcontext.

Let us consider the concepts involved in the inferential context of OT's problemsolving. A rough distinction can be seen between objects according to their participationat di!erent phases of the problem-solving. An object could be a goal object, i.e. thesystem will try to conclude a value for it, and if successful, it will present the object and itsvalue as a result from the reasoning. The user can provide values for some objects; thesewill be called ground objects. All objects between goal objects and ground objects arecalled intermediate objects. Values for such intermediate objects can be viewed asintermediate results. They can be used in the "nal conclusion or to obtain values relatedto other intermediate objects, for subsequent use in the reasoning identifying goalobjects. Figure 5 shows the di!erent kinds of objects.

The concepts involved in OT's problem-solving appear in a conceptual context whichincludes all surrounding concepts and their mutual interrelations, such as hierarchicalconnections, e.g. di!erent levels of abstractions. This kind of knowledge can provenecessary for justifying the knowledge formalized in OT.

In Figure 6, OT knowledge appears in the left column. At the corresponding rows inthe right column appears IT knowledge which sets up the inferential context. In additionto these rows, there are rows containing IT knowledge exclusively for setting up theconceptual context. These rows are empty in the left column.

6.3. AN ILLUSTRATION

Let us give an example of a dynamically computed display of IT information from thedomain of childhood illnesses. This is to illustrate how the inferential and conceptualcontext can be used for facilitating user and system cooperation in a dual intelligenthypermedia system.

FIGURE 5. Di!erent kinds of objects.

Knowledge in OT Knowledge in IT

Rules with the exact relation between di!erentgroups of objects for making a diagnosis.

Knowledge in a higher abstraction level to give theuser an understanding for how to make a diagnosis,referring to early and late symptoms together withimmunity.

Justi"cation of the domain knowledge.

Di!erent symptoms represented in di!erent rules. A coherent representation of the symptoms.

The user is supposed to know, for instance, how thepatients temperature can be measured and evenhow one estimates the temperature.

Knowledge about how to perform measurements andestimating them.

Results connected to rules. An evaluation of the result.

The user is supposed to know how to judge thekind of rash.

De"nitions of di!erent categories of rash andinformation about how to di!erentiate them.

Recommended treatment and possible complicationsfollowing the disease.

General information about the disease, for instance ifit is a virus or infection, how it infects and the lengthof the incubation period.

FIGURE 6. Knowledge in OT and IT.


Similar to the conceptual context the inferential context gives also a kind of abstrac-tion levels since rules (or material implications) like &&if human(x) then mortal(x)''e!ectively functions like links from sub-classes to super-classes. Thus, forward reasoningoften proceeds from concrete notions to more abstract notions and vice versa forbackward reasoning.

Let us now consider the childhood illness, measles; other childhood illnesses aremumps, German measles, chicken-pox, whooping-cough and scarlet fever. In the reason-ing, &&childhood illness'' is a goal object, &&early symptoms'' and &&late symptoms'' areexamples of intermediate objects, and observations, such as a &&hacking cough'' or &&skineruption'' exemplify ground objects. In Figure 7, the objects are presented in an objecttree. This tree does not account for logical connections among the objects so thebranches are not inferential paths for reasoning in the domain. Instead, the tree organizesthe objects by linking them to associated objects (cf. the semantic net approach)according to some principle which is here exempli"ed by shared levels of abstraction.

When building up the informal domain theory, the inferential context coupled to eachobject may be described through di!erent categories of knowledge according to where itappears in the inferencing.

Let us suppose that the following properties are important for describing the objects inthe domain of childhood illnesses.

f For all objects*justi"cation of the knowledge.f For both goal and intermediate objects2totality and order. Let totality refer to theobjects associated to the current object, in particular at the adjacent lower level and let

FIGURE 7. The object tree for childhood illness, measles.


the order refer to the sequence in which the objects are investigated. For example forthe intermediate object &&symptoms measles'' the totality, according to the above tree,comprises the early and late symptoms; the order is that early symptoms should beconsidered "rst.

f For intermediate objects2consistency between objects, information about theobject class, general information and alternative ways of reaching a conclusion.Consistency means that certain objects cannot coexist, e.g. even if there aresymptoms measles cannot be at hand if the patient is immune. Alternative ways ofreaching a conclusion is for instance that measles can be diagnosed either in thebeginning, according to the early symptoms, or later, based on both early and latesymptoms.

f For ground objects*the method of observation or measurement, the unit and intervalof the measurement and observation or description of the object.

The notion of explanation in knowledge system research traditionally includes &&how''and &&why'' facilities. The former presents the reasoning carried out to reach the currentconclusion, whereas the later simply displays the rule being applied (sometimes supple-mented by subsequent rules) which needs input information.

Explanations are tightly coupled with the procedural behaviour of the system. Suchfacilities can easily be programmed within the system model proposed in this study.However, the methodology allows more abstract and declarative description of thesystem's knowledge and reasoning since MT may carry out knowledge-based reasoningconcerning how to present the knowledge from IT. Let us now exemplify this bya presentation of domain knowledge at di!erent abstraction levels based on the idea ofan inferential context.

FIGURE 8. A branch describing fever.


6.3.1. Display of inferential contextThe object tree is used when generating the explanations by means of the inferentialcontext. When the system asks for the knowledge required for the reasoning, e.g. &&highfever'' the user may ask for related information. If the object is used in relation to severalillnesses, these are presented by the system and the user chooses that which is currently ofinterest. If the answer is measles, the inferential context is presented for a branch inFigure 7, as shown in Figure 8. The display is expanded from informal knowledge aboutthe root object'' &&childhood illness'' for measles, the intermediate objects &&symptoms''and &&early symptoms'' for measles to the ground object &&high fever'' (see Figure 9). Notethat the contextual information for the goal level is on an abstract level, whilst it getsmore specialized at lower levels, where, e.g. ground objects tend to be coupled to fairlyclear instructions for how to obtain data.

Consider the branch in Figure 8. Above we have generated an informal description ofthis branch at a quite detailed level. Our system structure allows alternative abstractionlevels. For instance, the concepts involved in the nodes of the branch, such as &&highfever'', &&symptoms'', and &&childhood illnesses'' can be arranged in frame systems andexploited for the generation of concise but general description of the class of diagnoses towhich the branch belongs.

The user should constantly have access to an overview of the current reasoning. Thisrequires MT reasoning concerning the relation between IT and OT so as to select fordisplay informal descriptions of OT formulas. Figure 10 illustrates such a dynamicallycomputed display. The prototype system communicates what it is trying to prove and thepremises for the actual goal. When the user's interpretation of the informal domaintheory is needed, the user is asked for a contribution. The system's interpretations of theuser's answers are used in the reasoning. The outcomes attained are continuouslypresented to the user. In the "gure, a part of such a dialogue is outlined. Note that thedialogue is presented so as to illustrate how information can be put together by computerreasoning. Here we have only considered the content of the presentation, not the

Childhood illness measles

Totality: In general, you could say that child illnesses are the diseases the children may su!er from. Usually, itcomprises only six illnesses, i.e. measles, mumps, German measles, chicken-pox, whooping-cough, and scarletfever.Justi"cation: These diseases are common among children but rather unusual among adults. The reason is thatthe diseases are very infectious and therefore you get the diseases as a child and furthermore, several of themgive life-long immunity.Order: The program will start to investigate measles.

Symptoms of measles

Totality: To diagnose, in this case measles, you investigate the child's early symptoms, and if there are specialsymptoms later on during the illness, then eventually also these, as well as whether the child has immunity.Order: The program will investigate the early symptoms "rst.Consistency: Some illnesses give immunity: Measles is such an illness, giving life-long immunity.Justi"cation: You get immunity if you have had measles or are vaccinated against the illness.Class: The illness measles is a virus, it is spread through droplets and it is very infectious. The incubationperiod is 8}11 days.General information: Generally, try to keep the child in bed or still until he/she has no fever.The illness lasts for about 8 days. Possible complications are in#ammation of the ear, pneumonia andbronchitis.

Early symptoms of measles

Totality: Early symptoms for measles are a hacking cough and/or a cold together with fever.Alternative: When you diagnose an illness and the child has recently fallen ill, the diagnosis can only be basedon the early symptoms. If the child has been ill for some days, the diagnosis may be based on both early andlate symptoms.Justi"cation: For measles it is obvious that the illness has two phases, an early and a late one, where thesymptoms di!er considerably.

Fever

Observation: A high fever is when the temperature is 393C or more, it is moderately high between 38 and 393Cbelow 383C is not considered to be fever.Justi"cation: These levels are not exact since fever can di!er between individuals. Young children usually gethigher fever than older children or adults.Method: The temperature is measured with a thermometer.Interval: The temperature is in the interval 36}423C.

FIGURE 9. A dynamic presentation of fever related to measles.


appropriate way of displaying it. Note the resemblance with traditional &&why'' expla-nations, the di!erence being that this display is computed from informal domainknowledge.

Our system structure a!ords also the possibility of letting the metalevel moni-toring reasoning along several diagnostic branches (di!erential diagnosis) so asto identify for termination branches to be excluded. This is claimed to modelan important aspect of medical diagnosis (cf. Schneider & Sandblad, 1979). If the user,for instance, has observed fever all illnesses which have fever as a symptom are displayed(see Figure 11).

If the user then gives eruption as a symptom a new presentation is generated showingthose illnesses that have both these symptoms (see Figure 12). After giving cough asa symptom there are only two possible illnesses left (see Figure 13).

Will try to prove: childhood illness is measles with high probabilityPremise: symptoms for measles with high probability AND NOT immunity for measles

Will try to prove: symptoms for measles with high probability.Premise: early symptoms for measles AND late symptoms for measles

Will try to prove: early symptoms for measlesPremise: (early symptoms is hacking cough OR early symptoms is cold) AND fever is high

Which of the early symptoms, cold, hacking cough, swollen ear or tongue salivary gland, or none has thepatient?

User: swollen ear salivary glandCannot prove early symptoms for measles. Is the sentence early symptoms for measles equivalent to anothersentence?

User YesWhich expression is early symptoms for measles equivalent to?

User: other symptoms for measlesEquivalent to: early symptoms for measles to other symptoms for measles

Other symptoms for measles are known to the system. (No new theory has to be constructed)

Will try to prove: other symptoms for measlesPremise: fever is very high

How much fever has the patient, none, moderate, high, or very high?User: very high

True according to IT: fever is very highHas proved: other symptoms for measlesHas proved: early symptoms for measles

Will try to prove: late symptoms for measlesPremise: late symptoms is skin eruption

Which late symptoms has the patient, skin eruption, cough coming in attacks, or none?User: skin eruption

True according to IT: late symptoms is skin eruptionHas proved: late symptoms for measlesHas proved: symptoms for measles with high probability

Will try to prove: NOT immunity for measlesIs the patient immune to measles, yes, no or unknown?

User answer: noTrue according to IT: NOT immunity for measlesHas proved: NOT immunity for measlesHas proved: childhood illness is measles with high probability

FIGURE 10. The reasoning leading to the conclusion that the childhood illness is measles.


6.3.2. Display of conceptual contextLet us now turn to the conceptual context. Childhood illnesses are infections caused bybacterium or virus. Therefore, an overview of the domain of infections might prove usefulto the user. In Figure 14, di!erent causes for infections are presented, followed bya partial presentation of virus in Figure 15. The properties of the objects are inherited byobjects at a lower level. The values of properties at a lower level overrule those at a higherlevel.

Figure 15 accounts for a very small fragment of the total conceptualization of thedomain. To promote comprehensiveness a conceptual context of adequate size should bedynamically selected for presentation. The objects involved in the inferencing serve topoint out the relevant part of the conceptual context. For instance, the branch in Figure 8

FIGURE 11. Relations between the symptom fever and the childhood illnesses having this symptom.

FIGURE 12. Relation between the symptoms fever and skin eruption and the childhood illnesses having thesesymptoms.

FIGURE 13. Relations between the symptoms fever, skin eruption and cough and the childhood illnesses havingthese symptoms.

FIGURE 14. The di!erent causes of infection.


includes the concept &&symptoms for measles''which is su$cient for selecting for presenta-tion the segment in Figure 16 of the conceptualization.

Communicating the system's reasoning to the user at various abstraction levels isessential for providing a comprehensive view of the diagnosis, that includes also thecontext, not only the details. This improves the user's ability to contribute to thereasoning, not only providing data.

FIGURE 15. German measles, measles, mumps and chicken pox are caused by virus.


6.4. ANALYSIS

The prototype dual intelligent hypermedia system in this section illustrated the model-ling of the relation between formal theories and their informal counterparts as anenabling technique for adding facilities to hypermedia and knowledge systems. Let usnow pin down these relations and the reasons for considering them.

FIGURE 16. Measles is a virus.


Relations between O¹ and I¹.f The formulas of OT are only approximations of their counterparts in IT. Importantmetaknowledge is therefore statements concerning the proximity between the two.

f The formulas of OT represent only its explicit part. Important metaknowledge istherefore statements concerning the implicit part of OT that corresponds to the user'sinterpretation of IT knowledge.

Relations within O¹.f The formulas of OT are only non-logical axioms. Important metaknowledge concernstherefore the relation between these axioms and theorems deducible therefrom, i.e. thelogic provability relation.

f The formulas of OT are non-logical axioms of theories that might stand in a meta-level/objectlevel relation to each other. Important metaknowledge is therefore state-ments concerning the relation between di!erent layers of OT, so-called &&re#ectionrules''.


Relations within I¹f The formulas of OT represent only fragments of IT. Important metaknowledge istherefore statements that promote a coherent view of IT.

f The formulas of OT represent only certain abstraction levels of IT. Important meta-knowledge is therefore statements that describe IT at other abstraction levels that aremore suitable for user display.

7. Formalizing the system architecture

This section de"nes the overall structure of a formalization of the dual intelligenthypermedia model. This formal structure is to serve as a schema for composing suchsystems in a structured manner.

7.1. THE METATHEORY AND ITS INTERRELATIONS

Let us now outline how the relations between OT and IT are managed by MT. MT mustrepresent the deduction rules for OT and deal with the problem that OT is incomplete.That is, when OT is insu$cient for deducing a conclusion, MT is to invoke IT. This is tosee whether OT can be claimed to contain additional formulas, not identi"ed so far,corresponding to the user's interpretation of the informal domain knowledge in IT.Assume we want to know whether Formula is a logical consequence of OT. Let Demorepresent logic provability for OT, KnownFormula be the part of OT established so far,and;ser a formalization of the user's interpretation. Then the problem can be expressedas the following predicate.

Demo(KnownFormulasX;ser, Formula)

Note that OT"KnownFormulasX;ser is a static theory, although its subset ;ser isdynamically identi"ed (Sergot, 1983). In order to identify the subset ;ser of OT,reasoning with IT knowledge must be integrated in the problem-solving process. MTrelates OT to IT, and must thus represent this integration.

Incremental knowledge acquisition is consonant with monotonic classical logic aslong as the formalization is only extended with additional axioms, and no acceptedformula are afterwards excluded. The content of OT can be formalized as one demo unitclause for each particular formula f of OT, i.e. demo(O¹, f ). Let ot be a term denotinga set of formulas, then the model theoretical meaning for demo can be de"ned as the set ofpairs

MSot, f T3D2 Dot3D and f3D and P D"demo(ot, f )N,

where P is a metaprogram for demo and D is the domain of P, i.e. the set of all terms andformulas constructable in the language of OT. Alternatively, OT may be formalized asa data structure containing a set of formulas, e.g. a list or tree. A model theoreticalsemantics of predicate demo can then be attempted as the set of pairs

MSot, f T32D]D Dot32D and f3D and P D"demo(ot, f )N.

Regardless of the semantical characterization the content of OT is only partially known.The explicitly available knowledge can be understood as axioms. Implicit knowledgeforms two categories, such knowledge that follows logically from the available axioms

FIGURE 17. MT and its interrelations.


and additional axioms identi"ed by an external observer. Thus, to identify the implicitknowledge a metatheory is needed that both comprises logical inference rules for OT,and represents knowledge about the content of OT.

That OT's full content is not known a priori implies that neither is MT's. Predicates ofMT formalize the relations between OT, IT and the user. MT contains knowledge aboutwhat parts of IT should be used for identifying additional axioms of OT. Since the usergives the "nal decision in these matters, MT has an additional implicit part which isestablished at run time. Figure 17 describes the information #ow between the threetheories and the user.

7.2. DETAILING THE CONTENT OF MT

In MT, a logic program metainterpreter is needed for the inferencing in OT. Thismetainterpreter must be tailored to integrate the informal knowledge when needed forverifying the content of OT. For simplicity, we use a vanilla interpreter (which does notreify uni"cation) since a pure (ground) logic program metainterpreter would not reallyadd anything of explanatory value here.

A formula a can either already be part of OT or be established as a part of OT byconsulting the user for an interpretation of IT. Let ∀ be the universal closure symbol, i.e.it universally quanti"es all variables within its scope. The "rst argument of Demo, t1, isthe current OT, whereas t2 is the updated OT, the second argument a is the goal to beproved:

∀[M¹ (Demo(#1, a),#1)QO¹(t1, a)]

∀[M¹ (Demo(t1, a), t2)Q I¹(t1, a, t2)]

Note that a can also be a negated formula.


The following clauses simply represent and-introduction, or-introduction and modusponens for OT:

∀[M¹(Demo(t1, a AND b), t3)Q

M¹(Demo(t1, a), t2)'M¹(Demo(t2, b), t3)]

∀[M¹(Demo(t1, a OR b), t2)Q

M¹(Demo(t1, a), t2)s

M¹(Demo(t1, b), t2)]

∀[M¹(Demo(t1, a), t3)Q

M¹(Demo(t1, afb), t2)'M¹(Demo(t2, b), t3)]

A formula is already part of OT if it is a member of the data structure t1 representing thepart of OT identi"ed so far:

∀[O¹(t1, a)QMember(a, t1)]

When knowledge is not initially present as formulas in OT, it must be interpreted by theuser before being used in the reasoning, see the second clause. Below we present a clausethat takes the current OT theory and a formula as its input. If the formula is accepted asan interpretation of IT knowledge, it then returns an updated OT theory. Note that theuser can verify formula

}in, replacing it by true, or state that it is equivalent to another

formula, formula}out:

∀[I¹(t1, formula}in, t2)Q

Accepted}

from}I¹( formula

}in, formula

}out )'

;nion(t1, M formula}outN, t2)]

Advanced facilities are needed for dealing properly with the information in IT so as toguarantee satisfactory user cooperation. Accepted

}from}I¹ is responsible for present-

ing the context to the user, e.g. the necessary explanation. In the present paper, we areconcerned with system development and, in particular, with arguing for an adequateoverall system structure. Thus, we con"ne ourselves to a very simpli"ed example ofa relation between IT and OT.

The MT clause below relates a formula to an informal description which will begenerated on the user's demand:

∀[Accepted}

from}I¹( formula

}in, formula

}out)Q

formula}in"early

}symptoms(measles) f

(early}symp(‘heacking cough’) OR early

}symp(cold)) AND fever(high).

informal}description"

Generated when needed for each object. In Fig. 9 this is

exemplified for fever in relation to early symptoms for measles.

;ser}interpretation( formula

}in, informal

}description, formula

}out)]


Obviously, the clauses for ;ser}interpretation cannot be "nitely enumerated. First of

all, they will di!er depending on the current user. But even for one particular user, it isnot possible, not even in theory, to encode a priori his or her interpretation of a descrip-tion with respect to every conceivable fact situation since these are in"nite in number.Therefore, taking MT as a "rst-order theory presupposes that it is assumed to have animplicit part consisting of these clauses. This extension can be described in a metatheoryof MT. It is also convenient to encode metatheory knowledge in this metatheory,supporting the retrieval process for Accepted

}from}I¹ clauses, since, though "nite in

number, they may amount to a very large number in a full-blown system. From a systemdevelopment point of view, it is appropriate to implement both MT and its metatheorydeclaratively. However, it is beyond the scope of this article to detail these parts of thesystem. The interested reader is referred to Hamfelt (1996).

8. Related work

The topic of this paper is support for programming in the large for intelligenthypermedia systems. Since intelligent hypermedia systems are knowledge based it isimportant to relate to alternative or complementary approaches to support developmentof knowledge-based systems.

KADS (KBS Analysis and Design Support) and CommonKADS are tools supportingprogramming in the large for knowledge systems (Wielinga, Van de Velde, Schreiber& Akkerman, 1993; Schreiber, Wielinga, de Hoog, Akkermans & Van de Velde, 1994).These tools however are focused on knowledge modelling and do not prescribe anyparticular representation approach such as object-oriented or rule-based languages.CommonKADS promotes reusability in knowledge modelling by a library of so-calledmetamodels. In contrast to these tools, our approach is to promote reusability inknowledge representation by supplying the above program schemata for MT, OT andIT. These schemata are independent of the application domain but are de"ned at thesystem structure level for a particular family of systems (dual intelligent hypermediasystems). MT is to contain a library of various schemata for navigating in IT and forreasoning in OT. These schemata directly support knowledge modelling but also systemand program development.

Our approach and KADS (Wielinga et al., 1993; Schreiber et al., 1994) are inspired bythe same emphasis on structure in system development. KADS methods representa structured systematic approach to the initial system development phases, i.e. analysisand de"nition of requirements and design of system (Tansley & Hayball, 1993). Incontrast, our focus is on the subsequent phases of software design and implementationwhere we impose a certain system structure at a very general level.

Expert system shells support the development of formalizations of domain knowledgecorresponding to OT and provide inference machines for this object level. Second-generation expert system shells also support modelling of deep knowledge such asmodel-based reasoning (Steels, 1990). These shells may also support interface design butdo not facilitate the presentation of a more coherent IT (informal domain theory) andhave no metatheory MT for dynamically composing informal presentations re#ecting thecurrent stage of reasoning in the domain theory.


Our system architecture distinguishes three separate theories. Several authors considertwo theories, for instance Wenger (1987) who claims that two forms of knowledgerepresentation are needed in a knowledge communication system; the internal and theexternal representations. In an ideally &&intelligent'' knowledge communication system,the interface is strictly an external representation of the expertise that the systempossesses internally. This model corresponds directly to our theories IT and OT but itlacks the metatheory for relating the two representations.

Diaz, Aedo, Torra, Miranda & Martin (1997) propose a hypermedia training system tosupport di!erent styles of learning. There are two parts; the information element used inthe exercise (e.g. texts, images, animation and videos) and the logic of resolution whichmust be applied, also called strategy. The "rst can be compared with IT and the secondwith OT. Similar to our architecture, the system o!ers modi"cation of informationelements without altering the solving strategy and vice versa, the same knowledge can befocused from di!erent perspectives since information elements can be tied to di!erentsolving strategies, and the most appropriate strategy for each user can be selected sincethe same logic of resolution can be applied to distinct information elements. The relationbetween the strategy and the information objects is stored in the exercises base, whichthus corresponds to MT.

BreH zillon (1992) proposes that an explainable knowledge system comprises of anapplication knowledge base (AKB), an explanatory knowledge base (EKB), and a do-main-independent part called the Manager. The Manager performs the reasoning basedupon both knowledge bases and it synchronizes the two lines of reasoning. Furthermore,it handles the interaction with the user. The division of the system into three parts is quitesimilar to our approach, where OT corresponds to AKB, IT to EKB and MT to theManager. But it is not possible to really compare the two systems since the explanatoryknowledge base is not yet implemented.

The goal of the Dexter Hypertext Reference Model (Halasz & Schwartz, 1994)is to provide a basis for comparing systems as well as for developing interchangeand interoperability standards. The model provides a standard hypertext ter-minology coupled with a model of the important abstractions commonly foundin hypertext systems. The model exhibits certain features reminiscent of our systemarchitecture.

The Dexter model divides a hypertext system into three layers, the run-time layer, thestorage layer and the within-component layer. The storage layer models the basicnode/link network structure. This layer describes a database that is composed ofa hierarchy of components interconnected by relational links. This corresponds to ourMT. The within-component layer has no direct counterpart in our framework and is, bythe Dexter model, treated as being outside of the hypertext model. The storage layerstreat hypertext as an essentially passive data structure, whereas our MT allows inten-sional rules o!ering the possibility to perform deduction. In Dexter, the functionality forthe user to access, view and manipulate the network structures is captured by therun-time layer of the model which would correspond to the program that executes MT inour approach.

The Amsterdam Hypermedia Model (Hardman, Bulterman & Van Rossum, 1994)extends the Dexter model by adding to it the notions of time, high-level presentationattributes and link context. The temporal relationships among data items are partitioned


into two classes: those related to the identi"cation of the components that are to bepresented together (called collection), and those that relate to the relative order in whichthese components are presented (called synchronization). The principal di!erence fromthe Dexter model is that the composite components serve to build a presentationstructure rather than to simply collect related components for navigational purposes.This resembles the possibility in our framework to compute dynamically presentation ofIT knowledge.

Nanard and Nanard (1995) proposes a knowledge-based method for taking intoaccount the context of use of anchored texts. Factual knowledge about the documentsbase is "rst extracted from the documents by a user-interactive linguistic analysis rununder the control of a human expert. This knowledge is used to map a hypertextstructure, characterizing the semantics of the anchors, on the document base in order toeasily access semantically related information. In our terminology, this corresponds toa semi-automic extraction of MT knowledge.

N+rmark and "sterbye (1995) separate the internal hypertext representationfrom the external presentation similar to our MT and IT. This enables a conceptuallyclean internal representation as a common basis for a variety of tools for processinghypertext and an arbitrary number of di!erent presentations of the same internalrepresentation.

The Microcosm system (Hall, 1993) provides a sort of metahypermedia tool (Boyle,1997) characterized by a separation of links from the information stored in the nodes incontrast to traditional hypertext systems where the links are embedded in the docu-ments. In such an open hypermedia system the links may be processed separately as data.This corresponds to our separation of MT from IT.

In adaptive hypermedia, support can be provided at two levels*selection and pre-sentation of content at the node level, or navigational guidance at the link level (Boyle,1997). Both corresponds to MT reasoning for presenting IT but the former is a display ofconceptual context whereas the latter is inferential context.

In an early article, Rada and Barlow (1989b) claim that knowledge-based systems andhypermedia systems are #ip sides of the same coin insofar as they complement eachother. The former are designed to solve problems and the latter to convey informationand developments of each system type requires techniques from the other type.

9. Conclusions and further work

In large hypermedia systems the user is left to "nd his way around a highly complex andunde"ned structure, the hyperspace. E$cient access to the information content of sucha system requires more advanced guidance (Waterworth, 1992). Second-generationhypermedia systems provide such facilities mostly implemented drawing on techniquesfrom knowledge systems. Knowledge systems are designed to solve problems, whereashypermedia systems are designed to convey information. Knowledge systems and hyper-media systems are #ip sides of the same coin insofar as they complement each other(Rada & Barlow, 1989b). Developments of each system type requires techniques from theother type since both automatized reasoning and unguided navigation have theirinherent shortcomings. Automatized reasoning is incapable of dealing with full domain


theories and requires therefore support from the user for interpreting the unformalizableparts of the domain. To this end, the user needs access to informal descriptions of thedomain knowledge and the system's inferencing. These descriptions must be generatedon the #y since they re#ect arbitrary states of the reasoning. Hypermedia systems cano!er access to static informal descriptions but complemented with knowledge basedtechniques such descriptions can also be computed dynamically. Moreover, in a largehypermedia system the user is lost without a guidance support which can be naturallyimplemented with knowledge techniques.

Software projects bene"t from support methodologies for program and system devel-opment. An important component in such a methodology is a system architectureproviding reusable program and system module schemata. In the paper, we have takensome steps towards such an architecture for knowledge-based hypermedia.

As a basis for the system architecture, we propose that one should exploit the basicnotions involved in formalization, i.e. the formal object theory, an informal description ofthe domain and the metatheory where the two "rst are interrelated. We argue for theneed of considering the user's knowledge as an implicit part of the three theories whichmeans that at least the metatheory must be interactive. We emphasize the appropriate-ness of these basic notions for analysing hypermedia systems which explicitly deal withinformal knowledge descriptions and formalized knowledge in the form of links betweenthese. That is to say the metatheory describes relations between documents. Further-more, we argue that intelligent hypermedia is obtained by allowing these links to berepresented intensionally as rules rather than as explicit (extensional) relations. Wesurvey various programming methodologies for knowledge representation from theperspective of expressivity and tractability and conclude that non-ground metalogicprogramming seems to be the most reasonable choice today for implementing intelligenthypermedia systems. In addition, we distinguish between unary and dual intelligenthypermedia systems where the "rst type only exploits computer intelligence for navi-gation, whereas the second also deals with problem-solving in the domain described bythe documents in the hyperspace.

We point out that advanced knowledge representation techniques allow presentationof both conceptual and inferential context. The conceptual context is obtained bystructuring the documents by concept-based techniques which, e.g. allow the domainknowledge to be described at di!erent levels of abstraction, whereas the inferentialcontext is obtained as a description of the reasoning carried out to reach a certaindocument.

Finally, we consider the problem of developing a dual intelligent hypermedia systemfor a sample domain and based on these experiences we propose an implementation ofthe system architecture.

Empirical large-scale tests are needed for evaluating the proposed architecture. There-fore, in further work, we will carry out a full-#edged implementation of a system. Ourlong-term goal is a system architecture where we can o!er piecewise compositions ofintelligent hypermedia systems through diagrams giving graphical descriptions of theschemata of the architecture. This would eventually support also the knowledge-acquisi-tion phase since the system would support structuring of domain knowledge and evendisplay through its diagrams the knowledge for validation, cf. e.g. the Wisdom system forknowledge modelling (Lucardie, De Gelder & Helsper, 1997).


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Paper accepted for publication by Associate Editor, Dr A. Edman

a system architecture for knowledge-based hypermedia

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