=Paper=
{{Paper
|id=Vol-1289/kese10-09_submission_11
|storemode=property
|title=Knowledge Modeling with the Open Source Tool myCBR
|pdfUrl=https://ceur-ws.org/Vol-1289/kese10-09_submission_11.pdf
|volume=Vol-1289
|dblpUrl=https://dblp.org/rec/conf/ecai/BachSAR14
}}
==Knowledge Modeling with the Open Source Tool myCBR==
https://ceur-ws.org/Vol-1289/kese10-09_submission_11.pdf
Knowledge Modeling with the Open Source Tool
myCBR
Kerstin Bach1 , Christian Sauer2 , Klaus Dieter Althoff3 , and Thomas
Roth-Berghofer2
1
Verdande Technology AS
Trondheim, Norway
http://www.verdandetechnology.com
2
School of Computing and Technology
University of West London, United Kingdom
http://www.uwl.ac.uk
3
Competence Center Case-Based Reasoning (CC CBR)
German Research Centre for Artificial Intelligence, Kaiserslautern, Germany
http://www.dfki.de/web/competence/cccbr
Abstract. Building knowledge intensive Case-Based Reasoning applica-
tions requires tools that support this on-going process between domain
experts and knowledge engineers. In this paper we will introduce how the
open source tool myCBR 3 allows for flexible knowledge elicitation and
formalisation form CBR and non CBR experts. We detail on myCBR 3 ’s
versatile approach to similarity modelling and will give an overview of
the Knowledge Engineering workbench, providing the tools for the mod-
elling process. We underline our presentation with three case studies of
knowledge modelling for technical diagnosis and recommendation sys-
tems using myCBR 3.
1 Introduction
Case-Based Reasoning (CBR) is a methodology introduced by Riesbeck and
Schank [13] and Kolodner [8] who derived its basic principles from cognitive
science. They describe how humans manage and reuse their experience described
in episodes. Aamodt and Plaza [2] introduce a basic model for developing CBR
applications. It consists of four processes: Retrieve, Reuse, Revise and Retain.
The CBR process requires cases that consist of problem and solution description.
Problem descriptions are usually attributes values describing a problematic or
critical situation while the solution contains information on how to solve the
given problem. In the retrieve phase, the attributes describing a problem are
matched against cases in a case base. The best n cases are returned. In order
to match a given situation these cases can be adapted (Reuse). In the revision
phase, reused cases are verified before they are retained.
CBR systems always carry out the retrieve phase which is characterized by
a similarity-based comparison of features, while the remaining phases can omit-
ted. Richter [12] introduced to model of four knowledge containers describe the
required knowledge within a CBR system:
– Vocabulary defining the range of allowed values for attributes. For numeric
values this is usually the value range (minimum, maximum) while for sym-
bolic values this can be a list of values.
– Similarity Measures defining the relationship between attribute values in
form of a similarty assignments. Similarity measures can be formulas like
the hamming distance for numeric values or reference tables for symbolic
values.
– Adaptation Knowledge is knowledge describing how cases can be adapted in
the reuse step, often represented as rules.
– Cases are instances describing situations that have happened and are worth
capturing in order to be reused. They instantiate attributes describing the
problematic situation as well as a solution description. Their degree of for-
malization can vary.
Developing CBR systems requires a systematic development of knowledge
models by defining the requirements and building the models itself. myCBR 3 4
is an open source tool targeting at developing customized knowledge models
with an emphasis on vocabulary and similarity measure development. myCBR 3
is an open-source similarity-based retrieval tool and software development kit
(SDK). With myCBR 3 Workbench you can model and test highly sophisti-
cated, knowledge-intensive similarity measures in a powerful GUI and easily
integrate them into your own applications using the myCBR 3 SDK[3]. Case-
based product recommender systems are just one example of similarity-based
retrieval applications.
In the remaining of this paper we will give an overview of other CBR tools
and applications (section 2) as well as showcase the functionalities of myCBR 3
(section 3). In section 4 we will show how myCBR 3 has been applied in different
CBR projects while the final section will sum up the paper and give an outlook
on future work on the tool.
2 Related Research
Freely available CBR tools are for instance FreeCBR, jCOLIBRI or eXiT*CBR,
which will be briefly discussed in this section. FreeCBR5 is a rather simple CBR
engine, which allows the realization of basic CBR features. However, it does not
cover features like case revision or retention and more individualized knowledge
models, or comprehensive global and local similarity measures, are not applica-
ble either. Further, it still requires quite some effort to apply it to a high variety
of tasks. jCOLIBRI started from a task oriented framework also covering dis-
tributed reasoning [10], recently jCOLIBRI Studio [11] for more comprehensive
support of building CBR knowledge has been introduced. Up to today jCOL-
IBRI includes more machine learning and semantic web features while myCBR 3
focused on the knowledge required in the knowledge containers.
4
http://www.mycbr-project.net
5
http://freecbr.sourceforge.net/
COLIBRI is another platform for developing Case-Based Reasoning (CBR)
CBR software. COLIBRI’s main goal, opposed to myCBR 3, is to provide the
infrastructure required to develop new CBR systems and its associated software
components, rather than a CBR knowledge model. COLIBRI is designed to offer
a collaborative environment. It is an open platform where users can contribute
with different designs or components of CBR systems, which will be reused by
other users. Subsequently many of the components available have been developed
by third-party research groups and contributed to the platform to be shared with
the community.
As a platform, COLIBRI offers a well-defined architecture for designing CBR
systems. COLIBRI also provides a reference implementation of that architecture:
the jCOLIBRI framework. jCOLIBRI is a white-box tool that permits system
designers to have total control of the internal details of the software. The plat-
form also includes graphical development tools to aid users in the development
of CBR systems. These tools are enclosed in the COLIBRI Studio IDE and
generate applications that use the components provided by jCOLIBRI.
Furthermore, creating individualized case representations and especially flex-
ible similarity measures is the strength of myCBR 3. eXiT*CBR has also its roots
in machine learning applications and is specialized for medical diagnosis tasks [9].
It has recently been extended in order to cope with more than one case base. In
comparison to myCBR 3, the ideas behind the methodology also differ, since we
are focusing on the knowledge container model rather than the machine-learning-
related tasks. The integration of Drools in an existing framework for executing
rules on a given corpus has been introduced by Hanft et al. [7]. In this paper
Drools has been integrated in an existing OSGi environment. The approach pre-
sented here required a more comprehensive customization since myCBR 3 was
not embedded in OSGi and the requirements for the rules differed in terms of
usable knowledge and modification of cases.
In industry, most prominent CBR tools or CBR related technologies are
used by empolis in the on SMILA6 based Information Access Suite7 as well as
by Verdande Technology in DrillEdge 8 [6] . The Information Access Suite has
been applied in various help-desk scenario applications as well as in document
management while DrillEdge focuses on predictive analytics in oil well drilling.
Both companies run proprietary implementations based on academic software -
CBR-Works [18] and Creek [1] respectively.
3 Knowledge Engineering in myCBR
myCBR 3 is an open-source similarity-based retrieval tool and software devel-
opment kit (SDK)[19]. With myCBR 3 Workbench you can model and test
highly sophisticated, knowledge-intensive similarity measures in a powerful GUI
and easily integrate them into your own applications using the myCBR 3 SDK.
6
https://www.eclipse.org/smila/
7
http://www.empolis.com
8
http://www.verdandetechnology.com
Case-based product recommender systems are just one example of similarity-
based retrieval applications.
The myCBR 3 Workbench provides powerful GUIs for modelling knowledge-
intensive similarity measures. The Workbench also provides task-oriented config-
urations for modelling your knowledge model, information extraction, and case
base handling. Within the Workbench a similarity-based retrieval functionality
is available for knowledge model testing. Editing a knowledge model is facilitated
by the ability to use structured object-oriented case representations, including
helpful taxonomy editors as well as case import via CSV files.
The myCBR 3 Software development Kit (SDK) offers a simple-to-use data
model on which applications can easily be built. The retrieval process as well as
the case loading, even from considerably large case bases, are fast and thus allow
for seamless use in applications built on top of a myCBR 3 knowledge model.
Within myCBR 3 each attribute can have several similarity measures. This
feature allows for experimenting and trying out different similarity measures to
record variations. As you can select an appropriate similarity measure at run-
time via the API, you can easily accommodate for different situations or different
types of users.
The myCBR 3 Workbench is implemented as using the Rich Client Platform
(RCP) of Eclipse and offers two different views to edit either knowledge models
or case bases. In this section we will focus on the modelling view as shown in 1.
The conceptual idea behind the modelling view is that first a case structure
is created, followed by the definition of the vocabulary and the creation of in-
dividual local similarity measures for each attribute description (eg. CCM in 1)
followed by the global similarity measure for a concept description (Car in 1).
The modelling view of the myCBR 3 Workbench (see figure 1) is showing
the case structure (left), available similarity measures (left bottom) and their
definition (center). Modelling the similarity in the Workbench takes place on
the attribute level for local similarity measures and the concept level for global
similarity measures.
3.1 Building a Vocabulary
The vocabulary in myCBR 3 consists of concepts and attributes. A concept
description can contain one or more attribute descriptions as well as attributes
referencing concepts, which allows the user creating object-oriented case rep-
resentations. In the current version myCBR 3 also allows for the import of
vocabulary items, e.g. concepts and attributes, from CSV files as well as from
Linked (Open) Data (LOD) sources.
An attribute description can have one of the following data types: Double,
Integer, String, Date and Symbol. When attributes are defined, the data types
and value ranges are given with initial default values and can be set to the
desired values in the GUI.
Fig. 1. Example view of the knowledge model view in the myCBR 3 workbench
3.2 Building Similarity Measures
The Workbench provides graphically supported modelling of similarity functions
that support their definition. As an attribute description can have more than
one similarity measure experimenting with knowledge modelling approaches is
facilitated. For numerical data it is providing predefined distance (or similar-
ity) functions along with predefined similarity behaviour (constant, single step
or polynomial similarity decrease). For symbolic values, myCBR 3 Workbench
provides table functions and taxonomy functions. A table function allows defin-
ing for each value pair the similarity value, while a taxonomy subsumes similarity
values for subsets of values. Depending on the size of a vocabulary, table simi-
larity measures are hard to maintain and taxonomies allow an easier overview.
For symbolic values, also set similarities are provided in order to compare mul-
tiple value pairs. For each of the similarity measures, as well as for the global
similarity measure(s) a specific, versatile editor GUI is provided.
4 Case Study
4.1 Creating Knowledge from Unstructured Documents
This approach has been developed in machine diagnosis based on experiential
knowledge from engineers[4]. Most vehicle companies provide service after de-
livering their machines to customers. During the warranty period they are able
to collect data about how and when problems occurred. They take this data for
improving vehicles in different ways: collected data can go back in the product
development process, it can be used for improving diagnostic systems to repair
them at dealerships or in the factory and also educating service technicians re-
pairing vehicles abroad. This is extremely important if vehicles cannot easily be
taken back to factory, e.g. services for aircrafts or trucks.
Such machine manufacturers collect information about machine problems
that are submitted by technicians containing machine data, observations and
sometimes further correspondence between an engineer or analyst with the tech-
nician at the machine. In the end, these discussions usually come up with a
solution - however, the solution is normally neither highlighted nor formalized
and the topics and details discussed highly depend on the technician and what
the Customer Support asks for. That is the reason why cases that are stored
for collecting Customer Support information can not directly be used for CBR.
Therefore we will differentiate between Customer Support Cases (CS Cases) and
CBR Cases. CS Cases contain original information collected during a discussion
between Customer Support and the field technician, while a CBR Case contains
only relevant information to execute a similarity based search on the cases. The
CBR cases content represents each CS Case, but contains machine understand-
able information.
For building the vocabulary, we extracted all nouns and organized them in
attribute values, which were directly imported into myCBR 3 and from there
discussed with the experts. Especially the given taxonomies provided great feed-
back, because we were discussing both, the terms as well as their relationship.
Further, the workbench provided great feedback in explaining CBR because the
information the CBR engine uses gets visible. Experts can see local and global
similarity measures as well as they can adjust weightings. After 4 sessions with
the experts we had a status where the case formats and vocabulary was ready
to be deployed in a prototype.
Throughout the project we kept using the workbench when refining case
formats as well as similarity measure until the experts themselves started looking
into the knowledge models themselves.
On the application’s backend, we used the myCBR 3 SDK to develop a
web-based application that searches for similar customer cases after entering all
available machine data and observations. Because of the modularity, we were
able to deploy updated knowledge models smoothly into the application.
4.2 Knowledge Formalisation for Audio Engineering
A case study on creating a case-based workflow recommendation system for au-
dio engineering support was performed in 2013 [15]. In this study the approach
to formalise the special vocabulary used in audio engineering, consisting of vague
descriptors for timbres, amounts and directions, was developed. The study intro-
duced CBR as a methodology to amend the problem of formalising the vagueness
of terms and the variance of emotions invoked by the same sound in different
humans. It was further detailed that the researchers opted for the use of CBR
due to CBR’s ability to process fuzzy and incomplete queries and the ability to
choose between grades of similarity of retrieved results to emulate the vagueness.
The relations between timbres, amounts and effects, were modelled into the lo-
cal similarity measures of the initial CBR knowledge model as they compose the
overall problem description part of what was later used as a case in the resulting
CBR engine.
A challenge during this case study was encountered in the form of the task
of finding an optimal grade of abstraction for the frequency levels in audio en-
gineering within the CBR knowledge model. This was of importance as in any
knowledge formalisation task, one is facing the trade-off between an over en-
gineered, too specific knowledge model and the danger of knowledge loss by
employing too much abstraction e.g. choosing the abstraction levels too high.
The challenge was met by the researchers by choosing two additional abstrac-
tion levels of frequency segments for the timbre descriptors[15].
The next knowledge modelling step consisted of determining the best value
ranges for the numerical attributes which were to be integrated into the initial
knowledge model. After discussing this approach with the domain experts, the
researchers agreed to use two ways to represent amounts in the knowledge model.
The first way used a percentage approach, ranging from 0 to 100% and the second
way used a symbolic approach. The symbolic approach was chosen because the
domain experts mentioned that from their experience the use of descriptors for
amounts, such as ’a slight bit’ or ’a touch’ were by far more common in audio
mixing sessions then a request like ’make it 17% more airy’. So the researchers
integrated, next to the simple and precise numerical approach, a taxonomy of
amount descriptors into the initial knowledge model. The taxonomy was ordered
based on the amount the symbol described, starting from the root, describing
the highest amount down to the leaf symbols describing synonyms of smallest
amounts.
The researchers used the myCBR 3 Workbench to swiftly transfer their ini-
tial elicited knowledge model into a structured CBR knowledge model. Figure 2
provides an insight in the modelling of the local similarity measure for timbre
descriptors. The first figure shows the taxonomic modelling on the left and a sec-
tion from the same similarity measure being modelled in a comparative symbolic
table on the right.
Within myCBR 3 the researchers had the choice between a taxonomic and
a comparative table approach. Considering the versatile use of taxonomies in
structural CBR[5] the researchers initially opted for the use of taxonomies. Yet
regarding the complex similarity relationships between the elicited timbre de-
scriptors the researchers also investigated whether a comparative table approach
for modelling the similarities of the timbre descriptors. Experiments to establish
the performance and accuracy of both approaches yielded no significant differ-
ence in the performance of the similarity measures but taxonomies were found
to be more easily and intuitively elicited from the audio engineer experts.
After the initial knowledge model was created the researchers performed
a number of retrieval experiments using the myCBR 3 built in retrieval test-
Fig. 2. Timbre descriptor taxonomy and comparative table
ing facilities. The goal of these tests was to refine the initial knowledge model,
specifically the similarity measures for the timbre descriptors. Additionally the
researchers used the feedback from domain experts to streamline the case struc-
ture to the most important attributes. This streamlining was performed within
a live system and the researchers were able to directly integrate the streamlined
CBR engine into their Audio Advisor application thanks to myCBR 3 ’s flexible
API.
4.3 Knowledge Formalisation for Hydrometalurgy Gold Ore
Processing
In this case study a twofold approach to elicitating and formalising knowledge
in the domain of hydrometallurgical processing of gold ore was researched. The
study demonstrated processes of formalising hydrometallurgy experts knowledge
into two different CBR knowledge models. The first knowledge model was than
employed in the Auric Adviser workflow recommender software [17].
Based on the knowledge gathered from the domain experts the researchers
created an initial knowledge model and distributed the knowledge into the 4
knowledge containers of CBR in the following way: The vocabulary consisted
of 53 attributes, mainly describing the ore and mineralogical aspects of an ore
deposit. With regard to the data types used, the researchers used 16 symbolic, 26
floating point, 6 boolean and 5 integer value attributes. The symbolic attributes
described minerals and physical characteristics of minerals and gold particles,
such as their distribution in a carrier mineral. Further symbols were elicited to
describe the climate and additional contexts a mining operation can be located
in, like for example the topography.
The cases were distinctive mainly with regard to the mineralogical context
of the mined ore. Thus the researchers created 5 cases describing refractory
arsenopyritic ores, 5 describing free milling gold ores, 2 on silver rich ores, 6
cases on refractory ores containing iron sulphides, 4 on copper rich ores and one
each on antimony sulphide rich ores, telluride ore and carbonaceous ore.
Fig. 3. Example of a similarity measure for the gold distribution within an ore
To compute the similarity of a query, composed of prospective data, and
a workflow case, the researchers modelled a series of similarity measures for
which the researchers had the choice between comparative tables, taxonomies
and integer or floating point functions. For their initial knowledge model the
researchers mainly relied on comparative tables.
The study’s approach included the idea to model as much of the complex
knowledge present in the domain of ore refinement into the similarity measures
as possible. This was based on the assumption that the similarity based retrieval
approach provided by the use of CBR would allow to capture and counter most
of the vagueness still associated with the selection of the optimal process in
the hydrometallurgical treatment of refractory ores domain. For example, it was
possible to model into the similarity measures such facts as that the ore does not
need any more treatment if it contains gold grains greater than 15 micro meters
in diameter. Such facts are easy to integrate into the similarity measure and
thus are operational (having an effect) in the knowledge model. The researchers
deemed this capability of the similarity measures to capture and represent such
‘odd’ behaviours of the knowledge model very important. The study assumes
also that these ‘odd’ facts or bits of knowledge are hard to capture by rules,
and thus has ultimately kept another, rule-based approach of modelling the
hydrometallurgical domain knowledge, IntelliGold, from succeeding on a broad
scale [20].
For the global similarity measure of the cases the researchers used a weighted
sum of the attributes local similarities. This allowed for the easy and obvious
emphasise of important attributes, such as for example ‘ Clay Present’, as the
presence of clay forbids a selection of hydrometallurgical treatments. As the
study mainly aiming for case retrieval, the need for adaptation knowledge was
minor. Therefore the researchers did not formalised any adaption knowledge.
The retrieval results achieved with the first knowledge model was described as
satisfying in accuracy and applicability by domain experts.
5 Conclusion and Future Work
In this paper we have presented the approach to knowledge formalisation within
myCBR 3. myCBR 3 emphasised the fact that myCBR 3 is a very versatile
tool to create CBR knowledge models with a particular versatile suit of editors
for similarity modelling. During the evaluations of the presented projects with
the stakeholders, especially the domain experts found that the GUIs offered by
myCBR 3 are intuitive, particularly with regard that they did not have prior
knowledge of CBR and the required domain knowledge modeling techniques.
Furthermore, also based on experiences from the case studies, we demon-
strated that myCBR 3 allows for on-going knowledge model improvement, even
in a running application. This fact allows also for knowledge maintenance and
refinement in live CBR applications and also enables developers to follow the
rapid prototyping approach in their projects. As shown in previous a research
cooperation with COLIBRI, as well as in a research cooperation on similarity of
event sequences, myCBR 3 is particular versatile for similarity measure based
knowledge modelling. Furthermore myCBR 3 is also easily extendable with re-
gard to its SDK and API to cater for any kind of new similarity measures [14].
For future work we are currently reviewing prototype implementations of
additional features for myCBR 3. These additional features comprise the abil-
ity of automatic extraction of vocabulary items and similarity measures from
web community data, the incorporation of drools for the generation of adaption
knowledge and the incorporation of case acquisition from databases. Furthermore
we are currently finishing the work on the next release of myCBR 3, reaching ver-
sion 3.1. We are also in the process of integrating a mobile version of myCBR 3,
catering for the needs of android application, such as fast access to assets in a
future version of myCBR 3 [16].
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