=Paper=
{{Paper
|id=Vol-268/paper-8
|storemode=property
|title=Library-style Ontologies to Support Varying Model Views
|pdfUrl=https://ceur-ws.org/Vol-268/paper8.pdf
|volume=Vol-268
|dblpUrl=https://dblp.org/rec/conf/uai/Tabachneck-SchijfG07
}}
==Library-style Ontologies to Support Varying Model Views==
https://ceur-ws.org/Vol-268/paper8.pdf
Library-style ontologies to support varying model views
Hermi J.M. Tabachneck-Schijf and Linda C. van der Gaag
Department of Information and Computing Sciences, Utrecht University,
P.O. Box 80.089, 3508 TB Utrecht, The Netherlands
e-mail: {hermi,linda}@cs.uu.nl
Abstract In this paper we argue that to support model views for
varying tasks, a suite of Bayesian networks should be built
The next development in building Bayesian net- rather than a single network. We further argue that in the
works will most likely entail constructing multi- first step of developing such a suite, knowledge elicitation
purpose models that can be employed for vary- will necessarily result in task-specific information mostly,
ing tasks and by different types of user. We ar- although also some task-neutral knowledge may emerge.
gue that the development of an ontology to or- Structuring the elicited knowledge into a library-style on-
ganize the knowledge needed for such a multi- tology of task-specific and task-neutral modules then is best
purpose model is crucial for the management of suited to empower reuse of knowledge segments and to fa-
the model’s content. This ontology should pre- cilitate composition of model views. We reiterate our view
serve all elicited knowledge and be accessible to that this ontology should capture all elicited knowledge and
both domain experts and knowledge engineers. be accessible to domain experts and engineers alike.
Based on the different ways in which people We begin by defining different types of model view in Sec-
learn and gain expertise, we further argue that tion 2, and outline the task model view under discussion
knowledge elicitation will result in task-specific in the current paper. We argue that a single multi-purpose
knowledge mostly, although some task-neutral model would quickly become too large and unyieldy to af-
knowledge will emerge as well. To support vary- ford the knowledge engineers and the domain experts an
ing model views, this combination of knowledge overview of its contents. We therefore advocate building a
is best stored in a library-style ontology of task- suite of models to support multiple task model views, rather
specific and task-neutral modules. than a single Bayesian network.
In Section 3 we outline our view of ontologies. We rational-
ize why an ontology should be constructed of the elicited
1 Introduction knowledge, before actually developing a suite of Bayesian
networks. This rationalization is much in line with our
While in the early years of the field of Bayesian networks
earlier arguments for developing ontologies for single net-
attention focused primarily on algorithmic issues, the last
works [9]. The ontology provides as a well-structured doc-
decade has seen an increasing interest in methods to sup-
umentation of all elicited knowledge and includes also any
port the construction of such networks. The field also has
background information that is not captured explicitly in a
become more and more experienced in building decision-
network. This background information supports, for exam-
support systems that include a Bayesian network. Bayesian
ple, viewing the elicited knowledge from different perspec-
networks by now have evolved beyond laboratory settings
tives, as required for different tasks. The well-structured
and are being employed by non-academic users. In turn,
documentation then scaffolds the building of different task
users of these network-based decision-support systems are
model views for a suite of Bayesian networks.
starting to see the possibilities that these systems offer, and
begin to ask for more. For example, for various of our We are not the first to suggest the use of ontologies. Ontolo-
biomedical applications, we have been asked whether we gies are being developed for a variety of purposes, ranging
could perhaps adapt the model for teaching purposes. It from providing a portal for the semantic web to document-
is therefore likely that the next development in the field of ing elicited knowledge for the development of knowledge-
Bayesian networks will entail building multi-purpose mod- based models; see for example [4, 6, 8, 18]. For many
els which can be employed for varying tasks and, in all of these purposes, a rigorously formal logic-based or other
likelihood, by varying types of user. mathematical ontology language is used to allow for auto-
mated processing. For our purpose of supporting the devel- structure of the suite of models to be developed, and then
opment of a suite of networks by well-structured documen- representing this knowledge in the mathematical formal-
tation, however, the ontology should provide as a medium ism of Bayesian networks. The final step is characterized
for communication between the engineers and the experts by designing interfaces to the suite of models, that is, the
involved in the suite’s construction. Based upon the obser- different ways the models can be presented to someone in-
vation that a rigorously formal language is not easily ac- teracting with it, be this an engineer or an end-user.
cessed by non-mathematical experts, we advocate, in Sec-
We consider a task model view to be one view of a suite
tion 3, the use a less formal language for our ontologies.
of models. The task model view is the result of carry-
We address the knowledge content of our ontologies in Sec- ing out the elicitation and structuring of task-neutral and
tion 4. In order to align the content of our ontologies with task-related domain knowledge and of making selections of
elicited knowledge, we consider the processes by which the elicited knowledge to support a single or a few closely
humans learn and structure their own knowledge. We ob- related tasks. In the medical field, for example, one task
serve that the elicited professional knowledge of practic- model view might support diagnostic reasoning, while an-
ing experts is mostly both task- and domain-specific, al- other task model view could support teaching diagnostics,
though also some task-neutral information may emerge which requires additional modeling of underlying mecha-
during knowledge elicitation. nisms so that deeper ‘why’ and ‘what if’ questions can be
posed and answered. Interaction model views, on the other
In Section 5, we argue that knowledge is best stored in the
hand, comprise the interfaces of a model that are tailored to
fashion in which it is obtained from the experts. We fur-
task and user. For example, for a diagnostics model view,
ther argue that the elicited knowledge is best organized into
one interaction model view could be optimized for data en-
modules. An organization of knowledge in modules is well
try and another might support maintenance of the model by
suited for storing task-specific knowledge to support mul-
the knowledge engineer.
tiple tasks. Organizing the modules in a library-style on-
tology further encourages reuse of the knowledge elicited In sum, for different tasks to be carried out by differ-
for one task model view for the construction of another ent types of user, a suite of models can require several
task model view. We would like to note that in our ear- task model views, each of which can need several inter-
lier work we proposed the development of a meta-library action model views. In last year’s workshop, we laid
of generic knowledge structures complemented with ex- out some methods to construct effective interaction model
ample network derivations [11]. To support the evolve- views [16]. In the current paper, we concentrate on the elic-
ment of an ontology for a suite of Bayesian networks, such itation and structuring of knowledge, in order to support the
generic knowledge structures can guide and speed up en- development of multiple task model views.
tering knowledge into the various modules.
The paper ends with a discussion and some perspectives 3 Ontologies for Bayesian networks
for further elaboration of the presented ideas to a practica-
ble knowledge-engineering approach to developing multi- A suite of Bayesian networks that supports several tasks
purpose Bayesian networks. with different task model views, is likely to be of a com-
plexity necessitating development over multiple years, in-
volving possibly different engineers and experts. Build-
2 Model views of Bayesian networks ing and maintaining models of such complexity is a hard
and time-consuming process. The knowledge elicited from
We distinguish two types of model view, namely task model domain experts constitutes a rich pool of knowledge, seg-
views and interaction model views. To explain the differ- ments of which can play varying roles in the domain under
ence between the two types, we distinguish three different study. All this elicited knowledge has to be carefully re-
states in the development of a suite of models. The first viewed and structured, and ultimately captured in the for-
state consists of a stored pool of knowledge relevant to all malism of Bayesian networks. In this process, a multitude
tasks to be carried out. The second state encompasses the of modeling decisions are taken as well as numerous deci-
actual suite of models that allows computations to be car- sions to demarcate the scope of the model. Such decisions
ried out for the various tasks. The third state comprises tend to forestall an overview and thorough comprehension
concrete means that allow users to work with the suite of of the model by anyone who has not been intimately in-
models. In view of these three states, we also consider the volved in its construction. We have experienced already for
steps that need to be taken to proceed from one to the next single larger networks, that construction and maintenance
state. The first step reaches the first state and involves elic- are seriously hampered if the elicited domain knowledge
iting and structuring knowledge. The second step neces- and the decisions taken are not made explicit by proper doc-
sitates first selecting, from the pool of all elicited knowl- umentation [9]. This problem is bound to grow worse if a
edge, the knowledge that determines the content and the suite of networks is to be developed and maintained.
Having observed the advantages of developing an ontol- mal representation in addition may provide for (semi-)au-
ogy before building a single Bayesian network in our ear- tomated derivation of segments of the Bayesian networks
lier work [9], we feel that the construction of a suite of under construction. While rigorously formal languages
models will especially benefit from an explicit ontology, often have limited expressiveness, an ontology language
which then serves not just as a documentation of all elicited should come with a rich semantics to introduce as little bias
knowledge but also as a means of ensuring consistency over as possible in the represented contents. If the language in-
the models within the suite and as a medium for communi- troduces biases, for example as a result of not allowing the
cation between the experts and engineers involved. representation of specific knowledge constructs, then the
ontology may not properly reflect the intricacies of the do-
3.1 The role of ontologies main. Since the ontology is to be used for the construction
of a network, the resulting model may then be biased as
Most generally applicable knowledge-engineering method- well, maybe even in unforeseen ways. The development of
ologies, among which is the well-known CommonKADS an independent knowledge model, recommended by most
methodology [13], strongly recommend the development knowledge-engineering methodologies, in fact has its ori-
of a conceptual model before actually constructing a model gin in this observation.
in the knowledge-representation formalism to be used. In
The purpose of knowledge sharing provides a strong argu-
line with this recommendation, we recently proposed to de-
ment for using a less formal language. The ontology should
velop an ontology before constructing a Bayesian network
be represented in a language that is understandable for both
for a domain at hand [9].
the knowledge engineers and the domain experts involved
There exist many views of the concept of ontology in gen- in a network’s construction. We argued before that the
eral; see for example [4, 7, 8, 18]. In this paper, we use mathematical language of Bayesian networks, for example,
the term ontology to refer to an explicit specification of the is very difficult to grasp by non-mathematical persons [16].
elicited domain knowledge that is to be shared by the ex- In our opinion in fact, many of the formal languages com-
perts and the knowledge engineers involved in a network’s monly used for ontologies are unsuitable for checking the
construction and maintenance. From this perspective, an accumulated knowledge with non-mathematical experts. If
ontology plays two distinct roles. One of these is to make the use of a formal language is uncommon in a domain of
all elicited domain knowledge explicit. To this end, the application, then a rigorously formal language is unsuited
ontology specifies not just the knowledge that is to be cap- for the purpose of knowledge sharing between the knowl-
tured in a network, but also the relevant background knowl- edge engineers and the domain experts in the domain at
edge of the domain and the meta-level knowledge of its hand and a less formal language had best be used.
regularities and organizational structure. Note that captur-
To support developing Bayesian networks in the biomedi-
ing the elicited knowledge directly in a Bayesian network
cal domain, we use a semi-formal ontology language com-
would result in a representation from which not all types
posed of well-structured tables, depictions, graphs and hi-
of domain knowledge are easily recognizable as a result of
erarchy representations combined with text [9], which can
the modeling decisions taken. Also, some of the elicited
be understood by both the domain experts and the knowl-
knowledge may not be captured at all in the network. The
edge engineers. As an example, Figure 1(a) shows part of
other main role of an ontology is to provide as an explicit
an ontology for the medical domain of oesophageal can-
medium for communication between experts and engineers
cer. The depicted graph captures the relationships between
alike for further knowledge acquisition, network validation
the result of a gastroscopic examination of the circumfer-
and maintenance.
ence of a patient’s tumour and the underlying true circum-
ference. It describes, for example, that a gastroscopic ex-
3.2 The ontology language and an example
amination may not result in an image from which the cir-
To support the two roles mentioned above, the representa- cumference can be established, as a result of a patient’s
tion language to be used for an ontology should be chosen impaired swallowing capabilities.
with care. The issue of selecting an appropriate ontology Upon establishing the stage of a patient’s cancer, not only
language has been addressed by many researchers. Some the circumference of the primary tumour is investigated.
suggest that domain knowledge should be represented by Other diagnostic tests are performed as well. In addition
a language that is highly informal, semi-informal, or semi- to the knowledge pertaining to these tests separately, the
formal [18]; others argue that ontologies should be speci- domain’s ontology specifies the high-level regularities of
fied in a rigorously formal language and, in fact, should be the knowledge involved. The graph capturing these regu-
machine readable [14]. larities for the various diagnostic tests is depicted in Figure
An important argument for using a formal ontology lan- 1(b). Note that this graph can be exploited upon extending
guage is that it allows a highly structured and unambigu- the network with the results of a new test, as it provides
ous representation of the elicited knowledge. Such a for- for guiding the elicitation of the knowledge pertaining to
oesophageal tumour inducing gastroscopic image inducing result gastroscopy
circumference circumference circumference
value value
inducing enabling enabling
enabling laboratory technician
test skills gastroscopy
passage impairment enabling physician enabling
degree interpretation skills gastroscopy
gastroscopy interpretation gastroscopic image
status status
(a)
entity inducing presentation inducing result
property value value
inducing enabling enabling enabling
test technician
test skills
manifestation enabling interpreter enabling
degree interpretation skills
test interpretation
status status
(b)
Figure 1: Relations between test results and the underlying true values, (a) for a gastroscopic examination of the circum-
ference of an oesophageal tumour, and (b) for a diagnostic test in oncology in general
the new test. For further details of the oesophageal cancer that adheres to the syntax of Bayesian networks. To this
ontology, we refer to [9]. end, the domain concepts from the depiction are translated
into stochastic variables, which may involve for example
re-defining multi-valued variables. The relations from the
3.3 Ontology-supported construction of networks
depiction are translated into arcs between variables in the
initial graphical structure. Note that many of these steps
Of course it is a daunting prospect to have to capture all
can be performed in an automated way. Figure 2 shows,
elicited knowledge in two ways, that is, first in an ontol-
as an example, part of the initial graphical structure that is
ogy and then in a suite of Bayesian networks. A carefully
derived from the graph of Figure 1(a). In the final step, the
structured ontology, however, can be used to derive the
engineer has to verify that the resulting structure correctly
graphical structure of the suite in a semi-automated fash-
captures probabilistic independence. Also, the initial struc-
ion. First, the knowledge that is to be captured in the suite is
ture may need further optimization [10].
selected from the ontology; the remainder of the ontology
then serves as background knowledge to the suite. Note
that this step involves a reflection on the elicited knowledge 4 Eliciting ontology knowledge
which must be performed and documented by the knowl-
edge engineer. In the next step, the central concepts and Given the prospective advantages of constructing a domain
relations from the selected parts of the ontology are com- ontology before building a suite of Bayesian networks, we
bined into a single depiction for each envisioned network. now turn to the question of how to organize the elicited
From this depiction, an initial graphical structure is derived knowledge in the ontology so that it most usefully supports
different task model views for the suite.
Gastro-image- Many researchers recommend that ontologies be con-
Circumference circumf Gastro-circumf structed independently of the projected use of the ontology
and its contents; see for example [3]. Underlying this rec-
ommendation is the argument that any commitment to the
Passage Test-skills Interpretation-skills
problem-solving method that will be applied to the domain
knowledge for example, will influence and thereby bias the
Figure 2: The initial segment of the graphical structure contents of the ontology. Such commitments thus hamper
the extendibility and reuse of the ontology. However, con-
structing an ontology without any commitments to a par-
ticular task requires either eliciting task-neutral knowledge
from domain experts, or stripping the task-specific aspects
from the elicited knowledge. In this section, we address the
feasibility of the first option; the second option is briefly
addressed in Section 5.
We consider eliciting task-neutral information, that is, elic-
iting knowledge from experts without them having a par-
ticular task in mind. To provide task-neutral information,
experts should be able to gather such information from
their minds, which implies that the knowledge should be
stored in their brains in such a way that task-neutral as-
pects are readily separated from task-specific aspects. We
now briefly lay out the different ways in which people learn
information and argue that these learning processes imply
that the knowledge stored in the human brain is largely
both domain- and task-specific. We then conclude that,
given how knowledge is learned and stored, it would be Figure 3: Knowledge acquired by different processes
extremely difficult to elicit task-neutral knowledge from an
experienced professional.
in particular situations, accreted knowledge is largely both
task- and domain-specific. Figure 3 summarizes the four
4.1 Human knowledge acquisition processes
processes by which humans acquire knowledge.
Humans acquire knowledge in four different ways: trans-
mission, acquisition, accretion, and emergence [19]. Usu- 4.2 Example: the acquisition of medical knowledge
ally people start gathering professional knowledge from
While the four learning processes reviewed above relate
books and teachers: the knowledge is explicitly transmit-
to general educational practices, they are easily mapped
ted to them. Except in vocational training, such trans-
onto what happens in the course of gathering professional
mitted knowledge is mostly task-neutral. Over the course
knowledge. Although the exact percentages may vary a
of a lifetime, transmission accounts for some 10% of our
little, the different processes will create roughly the same
knowledge. Further learning done by conscious choice
proportions of the knowledge that our domain experts pos-
is termed acquisition learning, which is good for about
sess. We illustrate this observation with an example from
20% of our knowledge. Acquired knowledge is gathered
medicine [2], and also argue that transmission and acquisi-
by our own initiative: by exploring, experimenting, self-
tion learning in college does not prepare a student for med-
instruction, inquiry and the like. Emergence is the result of
ical practice, because of the task-neutral nature of the ma-
self-constructing new ideas and meanings that did not ex-
terial learned in medical school.
ist before, which in current educational practices is said to
account for just 1-2% of our knowledge. The basics for medical knowledge are taught by transmis-
sion in universities. This type of knowledge is explicitly
When people are asked to describe learning processes, they
task-neutral and consists of biomedical knowledge, which
generally mention explicit processes akin to transmission
is mostly causal and definitional in nature and describes
and acquisition, and perhaps emergence. Accretion, which
the functioning and possible dysfunctioning of the human
accounts for about 70% of what we know, however, does
body. It is this transmitted knowledge that upon elicitation
not commonly come to mind. Accretion is the gradual, un-
would result in task-neutral knowledge segments.
conscious and implicit process by which we learn for exam-
ple language, culture, social behavior, and whatever other Next, students are confronted with patients in internships,
knowledge comes on our path. Accretion knowledge is where they have to link the transmitted task-neutral infor-
picked up simply by living and interacting with the world. mation to clinical knowledge. In contrast to biomedical
Within limits, we process and react to all we see, hear, knowledge, clinical knowledge is task-specific in nature.
smell, taste and experience. By processing the information It consists of knowledge of symptoms, classification and
and reacting to it, it is stored in the brain without our be- treatment of diseases, all embedded in medical situations.
ing conscious of the learning process. People consequently In internships, some transmitted information is still offered,
often are not even aware they possess this type of knowl- but students are also acquiring knowledge by trying to fig-
edge. Because it is unconsciously experienced and learned ure out diagnoses and treatment plans themselves. Accre-
tion then is also at work, continually recording knowledge comes to the fore upon elicitation is task- and domain-
from all perception instruments. Examples of accreted specific. Of course an engineer can explicitly ask a do-
knowledge are how to read symptoms from patients’ look, main expert to provide task-neutral knowledge. If experi-
smell, utterances and behavior, and how to communicate ence from practice is requested, however, the engineer is
with colleagues, patients and their next of kin, yet also how asking for extra information processing from the expert:
to get around in the hospital and many other aspects of the expert has to relate his or her knowledge in a differ-
work. All that is learned is now embedded in the task at ent way than is stored in the brain. This, as argued in
hand and in the medical culture and practices. In cognitive- the example above of the medical students’ transition from
science terms, the knowledge is situated. book knowledge to diagnostic and treatment knowledge,
requires non-trivial effort, which, as it is to be done real-
It is taking the step from employing task-neutral knowledge
time, will at least considerably slow down the elicitation.
in college to having to apply task-specific knowledge in a
More potentially damaging, however, asking people to re-
hospital setting that makes the transition from the univer-
lay knowledge in a way that requires them to reason about
sity classrooms to practice so problematic for many medi-
their stored knowledge, as is done when asking an expert
cal students [2]. Students may have learned which disease
for task-neutral information, always increases the risk of
causes which symptoms, and maybe even have seen pic-
introducing errors [5]. We conclude that, except for infor-
tures of such symptoms. However, recognizing the symp-
mation that was transmitted in a task-neutral fashion, it will
toms when exhibited by a patient is a very different matter.
be difficult, time-consuming and error-prone to try to elicit
Each patient is unique, and may or may not exhibit all of the
task-neutral knowledge from domain experts.
symptoms. Patients also may exhibit symptoms differently.
Patients may further have more than one disease, which Two examples from our own research will serve as illus-
may result in an indistinct mixture of symptoms. Last but trations. As a first example, when we asked veterinarians
not least, the reasoning required now goes diagnostically to supply us with average disease symptoms for pigs that
from symptoms to disease, not causally from disease to were sick, most of them provided us with symptoms be-
symptoms. The difficulty of this re-representation is sup- longing to one particular illness rather than a context-free
ported by research in various other contexts, from which average; some gave symptoms associated with a particu-
it is also clear that switching information from one repre- lar group of closely related diseases such as infections of
sentation to another is very difficult. Switching represen- the respiratory tract. What happened is that the veterinar-
tations, in fact, does not occur spontaneously and must be ians called a pig having a particular disease to mind, of
explicitly and extensively taught [1, 17]. which they provided the symptoms. The veterinarians pro-
viding a few more symptoms ostensibly generalized but ac-
Professional learning in medicine does not stop with the
tually were doing exactly what their colleagues did: they
internship phase. It continues by a mixture of accretion
provided the symptoms of diseases encountered within the
and acquisition during the entire professional career. All
same differential diagnosis. The veterinarians unwittingly
knowledge picked up in this phase is in a task-specific for-
rendered their knowledge in the same situated way it was
mat, because it is learned while carrying out specific tasks.
stored, rather than following our instructions.
The theory of situated learning describes this phenomenon
and argues that learning as it normally occurs is a func- As a second example, we relate a knowledge-elicitation
tion of the activity, context and culture in which it occurs session where we asked a group of veterinary experts to
[12, 15]. In fact, the theory argues specifically that learning reason out loud about particular pig cases of which the clin-
never occurs in a task-, context-, and culture-neutral man- ical symptoms were described in terms of variables and
ner.1 In a physician, for example, interaction with patients values. When asked what would happen to their assess-
is typically stored as examplars of sick people complete ment of the case when a particular symptom was changed
with diagnosis, treatment plan, and outcomes. from present to absent, one of the participants asked, in
earnest, how he could possibly change the symptoms of a
From the above observations, we conclude that the bulk
pig. Clearly, the veterinary expert had called the case to
of the professional knowledge of an expert is stored in the
mind as a concrete pig for which he had to come to a di-
mind in a task-specific format.
agnosis. Thinking in this task-related setting, he could not
imagine physically changing a pig’s symptoms.
4.3 Eliciting task-specific knowledge
Since professional knowledge is largely task-specific, it
is reasonable to assume that most of the knowledge that 5 Storing the elicited knowledge
1
According to this theory, the knowledge transmitted in med-
ical school is also not task-neutral: the task is passing the exam. Having established that it will be rather unlikely that an
For our purpose, however, the issue is that the knowledge is inde- engineer will elicit knowledge from a domain expert that
pendent of specific medical tasks. is altogether task-neutral, we now address how the elicited
knowledge is best stored in an ontology. More specifically, From the above observations, we conclude that although
we compare constructing a single task-neutral ontology storing knowledge in a task-neutral fashion is prefered,
that is free of task biases, with constructing multiple task- it is infeasible to do so for the bulk of elicited informa-
specific ontologies. We then argue that a library-style on- tion. Some of the elicited knowledge may be available as
tology best supports the development of a suite of Bayesian task-neutral information, however, for example if originat-
networks for multiple tasks. This library-style ontology is ing from the transmission phase of learning professional
composed of various modules that are task-specific as well knowledge. Also, some of the elicited information can
as domain-specific, supplemented with modules that are ei- be abstracted to segments of task-neutral knowledge. An
ther task-neutral or domain-neutral. example from our veterinary applications pertains to the
stress effects of handling a pig. Catching a pig will cause
stress to the animal, regardless of the task for which it is
5.1 Single or multiple ontologies being caught. The knowledge elicited in the contexts of the
various tasks thus is explicitly reusable and can be stored
We begin by comparing capturing all elicited knowledge in in a task-neutral fashion.
a single task-neutral ontology or in multiple task-specific
ontologies. For the construction of a single ontology, be it
composed of task-neutral or task-specific knowledge, plead 5.2 A library of ontology modules
that no duplication is needed and that it will be easier to en-
sure internal consistency upon maintenance and extension. Alternative to either a single task-neutral ontology or a
In spite of these advantages, however, we reject building a collection of multiple task-specific ontologies as discussed
single ontology. A single ontology is likely to become quite above, is a library consisting of multiple ontology modules.
large in size for a suite of Bayesian networks supporting Some of the library’s modules contain background knowl-
multiple task model views. Even if it is well organized and edge that is common to all tasks in the domain under study
highly structured, its mere size will cause the knowledge yet independent of a specific task. Other modules contain
engineers and the domain experts to quickly lose track of knowledge that is common to one task but holds across do-
its contents. Another argument against the construction of mains; the graph from Figure 1(b), in fact, showed a seg-
a single ontology is that it may be much more difficult to ment of such knowledge, pertaining to the interpretation of
build multiple task model views from a single entity than the results of diagnostic tests in biomedicine. The majority
from a collection of task-focused entities. of the modules, however, capture knowledge that is both
task- and domain-specific. A segment of knowledge may
Having rejected developing a single ontology, we now ad-
thus be captured in more than one module, described from
dress the format of the ontology’s content. There are quite
the varied perspectives of different tasks. A task-specific
strong arguments for storing knowledge in a task-neutral
ontology aimed at supporting a particular task model view,
fashion. Task-neutral knowledge need not be captured mul-
then is constructed by combining various modules.
tiple times for use for varying tasks, as would be required
if the knowledge were captured in a task-specific fashion. We illustrate the concept of a library-style ontology using
Also, when new task model views need be developed, it is our earlier example in medicine. A library of modules for
likely that these can already be supported using the avail- medical applications would include, for example, anatom-
able task-neutral knowledge. If the knowledge would have ical knowledge. Anatomical knowledge is descriptive and
been stored in a task-specific fashion, developing a new definitional in nature and summarizes the elements of the
task-specific ontology would be required. human body. Anatomical knowledge is common to most
medical tasks yet is independent of any specific task. In
Although there are strong arguments for storing the elicited
the library, it would therefore be included in one or more
knowledge in a task-neutral fashion, it generally will be
task-neutral ontology modules. Knowledge of which dis-
highly infeasible to do so. In Section 4, we argued that the
eases typically occur in the differential diagnoses of which
bulk of the elicited knowledge will be available in a format
other diseases is closely linked to the task of diagnosis, and
that is both task- and domain-specific. Constructing a task-
would be included in a task-specific ontology module for
neutral ontology would thus require stripping the elicited
diagnostic tasks. Note that gradations of task specificity
knowledge from its task biases and integrating the result-
may be supported. Knowledge of the relationships between
ing segments of neutral knowledge. The task of stripping
diseases and symptoms, for example, is common to both
the elicited knowledge from its task-related context is non-
diagnosis and prognostication, and could be included in a
trivial, however. Our opinion in fact is that it is infeasible
single ontology module subserving both tasks.
since not just the experts but also the engineers will have
particular tasks in mind when surveying the various seg- To construct a concrete task-specific ontology for support-
ments of knowledge. The engineers moreover are likely to ing a model view of teaching diagnostics, information from
be insufficiently knowledgeable in the domain of applica- the task-neutral modules of anatomical knowledge would
tion to recognize the various task biases included. be pulled in as well as information from modules related to
Figure 4: A library-style ontology for developing task model views: the library of ontology modules is supplemented
with a library of generic knowledge structures and a document of modeling decisions; drawn arcs indicate instantiation of
modules, dashed arcs indicate selection
the tasks of diagnosis and prognostication. The modules of 6 Concluding observations
anatomy and prognostication would then subserve simula-
tion purposes and answering in-depth ‘what-if’ questions.
In this paper, we argued that multiple task model views for
Note that the other, unrelated modules of the library need
Bayesian networks are best supported by a library-style on-
not be considered upon constructing the task-specific on-
tology composed mainly of task-specific knowledge mod-
tology. For supporting a model view of diagnosis, on the
ules, but also including task-neutral modules.
other hand, the knowledge from the task-neutral modules of
anatomy would most likely not be included explicitly in the In summary, this paper addressed several issues. We began
task-specific ontology, as the model to be developed could by reiterating the need for documenting all elicited knowl-
leave this knowledge implicit. Now suppose that an ontol- edge. If this knowledge is not properly documented, con-
ogy for the new task model view of predicting the effects of struction and maintenance of large suites of networks in-
treatment is to be developed. Any task-neutral knowledge evitably becomes problematic. We recommended building
required for the new model view ideally is already present an ontology to provide a well-structured explicit specifi-
in the library and can be readily pulled in. Also the on- cation of the elicited knowledge and a medium for com-
tology module of prognostication, which is already present munication for the knowledge engineers and the experts
in the library, captures some of the knowledge for the new involved in the networks’ development. We argued that
task and can be used. In addition, however, a new task- the ontology should not only store the knowledge needed
specific module needs to be developed and included in the for the different model views, but also any relevant back-
library. The knowledge for this new module, describing the ground knowledge; in addition, a modeling-decisions doc-
physiological effects of treatment, is elicited from domain ument should be maintained. Documentation of the infor-
experts, focusing on just the task at hand. mation that cannot be read off the suite of networks directly
is especially important when the development of the suite Boshuizen, T. de Jong (eds). Learning with Multiple
extends over several years of research and the suite ulti- Representations. Amsterdam: Elsevier Science, Ch.
mately is handed off to industry. 8, pp. 137 – 152.
The paper also attended to the language to be used for [2] H.P.A. Boshuizen, M.W.J. van de Wiel (1998). One
our ontologies. The necessity of including all types of person, multiple representations: An analysis of
relevant knowledge demands a language that allows for a a simple, realistic multiple representation learning
rich semantics and permits semi-automated model build- task. In: M.W. van Someren, P. Reimann, H.P.A.
ing. We stressed that the language used should be accessi- Boshuizen, T. de Jong (eds). Learning with Multiple
ble for non-mathematical domain experts. Earlier research Representations. Amsterdam: Elsevier Science, Ch.
had shown that rigorously formal representations, be they 12, pp. 237 – 263.
logic-based or stated in another mathematical language,
cannot readily be understood by domain experts who are [3] T. Bylander, B. Chandrasekaran (1988). Generic tasks
not trained in such representations. When stated in a semi- for knowledge-based reasoning: the ‘right’ level
formal language that is accessible for the experts, the on- of abstraction for knowledge acquisition. In: B.R.
tology can provide as a means of communication between Gaines, J.H. Boose (eds). Knowledge Acquisition for
the knowledge engineers and the experts, which serves to Knowledge-based Systems, vol. 1. Academic Press,
minimize the risk of omitting important information and of London, pp. 65 – 77.
including erroneous information.
[4] P.C.G. da Costa, K.B. Laskey, K.J. Laskey (2005).
Next, we pled for aligning the content of the ontology with PR-OWL: A Bayesian ontology language for the Se-
how practicing experts learn and store knowledge in their mantic Web, International Semantic Web Conference,
minds. Some knowledge, we argued, is stored in a task- Workshop Uncertainty Reasoning for the Semantic
neutral fashion, and should also be stored in this way in Web, Galway, pp. 23-33.
the ontology. However, we contended that most knowledge
of domain experts is inherently related to specific tasks [5] K.A. Ericsson, H.A. Simon (1993). Protocol Analy-
and is stored in that way in their brains. Constructing a sis: Verbal Reports as Data. MIT Press, Cambridge,
task-neutral ontology would thus require stripping the task- MA.
specific professional knowledge from its task biases. This,
[6] T.R. Gruber (1993). A translation approach to
however, is highly demanding, either on the part of the ex-
portable ontologies. Knowledge Acquisition, vol. 5,
pert or on the part of the knowledge engineer, and error-
pp. 199 – 220.
prone. We therefore proposed storing task-specific knowl-
edge in a task-specific fashion. [7] Th.R. Gruber (1995). Towards principles for the de-
Lastly, we proposed to develop a library-style ontology, sign of ontologies used for knowledge sharing. In-
composed of the aforementioned task-neutral and task- ternational Journal of Human-Computer Studies, vol.
specific knowledge modules which subsequently are com- 43, pp. 907 – 928.
bined into task-specific ontologies to support concrete task [8] G. van Heijst, A.Th. Schreiber, B.J. Wielinga (1997).
model views for a suite of Bayesian networks. We il- Using explicit ontologies in KBS development. Inter-
lustrated the ease of development of multiple views and national Journal of Human-Computer Studies vol. 46,
demonstrated that reuse of information is encouraged by pp. 183 – 292.
organizing the domain knowledge in modules.
[9] E.M. Helsper, L.C. van der Gaag (2002a). A case
In the near future, we intend to further develop our con-
study in ontologies for probabilistic networks. In: M.
cept of ontology library by using it in the development of
Bramer, F. Coenen, A. Preece (eds). Research and
a suite of Bayesian networks in the field of veterinary sci-
Development in Intelligent Systems XVIII. Springer-
ence. By doing so, we hope to initiate a publicly available
Verlag, London, pp. 229 – 242.
collection of ontology modules and inspire the uncertainty
community to contribute. [10] E.M. Helsper, L.C. van der Gaag (2002b). Building
Bayesian networks through ontologies. In: F. van
Harmelen (editor). Proceedings of the 15th European
Conference on Artificial Intelligence. IOS Press, Am-
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