=Paper=
{{Paper
|id=None
|storemode=property
|title=Knowledge Acquisition for Life Counseling
|pdfUrl=https://ceur-ws.org/Vol-1070/kese9-07_09.pdf
|volume=Vol-1070
|dblpUrl=https://dblp.org/rec/conf/ki/NewoA13
}}
==Knowledge Acquisition for Life Counseling==
https://ceur-ws.org/Vol-1070/kese9-07_09.pdf
Knowledge Acquisition for Life Counseling
Régis Newo and Klaus-Dieter Althoff
German Research Center for Artificial Intelligence, DFKI GmbH,
Research Group Knowledge Management,
Competence Centre Case-Based Reasoning
Email: firstname.surname@dfki.de
Abstract. In this paper, we explain how highly unstructured domain
knowledge can be acquired and integrated in a case-based reasoning sys-
tem. We apply our approach to the life counseling domain. We introduce
the two steps of our knowledge acquisition approach in such unstructured
domains. The first step is manual and relies on domain experts. The sec-
ond step is automatic and uses information extraction techniques. Our
approach has the potential to contribute to the formalizing and estab-
lishing of an important subset of life counseling terminology. In addition,
our approach could serve as an example for comparable weak theory do-
mains.
1 Introduction
Case-Based Reasoning (CBR) is a methodology for problem solving based on
the fact that previously experienced knowledge can be used to solve new prob-
lems [1]. It has been successfully applied in different domains like for example
medicine [2], help-desk [3] or technical diagnosis [4]. The needed knowledge used
in a CBR system is stored in the so-called knowledge containers (vocabulary,
similarity measures, adaptation knowledge and the case base) [5]. The amount
of knowledge available for each container depends on the application domain.
For application domains, in which a certain level of formalization is already
achieved, it might be easier to fill the vocabulary and similarity measures con-
tainers. Whereas it might be easier to fill the case base container in unformalized
and/or unstructured application domains.
Our application SeBaPort (Portal for counseling, in German Seelsorge- und Be-
ratungsportal) deals with life counseling. Life counseling deals with the wellbeing
of humans. Life counselors conduct converstions with consulters, give advices and
help them to help themselves. Counselors often rely on past expriences for the
counseling. The goal of SeBaPort is to help counselors by providing them with
counseling cases (depending on their requests), which they can learn from. This
makes CBR an ideal methodology to process the knowledge used in that do-
main.
Life counseling is a domain which is highly unstructured. This makes it very
difficult to develop a CBR system for life counseling and be able to provide
knowledge in the previously mentioned knowledge containers. For this applica-
tion domain, we would have to develop an initial set of vocabulary and similarity
measures, and also find a methodology to process the available (unstructured)
cases and store them in our case base. SeBaPort does not aim at providing so-
lutions for a given counsel or problem, primarily because the acceptance of the
counselors would significantly diminish, if we claim to be able to provide com-
plete solutions to counseling problems.
In order to build a life counseling CBR system, we started by developing an
initial CBR model and acquiring structured cases. In this paper, we describe
in Section 3 how we designed our initial CBR model and our approach for the
acquisition of (structured) cases in Section 4. Afterwards we will present some
related work in Section 5 and will conclude the paper in Section 6. In the next
section, we will first give an elaborate presentation of the life counseling domain.
2 Life Counseling
Life counseling is concerned with the welfare of human beings, more precisely
the thinking, feeling, acting, and also the faith of persons. Life counselors help
people deal with their problems and conflicts. They conduct several counseling
interviews with the consulters. The main idea is to help people help themselves by
having several discussions with them, give them multiple views on their problem
and give them basic hints. Life counselors for example give exercises, which
are part of a counseling method, to consulters after an interview. During the
following interviews, they try to find out, whether it helped the consulter or it
should be changed.
In order to do that, counselors themselves mainly rely on their experience in
the domain, but also on the methodical knowledge they learned during their
formation. They are grouped in small communities to share their experiences.
As they do not only build on self-made experiences but also on those from others,
they often rely on peer consulting and supervision to critically analyse past cases
(and be able to learn from them). Further they contact other colleagues when
they need help in an actual or past counseling case. Such help might comprise
a whole counseling case or just information about parts or aspects (e.g., the
method or exercise that can be used in a given situation) of life counseling.
Our goal is to provide a system that can be used to help life counselors in their
work. We want to provide a decision support system that helps the counselors
to document and share their experiences. They would also learn new cases from
others and be able to find hints and references (e.g., to counseling methods) while
looking for help (when they deal with a given case). The intended functionality
of our system is presented in figure 1 on the basis of the CBR cycle.
An example of the description of the patient’s problem in a documented case
is given below.
Woman, 48 years old, married and 3 children: 12, 15 and 18 years old.
She has been working shifts as a full-time midwife for 2 years. She at-
tends counseling because of insomnia due to her often alternating shift
work. She has particularly problems with insomnia after night shifts. She
Fig. 1. CBR Cycle in Life Counseling
cannot ignore surrounding noises and she cannot completely darken her
bedroom. As a consequence of this, she is often tired, is not able to work
under pressure and suffers from headaches.
The documentation of the case also contains the documentation of each interview
with for example the applied methods, goal validation and solution interventions.
3 Case Model for Life Counseling
When developing CBR systems, one of the first challenges is to fill the knowledge
containers. We have to evaluate which kind of knowledge is availaible in order
to know which containers can be filled. In life counseling, the knowledge that is
easier to acquire is the experirences made by the experts represented as cases.
Due to lack of formalization in the domain, we need to find a way to extract
formalized knowledge from the available cases. For that, we want to structure
the information contained in cases.
Fig. 2. Structure of a life counseling case
Most experts have their own way to write down their cases. Furthermore, the
cases do not contain the same kind of information, have different levels of detail
and elaborateness. It is thus nearly impossible to automatically detect a structure
in raw cases.
Our approach to structure the available cases is to get the needed information
from the experts. Instead of getting unstructured cases, we want to be able to
get semi-structured cases from the experts. For that purpose, we elaborated a
structure that should be used by the experts. The used structure has to reflect
the way of thinking of life counselors. We thus have to involve experts in order
to define such a structure.
Table 2 shows the structure for life counseling cases that we developed. It is
based on a preliminary study done with domain experts (i.e. counselors) [6]. We
validated the structure by comparing it with a doctor’s report used in a clinic
for psychotherapy and psychosomatic medicine. It shows that a case can contain
a multitude of information. Although a given case must not contain all possible
information (i.e. each parameter of the structure must not be filled), it would
be very difficult to automatically map the knowledge from a given case to the
parameters. We used the defined case structure to develop a CBR model with
an initial vocabulary and an initial set of similarity measures. The CBR model
is more detailled, so we can have a better description of the cases and also more
precise similarity measures. For example, the medication (in personal data) has
following attributes:
– the name of the medication,
– the generic type,
– the active substance,
– the daily dosage, etc.
As another example, the CBR has attributes for the number of children as well
as the gender and the age of each children.
4 Two-Step Case Acquisition
Now that we defined a case model, the next challenge is to fill our case base
with life counseling cases following the model. Unfortunately, counselors do not
have a formal manner to document their cases. This leads to unstructured case
descriptions. In order to be able to use those cases in our CBR system, we have
to find methods to formalize the existing knowledge (i.e. cases). This is a difficult
task because of the diversity of information available in a case, as can be seen
in the last section. Our approch for the knowledge acquisition consists of two
steps.
The goal of the first step is to organize the available information. This is a man-
ual step in which the available diversified information is mapped to the structure
defined in Section 3. We rely on domain experts to cope with this assignment. In
SeBaPort, this step is realized by providing experts with web forms, which can
be used to enter the cases. At the end of this step, we have a case description
that matches the structure defined in Table 2.
The second step of the knowledge acquisition is automatic and consists of using
information extraction to obtain structured CBR cases. The complexity of this
step depends on the type of information given by the experts in the first step.
Some information, like the gender or the nationality of the patients can be eas-
ily matched to a formal case model. Other, like the medication or the children,
need more effort for the formalization. For example, the way information about
medication is given differs from one expert to another and can be more or less
expressive. Nevertheless we have to be able to match the natural language de-
scription of the medication information to our formal model which contains the
additional attributes defined at the end of Section 3. Another example is the at-
tribute children, for which the same holds. From the given description, we have
to be able to identify, if given, the number of children, the age and/or gender of
each children and so on. In the example given in Section 2, we would identify 3
children and the ages of the children. However, the gender are not documented.
We used information extraction techniques provided by the component ANNIE
of the framework GATE (see [7]) to tackle this challenge.
Another goal we pursue in SeBaPort, is to be able to learn from the acquired
cases in order to formalize the application domain. We thus want to perform a
stepwise knowledge formalization for life counseling. This has to be done from
scratch because the domain, as explained earlier, is highly unstructured. We are
actually trying to gain the formalized knowledge from the acquired cases. The
purpose is to be able to tackle the fact that there are not only several ways
to document a counseling case, but also different counseling perspective. The
representatives of each perspective often have problems to deal with case docu-
mentations from other perspectives. A formaliztion like the one we are targeting
would promote the intercommunication between the representatives of the diff-
ent perspectives.
5 Related Work
The idea of using CBR in medical related domains has been explored in the
last couple of years. In [2] the authors present four recent CBR applications in
different medical domains. The applications deal with:
– Long-term follow-up of oncology patients
– Assistance of type 1 Diabetes patients
– Support of physicians in the domain of end stage renal disease
– Diagnosis and treatment of stress.
There have been many other CBR applications in medical domains. Nevertheless,
to our knowledge, SeBaPort is the first one to deal with life counseling.
As for knowledge formalization, there are also other approaches that deal with
that topic. One of them is the knowledge formalization continuum presented in
[8]. The authors present a process for knowledge development based on a flexible
organization of knowledge. The main difference between our aproach and this
one (as well as many other knowledge formalization approaches) is that the
only initially available knowledge in life counseling are the unstructured case
descriptions. That is, our inital information can hardly be used for learning,
classification or even formalization.
In [9], the authors present an approach for knowledge extraction from data taken
from forums, which are communities of experts. This approach relies on a initial
auxiliary data to extract the knowledge and uses the extracted knowledge to
improve the knowledge extraction.
6 Conlusion
In this paper, we presented the domain of life counseling and how the available
knowledge can be used to develop a CBR system (SeBaPort). SeBaPort will
help life counselors to extend their knowledge and learn from past cases. We
showed how we are actually extracting the knowledge from available cases and we
want to use it for the knowledge formalization. We intend to test our knowledge
acquisition by evaluating the similarity measures with the acquired cases. The
evaluation is still an ongoing work. Furthermore we will develop an approach to
incorporate experts’ feedback to our formalization process.
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