Clarification KBS as Consultation-Justification Mash Ups
Proposing a Novel Paradigm for All-in-One Knowledge-based Systems
Martina Freiberg, Felix Herrmann and Frank Puppe
Department of Artificial Intelligence and Applied Informatics, Institute of Computer Science,
University of W
urzburg, Am Hubland, W
urzburg, Germany
Knowledge-based System, Clarification KBS, Consultation-Justification Mash Up, KBS UI Design, Agility.
Regarding knowledge-based systems (KBS), the seminal paradigm—perfectly mimicking human experts—is
gradually replaced by an increasing demand for enabling users to influence the reasoning process according to
their domain knowledge. Therefore, we propose a novel KBS paradigm: Clarification KBS as a mash up type
of consultation and justification interaction—intended to foster active user participation according to users’
competency, the KBS’ explicability, and the support for learnability. We introduce the theoretical concept of
clarification KBS, as well as appropriate UI-/interaction variants. Further, we discuss the results of iteratively
evolving and evaluating ITree, a specific clarification KBS implementation for the legal domain.
Today, knowledge-based systems (KBS) are widely
applied, both in research-based and industrial
applications—e.g., for medical documenta-
tion/diagnosis, technical fault diagnosis, or envi-
ronmental advice (Castellanos et al., 2011; Ting
et al., 2011; Chen et al., 2012; Zeng et al., 2012).
Yet, the once common paradigm, that a KBS should
perfectly mimic the abilities of human experts, is
gradually replaced—by the increasing demand for
enabling users to influence the reasoning process
according to their expertise, e.g., by answering expert
shortcut questions. Another desirable objective for
such KBS is to provide a high level of skill-building
ability—i.e., to enable users to gain knowledge about
the domain by using the KBS.
A typical KBS session commonly conforms to an-
swering questions that are queried by the system—
other KBS types, such as embedded KBS, also exist
but are not targeted here. Based on the input data, the
KBS typically derives one or more solutions; there, it
is essential to also explain how and why the respective
conclusion was drawn, i.e., providing a justification.
Justifications basically support reproducing and re-
viewing the KBS’s operation and conclusions (Puppe,
1993)—thus, apart from leveraging the KBS valida-
tion task, justifications help to build trust in the KBS,
enable users to acquire domain knowledge, and thus
enhance the overall user experience and utility. So far,
handling the data input and presenting the results and
corresponding justifications mostly are treated sepa-
rately. In this paper, we motivate that mashing up
the consultation and justification interaction can be
highly beneficial: Resulting KBS offer a high level
of explorability and freedom to the user; that in turn
encourages users, to bring in their own competency
and expertise; finally, the anytime provided justifi-
cation, offered by such mashup KBS, strongly fos-
ters the explicability and thus learnability of such a
system. Today, diverse research is found regarding
explanation generation and their use/utility (Arnold
et al., 2006; Pinheiro et al., 2006); however, explana-
tion/justification presentation, general KBS UI design
and (usability) evaluation are still rather neglected.
Specifically for clarification KBS, except our semi-
nal work (Freiberg and Puppe, 2012) no similar re-
search efforts are published so far. Therefore, as a
generalization of our prior work, we propose clar-
ification KBS as a consultation-justification mashup
type in this paper.
The remaining paper is organized as follows: In
Section 2, we propose the theoretical concept of clar-
ification KBS as consultation justification mashups.
Appropriate UI representations are presented in Sec-
tion 3. We report various evaluations in Section 4,
that we performed specifically for ITree—a particular
clarification KBS instantiation. In Section 5, we con-
clude with a summary of the presented work and an
outlook on prospective future research topics.
Freiberg M., Herrmann F. and Puppe F..
Clarification KBS as Consultation-Justification Mash Ups - Proposing a Novel Paradigm for All-in-One Knowledge-based Systems.
DOI: 10.5220/0005028501680175
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2014), pages 168-175
ISBN: 978-989-758-049-9
2014 SCITEPRESS (Science and Technology Publications, Lda.)
Forward Consultation & Backward Clarification.
Forward- and backward chaining are known reason-
ing paradigms for rule-based systems, c.f., (Russel
and Norvig, 2010, Ch. 6). Similarly, we define
two KBS progression types: Starting with defined init
questions, forward consultation KBS query in all di-
rections, as all solutions contained in the KB can be
derived equally well depending on the provided input.
In contrast, backward clarification KBS target exclu-
sively one defined solution at a time; i.e., only ques-
tions, that potentially contribute to the target solution
rating, are posed.
Follow-up Questions & Abstractions. Follow-up
questions are not contained in the original core inter-
rogation sequence, but become activated only in case
a certain trigger question is answered. Abstraction
questions are questions the value/answer of which is
automatically calculated by the KBS; an example is
the BMI, which can be derived automatically once
values for weight and size are known.
2.1 Clarification KBS—Base Concept
The core idea of clarification KBS is to unite consul-
tation and justification interaction within an all-in-one
UI module. They base on backward clarification, i.e.,
they target a single solution only. Clarification KBS
generally can be seen as correspondent to the testing
part in hypothesize and test: Hypothesizing—i.e., nar-
rowing down the solution space to a probable solution
candidate—can be performed using forward consult-
ing KBS types or any other means such as mind-maps
etc.; a clarification KBS then handles its detailed in-
Due to its mashup characteristic, the clarification
KBS type can be invoked in two alternative ways:
Filled in, and empty. Main objective of the filled in
variant is to provide an interactively enhanced jus-
tification view with integrated, explorative consul-
tation facilities. Thus, the target solution already
possesses a certain rating/state, and the contribut-
ing question/answer pairs may be highlighted dis-
tinctly. Additionally, answer options might indicate—
visually or textually—in what way they contribute to
the target solution rating. The empty clarification vari-
ant aims at providing single issue consultation with
integrated justification. Therefore, it is initialized
with the yet unrated target solution, and entirely un-
answered (and thus, not marked) questions. However
they already might be marked with corresponding an-
swer contribution indicators, see above. Those initial-
ization variants can be applied for all UI styles that are
proposed in the subsequent section.
In the following, we introduce some interesting UI
styles for clarification KBS: Hierarchical Clarifier,
Answer Form Add-On, Daily, and Interview style.
The latter two are equally well applicable for forward
consultation; yet due to the focus of this paper, we de-
scribe them exclusively for the clarification context.
3.1 Hierarchical Clarifier Style
Hierarchical Clarifier is a single-line, interactive tree
style, sketched in Fig. 1. It presents the question as
well as all corresponding answers as one tree node,
that spans (a greater part of) the UI. In the exam-
ple, we experimented with placing answer options
before the question text as to increase the efficiency
of the answering interaction. The respectively clari-
fied solution is displayed as topmost node (Fig. 1, a);
this is followed by the base node set, representing all
first-level items that directly contribute to the core is-
sue rating (Fig. 1, b). Each question node again can
Call or visit a pediatrician or the hospital
Degrees (measured rectally)
36.5-37.5 37.5-38.5
> 38.5
> 41
Toddler/Baby with conspicuous symptoms
Age of the child
Conspicuous illness symptoms
Please select...
Degrees (measured rectally)
!! Attention !!
- measured orally/inside
the ear:
measured temperature
may be 0.5 degrees lower
than actual temperature!
- measured axillary:
measured temperature
may be up to 2.0 degrees
lower than actual
Fever lasts longer than one day
Febrile convulsion
NoYes Refine
repeated throwing up
unusual skin color
established (999)
Figure 1: Hierarchical Clarifier Base Conception.
be followed recursively by further refinement levels;
thereby, parents denote the abstraction, and their chil-
dren the corresponding abstraction sources that de-
rive the parent’s value; e.g., in Fig. 1, the first ques-
tion Temperature is an abstraction question, refined
by a one-question abstraction source Degrees?. An-
swered and thus solution contributing questions are
marked as to indicate the abstract contribution value,
e.g., the two green top-level items in the example; the
respectively selected answer options are displayed in
bold print. Further, we recommend the inclusion of
an anytime information display—here, realized by a
side-panel (Fig. 1, d)—which presents additional ex-
planations for a question once the corresponding info-
button (Fig. 1, c) of a question is clicked. This form
of presenting hierarchically nested/abstracted knowl-
edge allows for answering questions either directly
on the more abstract level, in case the users pos-
sess the required domain knowledge—which leads
Flu is derived as established (Score: +120.0) :
Shivers == Yes (+50),
Fever == Yes (+40),
Started Slowly == Yes (+10),
Lasted long == unknown,
Further Symptoms == Sneezing (-20)
Nausea (-5)
Fatigue (+10)
Sneezing (+20)
Figure 2: Answer Form Add-On enhanced Finding List.
to a shorter, more efficient session; or implicitly, by
expanding the parent node, and answering the cor-
responding refinement questions—the respective an-
swer values then are propagated to parent elements.
That way, this style intends to support broad user
groups (with varying proficiency) in highly expertise
domains. It has to be noted, that technical or domain
laymen may require some in-depth instructions and
training time for familiarizing with this interaction
principle. Hierarchical Clarifier optimally exploits its
potential benefits in the context of highly expertise
knowledge that can be defined using several differ-
ently detailed refinement levels; yet, this style can
also be used with less staggered knowledge—then,
however, it may degrade to a flat, list-like display that
lacks any expert shortcuts.
3.2 Answer Form Add-on Style
Answer Form Add-on fore-mostly targets the con-
text of providing interactive justification. Therefore,
static justification presentation forms are enhanced
by simple consultation widgets. In Section 3.2.1,
we describe two exemplary justification presentation
forms; based thereupon, we introduce the Answer
Form Add-on style in Section 3.2.2.
3.2.1 Base Justification Styles
In general, KBS justifications can be presented using
a vast array of text-based and visual techniques. Due
to space restrictions, we introduce only two examples
particularly apt regarding the Answer Form Add-On
enhancement: Finding List and Rule Graph.
Finding List. We define a pair of a question and the
user-entered answer as a finding. This justification
style lists all findings that contribute to the target issue
in a <question == value> style, c.f., Fig. 2; questions,
that at the time of invocation are not (yet) answered,
may display a default value, such as unknown (see
Fig. 2, Lasted long). Already selected findings may
further be highlighted, e.g., by bold face print; also,
findings can indicate the value they contribute to the
target issue—in the example, they are added in paren-
theses after the finding. Finding Lists generally suit
Flu (+120)
Cold (+40)
Lasted long = yes
Started slowly = yes
Shivers = yes
Fever = yes
Fatigue = yes
Sneezing = yes
Yes (+20) | No (-5)
Figure 3: Answer Form Add-On variants for Rule Graph.
all knowledge types, that allow for the identification
of questions and corresponding selected/non-selected
answers—e.g., rule-based-, set covering knowledge,
case bases, etc.
Rule Graph. Rule Graphs specifically visualize
rule-based knowledge. Those are a generalization of
Derivation Graphs, which have already been subject
to prior research (Baumeister and Freiberg, 2010).
Rule Graphs represent solutions as well as findings
by distinct nodes, c.f., Fig. 3. In case a finding
contributes to a solution rating, an arc is drawn be-
tween finding- and solution node. In the case of com-
pound rules—that connect several findings by AND
or OR conditions or that negate findings—the respec-
tive branches are connected by a rounded arc; in
Fig. 3, a continuous line is used for the AND connec-
tion, a dotted line for OR connections, and a bold dot
before the finding for a negation. Further, the rounded
arc can be decorated with the rating, the rule assigns
to the solution; e.g., the compound AND rule in the
upper half of Fig. 3 rates the solution positively with
the abstract rating class P6. When used as the basis
for the Answer Form Add-on, it is crucial that a Rule
Graph also visualizes those potentially contributing
rules that have not yet fired in the session as to enable
encompassing exploration. Such rules may be pre-
sented with a default answer value—e.g., unknown,
or in a greyed manner for indicating their state.
3.2.2 Answer Form Add-on Conception
Answer Form Add-On enhances static justification
presentation forms—see the previous section—by
simple Answer Form popups. Therefore, presented
findings serve as triggers that, once clicked, invoke
a popup that displays all further answer alternatives
for the question at hand. Fig. 2 sketches an exam-
ple that is based on the Finding List justification.
There, the question Further Symptoms offers the an-
swers Nausea, Fatigue, and Sneezing; in the current
session, Sneezing was selected, thus it is rendered in
the Finding List—the further answers then are listed
Baby/Toddler with clearly
conspicious symptoms
Yes |!No | Refine
Solution: Call or visit the pediatrician or the hospital ( not yet rated )
Conspicious Symptoms
Age of child
Please select...
Further Observations
Hyperthermie |!High | Increased
| Normal | Refine
repeated puking
[36-43] degree Celsius
Figure 4: Daily Style Base Conception.
only in the answer form popup. Another example is
shown in Fig. 3 for the Rule Graph justification style.
It sketches two further variants for realizing the An-
swer Form popups themselves: In a plain juxtaposed
style as shown in Fig. 3, a; and using radio buttons,
Fig. 3, b; the latter variant additionally indicates the
abstract rating value (green=positive, red=negative)
of answers by background coloring. Once the user
selects an answer inside the popup and closes it (X
button), the respective finding is added in the current
session and the justification view is re-rendered; this
implies, that the original session state and justification
are lost once the user starts interacting with the An-
swer Form widgets—it allows, however for a highly
interactive, in-place adaption of the entered data. As
Answer Form Add-On is based on justification pre-
sentation forms, it generally offers a high level of ex-
plicability and learnability: By explaining, why and
how the current solution was reached; and by mak-
ing explicit the coherences between findings and so-
3.3 Daily Style
Daily is a highly compact UI style where question-
naires, basically used as concept for grouping topi-
cally related questions, and their included questions
form a column-wide, visual entity—similar to arti-
cles in newspapers—which becomes best evident, in
multi-column variants; Fig. 4 sketches an example
in two-column style. This style optimally supports
the simultaneous display of (a greater part of) the
entire KB. Questions and answers are rendered in
a compact, juxtaposed style, c.f., Fig. 4, which in
turn supports the overview and free exploration of all
items. As the example indicates, the clarified solu-
tion is displayed prominently in the solution panel in
the top or at one side of the UI (Fig. 4, a). Ques-
tions, potentially further grouped by collapsable ques-
tionnaires (Fig. 4, c), are rendered in the center part
of the UI (Fig. 4, b). Answering questions is as
simple as clicking on the respective answer text—
repeated clicking deselects already set answers, de-
noting a highly intuitive overall interaction. Abstrac-
tion questions basically are handled inversely to Hier-
archical Clarifier: Abstraction source(s) are presented
as normal questions in the UI, whereas the abstraction
value itself is displayed in an inactive (greyed) man-
ner (Fig. 4, d)—once the user answers the abstraction
source(s), the abstraction value is calculated, inserted,
and displayed activated in case as users should be en-
abled to manually overwrite the derived value. In par-
ticular this implies, that there are no expert shortcuts
as in Hierarchical Clarifier, but questions—in case
they span several levels of abstraction—are always
presented in their most fine-grained, detailed manner
first. This on the on hand eases the KBS operation for
rather inexperienced users (regarding the domain), on
the other hand, there are no facilities for experts to
render the KBS session more efficient. Thus, in gen-
eral we assume Daily style more apt regarding inexpe-
rienced users. The example in Fig. 4 depicts an empty
initialized clarification variant—i.e., the solution is
presented yet without rating/state, and questions are
only marked regarding their potential contribution to
the solution, but without any answer selection-marks.
3.4 Interview Style
The core idea of the Interview UI style is to mimic
human conversations. As a consequence, this style
is only partly apt for clarification KBS: In the
consultation-focussed context, i.e., the empty clarifi-
cation variant, and in case users desire a high level
of system guidance but do not attach as much im-
portance on immediate, integrated justification. Inter-
view basically adheres to a single question paradigm,
i.e., it queries only one next question at a time,
thus strongly mirroring one by one conversations.
Thereby, the variant that the next question is pre-
sented automatically once the user answers the cur-
rent question provides the most guidance and thus po-
tential ease of use for laymen users; on the other hand,
this could as well be perceived as a loss of control
by other users. There, a stepwise navigation of al-
ready answered items, enabled by dedicated naviga-
tion buttons, denotes an alternative; i.e., the next suit-
able question is not displayed before the user actually
clicks on a confirmation/navigation button (Fig. 5, c).
Interview styles further offer the advantage of pre-
senting extensive additional information in an auto-
mated, integrated manner (Fig. 5, a). Contextual in-
formation regarding the current progress of the inter-
rogation can be provided either by a progress infor-
mation display (Fig. 5, b); or by including an anytime
solution display that presents the current state of the
target solution (Fig. 5, d).
Input History:
What do you want to do?:
Desired examination:
Test hypotheses for differences
Average values, central tendencies
How many dependent variables?
1 dependent variable
> 1 dependent variable
Additional information:
Dependent variable (also endogen variable) :
This is the variable, the implications on which are measured in a statistical
experiment, i.e., the one that can be changing.
Binomial Test: established (score: +120.0)
Figure 5: Interview Style Base Conception.
Despite its inherent focus on the consultation ac-
tivity, Strict Interview can nevertheless be adapted for
supporting a basic justification interaction: By inte-
grating an interview object history that lists the en-
tered findings (e.g., Fig. 5, e), and by enhancing that
list by certain interactions supporting the adaption of
such items. One example is interactive back-linking
to the question display corresponding to the clicked
finding, which enables users to quickly and easily go
back and change the input; or the integration of an-
swer form add-ons, as described previously, for in-
place editing.
So far, we have implemented one instantiation of
the Hierarchical Clarifier KBS style thoroughly—
the ITree clarification KBS. Therefore, the tailored
KBS engineering tool ProKEt (Freiberg et al., 2012)
was used. The ITree vision originated from the Ju-
riSearch project—a cooperation between the univer-
sity of W
urzburg and RenoStar, a german legal coun-
seling company. Since then, the design and conceptu-
alization of ITree have been further refined in several
iterative development and evaluation cycles. In the
following, we begin with a rough subsumption of the
ITree conception and its first evaluations; for details,
see (Freiberg and Puppe, 2012). We then focus on
discussing the results of the latest evaluations and the
consequential evolution of ITree.
Original ITree Vision, 1st & 2nd Evaluation.
ITree is a tailored instantiation of Hierarchical Clar-
ifier, see Section 3.1. It specializes that concept by
mapping all questions on a fixed answer set—yes, no,
uncertain; also, it applies special algorithms, based on
partly complex AND/OR compound rules, for deriv-
ing the values of parent questions from the values of
their child questions, see (Freiberg and Puppe, 2012).
The current implementation state of ITree is shown
in Fig. 6. The highly proficient and complex legal
domain is predestined for defining a deeply nested
abstraction/refinement KB as used with Hierarchical
Clarifiers; e.g., the ITree KB on dismissal contains up
to 10 abstraction levels (three levels shown in Fig. 6).
Therewith, legal ITree KBS support a wider range of
users by offering manifold operation levels regarding
the required prior domain knowledge.
The first evaluation in March 2012 showed, that
iTree generally is a favorable clarification UI style,
that basically supports an efficient and effective us-
age, free exploration, and skill-building ability. In
comparison to an Interview variant, ITree overall was
rated the more appropriate, convenient variant. How-
ever, problems occurred regarding the KB contents:
Concerning the understandability of question texts,
as too often specialist terms and intricate phrases
were used without deeper explanation; but also con-
cerning the refinement/nesting structures, see also
(Freiberg and Puppe, 2012). As those two aspects
had been the main (negative) cause variables, we ex-
perimented with two follow up variants of the initial
KB: One especially targeted at legal experts (EXP—
structured similarly to the original yet refined re-
garding the wording/explanations); and one targeted
towards laymen (USER—restructured entirely as to
provide items, most interesting to laymen, in a more
prominent manner). Those two variants were eval-
uated comparatively in May 2012—therefore, legal
laymen and expert users were asked to solve some
legal cases with the KBS variants. Interestingly, the
preference regarding the USER variant by laymen
was not as evident as assumed: In total, the USER
variant was preferred in 35% of the cases; the LEGAL
variant in about 30%; most remarkably, however, also
in 35% a complete indifference between both variants
was stated, both by expert and laymen users. As the
EXP variant is based on commonly taught, unified,
legal schemes, and thus can be formalized (by the ex-
perts themselves) and maintained in an easier man-
ner, we finally decided to further rework and refine
exclusively the EXP variant, as there were no con-
vincing indications in favor of the USER variant. As
a basically positive result, the overall success rate re-
garding the evaluated cases increased to 61% (USER)
and 72%(LEGAL), which denotes a good improve-
ment from the success rate of 43% for the ITree style
in the first evaluation.
3rd User Study and Consequential UI Adaption.
After further refining the legal (expert oriented) KB as
well as the basic ITree design, another user study was
conducted at the end of 2013 with 19 students of med-
Was the dismissal prohibited due to time limitations?
Is the dismissal correct regarding its form?
Is the reason for dismissal legally correct?
Is the reason for dismissal an orderly reason?
Is the reason for dismissal an extraoridinary reason?
Are there any special termination rights?
Result: NO
Result for the target solution:
Was the dismissal legally correct?
Please move the mouse over this box for displaying further justifications
within the additional information side panel.
Your work contract was not terminated legally correct. A dismissal is only legally correct, in case all legal
conditions are fulfilled. This means, that your work contract still persists legally, in spite of the spoken dismissal.
You need to take care that you prosecute your case within 3 weeks from receiving the dismissal. This is the legal
period of time from $ 4 KSchG. If you do not adhere to this period of time, also a dismissal which is formerly
incorrect becomes legally valid according to $ 7 KSchG.
- Additional Information -
Is the dismissal prohibited
due to timely restrictions?
If there exists a legally correct, valid,
timely restriction of the work contract,
the orderly dismissal is prohibited in
An extraordinary dismissal might still
be possible, though.
Yes No -?- X
Was the dismissal prohibited due to time limitations?
Yes No -?- X
Was the dismissal legally correct?
Result: NO
Figure 6: ITree Style Base Conception, used with a clarification KB on the legal correctness of a dismissal.
ical informatics; Similar to the prior studies they were
asked to solve given legal cases with the KBS. Most
remarkably, now a success rate of 90-100% could be
achieved—a huge improvement compared to former
evaluations. Also further aspects, rated on a scale
from 1/very good–6/very bad received good results—
e.g., the ease of use (2.0–2.5), the efficiency (2.2–2.5),
overall ITree utility (2.1) or the KB contents (1.8–
2.6), varying between the different cases to be solved.
However, also this study revealed some specifically
UI/implementation specific issues. The major remark
concerned the (too unresponsive) loading speed of the
system, both initially and when users enter data; there
we reworked the entire mechanism as to significantly
increase the loading behavior. Also, the side panel
was (in some cases) perceived as too small—and thus
enlarged in the ITree implementation.
Expert Usability Evaluation (4th Study). In early
spring 2014, ITree finally also was assessed partic-
ularly focussing on its general usability and design
(and not so much on success rates). Therefore, an ex-
pert evaluation study was performed in early spring
2014, where ITree (amongst further KBS UI styles)
was evaluated. 30 evaluators—human computer in-
teraction students of the university of W
performed both a heuristic evaluation (Nielsen, 1994)
and a cognitive walkthrough (Wharton et al., 1994).
This evaluation partly confirmed previous evaluation
insights, partly exhibited interesting new findings.
The first remark concerned the status mediation
by color coding: Adhering to a red/green/yellow traf-
fic lights scheme, the solution and question state were
highlighted according to the current solution deriva-
tion state or the question’s contribution to the solu-
tion rating, respectively. Depending on the respective
question, the answer yes and no can contribute both to
a positive, or to a negative solution rating—which ac-
cordingly sometimes leads to coloring yes with green
(positive contribution) or yes with red (negative con-
tribution). The main remark there was, that this un-
steady coloring of answer options was unfavorable,
all the more in those cases where yes was marked red
(as this additionally contradicts the cultural seman-
tic feeling of yes being positive/green). Despite high-
lighting in the instructions that colors do not mirror
the answer semantic but represent the contribution of
that answer to the solution, this was rated as highly
confusing. As color coding specifically was intended
to mirror the current system status clearly at first
glance—which was also approved of by several eval-
uation remarks—we did not remove colors entirely;
also, we assumed that adding color-independent signs
such as + and - would not be quite as clear and thus in-
crease the memory load of users when estimating the
KBS status. Thus, we introduced a different coloring
scheme yellow/white/blue, shown in Fig. 6.
The next aspect was the scrolling behavior: Basi-
cally, ITrees are scrollable as to enable users to reach
all nodes even if the expanded tree grows larger than
the available UI space; however, in the original ver-
sion this both induced a disappearance of the solution
display header and of the additional information dis-
play side panel (which is especially critical as with-
out the add-on explanations displayed there, ITree be-
comes almost unusable for most users). Thus, we fix-
ated and enlarged the side panel for prominent, any-
time explanation display. Regarding the top panel, we
added a flexible jump-to-the-top mechanism: Once
users have answered enough questions for deriving a
solution rating, the display automatically scrolls up to
the solution panel; thus, the tree can to take up more
available UI space as long as not solution is derived,
yet as soon as a rating is available, it is brought promi-
nently back into mind by the jump-scroll mechanism.
Regarding that side panel, also its general
interaction—automatically updating panel contents,
i.e., additional information for questions, when hover-
ing a question node—was criticized. Specifically re-
garding high-resolution displays, the required precise
hovering of potentially large question nodes was per-
ceived as cumbersome and disrupting. However, we
discarded the idea of using dedicated icon/question
clicks as triggers, as ITree is deliberately intended
as highly efficient UI and interaction type; thus re-
quiring an extra click each time such information
is required—potentially often in the complex tar-
get domains—would reduce the efficiency drastically.
Thus for enhancing the side panel interaction, a short
delay was added—i.e., once information for a ques-
tion is displayed, hovering/touching another question
does not update the panel immediately but only after a
certain delay; this obviates unwanted explanation up-
dates in the major part of cases.
The next remark concerned the initial automated
expansion of parts of the tree—basically intended to
highlight questions that are, on average, most rele-
vant for a solution and thus should not be overseen.
The evaluators remarked, however, that users often
might be left wondering whether this a presentation
flaw or a deliberate design decision. However, as
we still believe that this initialization is beneficial es-
pecially for domain-related, experienced users—who
might check such a similar base answer set in most
cases anyhow—only a clear remark explaining the is-
sue is added in the instructions but the mechanism
was kept. This remark, as well as a short subsump-
tion of the usage paradigm and color semantics are
further displayed prominently in the UI header at sys-
tem start—as initially not yet much solution related
information is available for display there, this offers a
great opportunity for presenting such important, ini-
tial information for quick start support.
Another major remark concerned the knowledge
connector items If, AND, and OR, displayed in front
of the questions c.f., Fig. 6, c for visually mirroring
the underlying derivation knowledge (rules). Those
were partly rated as confusing and as rather increas-
ing the mental workload of users. For ITree as a
clarification KBS, however, this in-place justifications
are essential for increasing its on-the-fly explainabil-
ity. Therefore, a short-hand key to such symbols is
planned for minimizing potential confusion.
It was further criticized that invoking a new case
[Neuer Fall] by clicking the respectively termed but-
ton does not display any warning regarding the conse-
quential loss of all currently entered data. Basically,
we did not assume that as critical due to the button la-
bel that already indicates that implicitly. As this was
a generally rather frequently stated issue, we never-
theless added a confirmation popup which highlights
this issue more prominently.
Recent Studies Summer 2014 and Synopsis. In
summer 2014, two more large- and smaller scale
ITree studies were performed (248 and 21 computer
science student participants, respectively), thus
following a highly agile, iterative development–
evaluation cycle. The overall baseline ratings
remained positive also in those last studies; e.g., over-
all utility (2.2–2.7), KB quality (2.6–2.8), knowledge
mediation (2.6–2.7) or ease of use (2.5–3.0) on scales
from 1/very good–6/very bad. Success rates quite
constantly remained at 86–88%, which we account
as great results given the highly expertise, specific
domain and complexity of potential cases. Those
studies further confirmed the suitability of the un-
usual coloring scheme and the added value of adding
the delay regarding the additional information panel
update. The latest suggestions for improvement,
partly already realized, included: Moving the side
panel as well as the answer buttons to the right side as
to enable the western cultural left-to-right read flow;
prominently displaying a notification once a solution
rating is reached, in addition to the jump-scroll
mechanism; and providing notifications once a user
tries to enter values that contradict already derived
Having evolved iteratively over about two years,
ITree now exhibits rather good baseline ratings; this
specifically concerns the success rate, but also further
important traits such as utility, KB quality, knowl-
edge mediation, ease of use, etc. Also, the over-
all design/interaction was mostly perceived as con-
venient. This makes us confident, that ITree mean-
while denotes a beneficial clarification KBS UI style;
and consequently, that the assumed benefits of consul-
tation/justification mashup types—such as high flex-
ibility, bringing in own competency, skill-building
ability—within a unified KBS solution actually apply.
As one base insight, we claim that especially in the
context of highly expertise KBS, an iterative, agile de-
velopment paradigm is extremely beneficial—which
was strongly fostered by the tool ProKEt. Another
important general insight was, that the evaluation re-
sults not only depend on the particular UI/interaction
realization, but likewise also on the KB quality and
the quality of the test cases/descriptions—at least in
similarly expertise domains and consequential com-
plex test case descriptions.
In this paper, we proposed Clarification KBS as a
novel KBS paradigm. Clarification KBS not only
mashup consultation and justification UI/interaction
within a single UI; they particularly possess the poten-
tial to foster more active user participation by letting
them bring in their own competency and further of-
fer a high explicability—therewith providing domain-
and KBS related skill-building ability. We first intro-
duced the basic concept of clarification KBS. We fur-
ther proposed some interesting, suitable UI represen-
tations: Hierarchical Clarifier, Answer Form Add-On,
Daily, and Interview style. Also, we discussed the re-
sults of evolving and evaluating an ITree KBS UI—a
tailored instantiation of Hierarchical Clarifier—in the
legal domain.
The overall results showed that ITree in particu-
lar and thus clarification KBS in general can stand
up to the assumptions regarding the assumed bene-
fits consultation/clarification KBS mashups; yet, also
the insight manifested that clarification KBS are not
well applicable for all diverse user types likewise—
rather, they seem particularly useful for users with at
least a little domain-related and/or technical experi-
ence; this assumption will be subject to future work.
Further practical work regards the implementation of
a more general clarification KBS for the medical do-
main. The currently envisioned medical clarifier re-
quires the flexible integration of diverse further ques-
tion types—e.g., numerical, date, or free-text ques-
tions. Consequently, also the propagation algorithm
of ITree will require some reconsideration and gener-
Also, the thorough implementation and evaluation
of the other proposed clarification UI types is subject
to future work. Regarding the Answer Form Add-On
style, this particularly also concerns the static base
justification types, two of which were presented in
this work; there, we are currently realizing some ex-
perimental solutions with the tool ProKEt. Finally,
also the envisioning and realization of further novel
clarification KBS UI styles offers ample room for fu-
ture work.
Arnold, V., Clark, N., Collier, P. A., Leech, S. A., and Sut-
ton, S. G. (2006). The differential use and effect of
knowledgebased system explanations in novice and
expert judgment decisions. MIS Quarterly, 30(1):79–
Baumeister, J. and Freiberg, M. (2010). Knowledge visual-
ization for evaluation tasks. Knowledge and Informa-
tion Systems, 29(2):349–378.
Castellanos, V., Albiter, A., Hern
andez, P., and Bar-
rera, G. (2011). Failure analysis expert system
for onshore pipelines. part-ii: End-user interface
and algorithm. Expert Systems with Applications,
Chen, Y., Hsu, C.-Y., Liu, L., and Yang, S. (2012). Con-
structing a nutrition diagnosis expert system. Expert
Systems with Applications, 39(2):2132–2156.
Freiberg, M. and Puppe, F. (2012). iTree: Skill-building
User-centered Clarification Consultation Interfaces.
In Proceedings of the International Conference on
Knowledge Engineering and Ontology Development
(KEOD 2012). SciTePress Digital Library.
Freiberg, M., Striffler, A., and Puppe, F. (2012). Extensible
prototyping for pragmatic engineering of knowledge-
based systems. Expert Systems with Applications,
Nielsen, J. (1994). Heuristic Evaluation. In Nielsen, J. and
Mack, R. L., editors, Usability Inspection Methods,
pages 25–62. John Wiley & Sons, New York.
Pinheiro, V., Furtado, V., Silva, P. P. D., and Mcguinness,
D. L. (2006). Webexplain: A upml extension to sup-
port the development of explanations on the web for
knowledge-based systems. In Proceedings of the Soft-
ware Engineering and Knowledge Engineering Con-
ference, San Francisco.
Puppe, F. (1993). Systematic Introduction to Expert Sys-
tems. Springer-Verlag. ISBN: 3-540-56255-9.
Russel, S. J. and Norvig, P. (2010). Artificial Intelligence:
A Modern Approach. Pearson. ISBN 13: 978-
Ting, S., Kwok, S., Tsang, A. H., and Lee, W. (2011). A
hybrid knowledge-based approach to supporting the
medical prescription for general practitioners: Real
case in a hong kong medical center. Knowledge-Based
Systems, 24(3):444–456.
Wharton, C., Rieman, J., Lewis, C., and Polson, P. (1994).
Usability inspection methods. chapter The Cognitive
Walkthrough Method: A Practitioner’s Guide, pages
105–140. John Wiley & Sons, Inc., New York, NY,
Zeng, Y., Cai, Y., Jia, P., and Jee, H. (2012). Development
of a web-based decision support system for support-
ing integrated water resources management in daegu
city, south korea. Expert Systems with Applications,