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2 LITERATURE REVIEW
Based on literature review using keyword search of
“KM lifecycle” from the Elsevier SDOS online
database, we found 10,014 articles on this topic.
This was done on June 1, 2014. After topic filtering,
only 73 articles covered the scope of this paper and
were KM technology specific and supported KM
lifecycle processes i.e. knowledge identification,
knowledge capture, knowledge codification,
knowledge storage, knowledge dissemination and
sharing. Several tools were introduced to support
KM technology implementation with regards to KM
lifecycle stages. In summary, based on the 73
articles mentioned earlier, we used Protégé for
knowledge identification, codification and capture
based on theory (Nonaka et al, 2001), (Wiig et al,
1997), (Fernandez-Breis, 2000), (Allsopp et al,
2002) and Wilkins et al (1997).
Jess (Java Expert Shell System) was applied in
the context of knowledge representation (Cauvin,
1996), Kim et al (2000) and knowledge capture
(Wielinga et al, 1997). SWOOGLE was used in the
context of search (Knight and Ma, 1997) and
indexing (Jiang el al, 1999). CLIPS and PAL
(Protégé Axiom Language), Algernon and SWRL
(Semantic Web Rule Language) assisted with
knowledge representation (Cauvin, 1996), Kim et al
(2000) and machine leaning (Zhong and Ohsuga,
1996). External reasoning engine (i.e. Racer Pro)
was applied for executing rules, checking
consistency and integrity in the Ontology
implemented. Description of each tool and how they
were used to meet the assignments assigned and
overall curriculum objectives are elaborated in the
following sections.
2.1 Protégé
Protégé is an open source platform-independent
ontology editor developed by Stanford University.
It’s a very useful tool for creating and editing
ontologies (Wiig et al, 1997), (Fernandez-Breis,
2000), (Allsopp et al, 2002) and Wilkins et al (1997)
and knowledge bases from scratch. The following
features in Protégé are reasons that make it
appropriate for Protégé to be used as a classroom
technology for E-KM:
a) Easy to use graphical user interface (GUI).
b) The ability to scale up with virtually no
performance degradation even if several
hundreds of frames are loaded into its
database all at the same time.
c) Several additional plug-ins can be easily
added into the Protégé framework as
components that perform reasoning,
matching, alignment and graphical
representation. To the best of my knowledge
I have not known any other tool that can
perform the same functions as Protégé does.
Students were first taught for several weeks
(about 20 face-to-face contact hours) on the concepts
and actual implementation process of a knowledge
base from scratch. The instructor used several
examples from the Protégé sample ontologies
available in this tool. The wine, newspaper and pizza
ontologies helped to provide a better understanding
of classes, sub classes, slots, inverse slots, instances,
data type definitions and relationships. In the first
assignment, a student was given three weeks to build
and implement ontology of their choice based on
principles taught in the face-to-face session. The
Protégé version used for assignment 1 was an earlier
version i.e. 3.4.1 so as not to confuse students with
OWL (Web Ontology Language) definitions which
they were not ready to comprehend. Assignments 2
and 3 were based on the 3.4.2 version.
2.2 JESS
Jess (Java Expert Shell System) is a rule engine and
scripting tool developed by Ernest Friedman-Hill at
Sandia Laboratories. Since Jess was always free for
educational purposes, it became an ideal choice to be
used in this course. Protégé provides a component
plug-in i.e. Jess Tab that can easily be configured for
executing Jess rules (Cauvin, 1996), within the
Protégé environment. Jess is an effective tool for
building intelligence into an existing knowledge
base. This can be done via an expert system rule
engine (Zhong and Ohsuga, 1996) that applies rules
on a collection of facts. Jess uses a special algorithm
i.e. Rete to match rule to given facts. This tool was
introduced to the students in subsequent meetings to
meet the requirements of assignments 2 and 3.
Students were first trained to use Jess for two
meetings before they could use it. Jess allows
forward and backward chaining and supports LISP
(LISt Processing) like syntax. Students were given
other options such as SWRL and PAL to implement
rules into their ontology if they did not want to use
Jess for any reason. An example of SWRL Jess Tab
is shown below.
2.3 SPARQL
SPARQL (SPARQLProtocol and RDF Query
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