Ontology as a description method of knowledge management systems classification · 2016-10-03 ·...

TOMASZ ORDYSI�SKI

Uniwersytet Szczeci�ski

ONTOLOGY AS A DESCRIPTION METHOD OF KNOWLEDGE MANAGEMENT

SYSTEMS CLASSIFICATION

Summary

Knowledge management applications can be found in almost every organiza-

tion. This is a result of very wide range of KM domain and marketing practices of

software producers. The number of available knowledge management solutions is

relatively high, what, in case of decision of choosing a proper one, can make the sit-

uation very complex (several factors must be analyzed and considered). The article

contains a reflection of KM tools classification in a form of ontology.

Keywords: ontology, knowledge management, building ontology

1. Introduction

Knowledge management (KM), which in the past was treated rather as “marketing” term, is

nowadays present in almost all areas of organization/company functioning. The definition given by

Ernst & Young: “system, which is designed to support company in gathering, analysis and usage

(or re-usage) of knowledge assets to make faster, wiser and better decisions, what builds compa-

ny’s competitive advantage” in connection with integrated IT solutions (containing e.g. CRM,

SCM or B2B modules) makes the initial theory practically applied. Those systems created very

solid platform for design and implementation of various solutions, which integrates data and

information (presented or stored in different forms). All those efforts were conducted for more

efficient knowledge usage. When we consider others KM definitions, we can come to conclusion,

that almost all IT system in organization can be qualified as a KM tool. This situation causes a

little confusion on software market – software producers very often (following marketing trends)

describe their very simple applications as a knowledge management supporting tools. Cause of this

situation, the decision about choosing proper application (in functionality, finance, support range

or other criteria) can be a hard problem for the manager. The number of offered solutions or

systems, which can support management of the most crucial asset, is relatively high. This causes

the choice even more difficult.

The article presents a research grounds, procedure and results of building a prototype of KM

systems ontology (containing its characteristic with identified features). This description method

enables presentation of complexity of KM software domain and easy classification development in

further research. In following Author’s research, the prototype KM ontology is planned to use as a

knowledge base for expert system, which will support the user in choosing the most suitable KMS

(Knowledge Management System).

158

Tomasz Ordysi�ski

Ontology as a description method of knowledge management systems classification

2. The KM development in organizations

In the past, when KM was not identified in organization management, employees were pre-

cisely doing activities assigned to their position in company/organization. The situation has

changed since the BPR (Business Process Reengineering) was introduced and applied. The idea of

“cost centres” made such an uncommitted employee an useless asset (very often replaced with new

one). However such a practices appeared to be wrong in many cases. Those fired employees had

usually one huge advantage – an experience, which was the result of several years worked in

organization and, with a little encouragement, could be used to create a set of “good practices”.

After some time those mistakes were noticed and different kinds of experts were identified as

a “consultation points”. In that moment the knowledge transfer was started. Consultants were

communicating each other (usually using IT solutions), sharing their knowledge and solving

identified problems. There were established specialized companies (consulting agencies) exchang-

ing their experience and knowledge internally and transferring it outside as a paid service.

The meaning of proper knowledge management was respected many decades ago. Organiza-

tions, analyzing their resources, found out that most of corporate knowledge had not belonged to

them. The consequences of this discovery was giving up the old order and designing new process-

es based on the knowledge acquisition and sharing. 1

The short review of knowledge management history can be presented in following stages:

• 70's, A number of management theorists have contributed to the evolution of knowledge

management:

o Peter Drucker: information and knowledge as organizational resources.

o Peter Senge: "learning organization".

o Leonard-Barton: well-known case study of "Chaparral Steel ", a company having

knowledge management strategy.

• 80's:

o Knowledge (and its expression in professional competence) as a competitive asset was

apparent.

o Managing knowledge that relied on work done in artificial intelligence and expert sys-

tems.

o Knowledge management-related articles began appearing in journals and books

• 90's until now:

o A number of management consulting firms had begun in-house knowledge management

programs.

o The International Knowledge Management Network (IKMN) went online in 1994.

o Knowledge management has become big business for such major international consulting

firms as Ernst & Young, Arthur Andersen, and Booz-Allen & Hamilton. 2

The other theory of the knowledge management evolution identifies 3 main phases. The first

one was based on information usage mainly for supporting decision processes. There were per-

formed many IT initiatives focused on the key aspects of organization, what enabled very fast

information access. When we consider the present highly advanced applications (due to data

integration) we can state that that stage is closed. The second phase focuses in “humanization” of

1 http://www.expertmanage.com/.2 http://www.indianmba.com/Faculty_Column/FC1210/fc1210.html.

159

POLISH ASSOCIATION FOR KNOWLEDGE MANAGEMENT

Series: Studies & Proceedings No. 42, 2011

knowledge management. The previous stage provided the tools, in the second the users must be

persuaded to use them and share owned knowledge. The third phase (which is developing these

days) identifies organization as a complex and dynamic system of connections based more on

social dependencies and cooperation than on strict management and control. 3 (Fig. 1)

First generation:

Document-based KM

Second generation:

People-based KM

Third generation:

System-based KM

Aggregated, organized

and analyzed infor-

mation and data →

Skill of using

knowledge to create

something unique →

Complex phenomenon

emerging from social

system (Beyond the

sum of individuals)

Stored in documents or

data warehouses →Stored in human

brains →

Stored in systematic

interaction and

relations

Extract, capture, store

and disseminate

information →

Interact, share and

Exchange knowledge →

Co-create, Discovery

and trans form sense

& meaning

Made available through

search and retrieval →Made available in

human interactions →

Made available by

understanding the

whole through

conversation and

creating sense &

meaning

Human beings are

reluctant to share their

knowledge →

Human beings are

eager to promote

their expertise →

Human beings depend

on interaction to be

knowledgeable

Produce and provide

information for

national management →

Share and learn for

improvement and

effectiveness →

Understanding &

innovate for sense-

making and impact

Figure 1. Three generations of knowledge management

Source: http://i-p-k.co.za/wordpress/allowing-human-ingenuity-to-unfold/a-conceptual-framework

-of-the-evolution-of-knowledge-management/

3 http://i-p-k.co.za/wordpress/allowing-human-ingenuity-to-unfold/a-conceptual-framework-of-the-evolution-of-knowledge-

management/

160

Tomasz Ordysi�ski


3. IT solutions in knowledge management

The goal of knowledge management is reduction of difference between owned knowledge as-

sets and the required one to reach the highest added value. Traditional companies, which are

willing to become intelligent organizations (using the maximum of possessed knowledge) must

redesign employee’s attitude, organization workflow and business processes. Then all the compa-

ny’s functions must be supported by highly integrated information system. Those IT solutions

must gather knowledge from different sources, codify it, create “added value” and enable

knowledge sharing. 4

There are many IT systems offered on the software market, which in some way support

knowledge management. The very wide definition of KM causes that anyone can call KMS

(Knowledge Management System) any solutions, which creates “added value” (generally process-

es information).

As it was mentioned, the marketing trend used by software producers causes, that almost all

available applications (except transactional systems) are sold as KMS. The paradox is that in

present “information era” KM is present in almost all aspects of organization work. In that case

there appears a question which computer system is not KMS. 5

One of possible directions in the tool’s classification can be due to the ranges of areas covered

by KM. According to Gartner Group (GG) there are following domains of knowledge manage-

ment:

• information and access to information management – supporting codified knowledge man-

agement (structured and unstructured databases, datasets),

• knowledge about processes – knowledge about organizational processes management

• work position based on knowledge – management of knowledge owned by specialists or

knowledge workers (mainly tacit),

• e-business – management of company’s internal and external knowledge integration,

• intellectual capital management – management of values production processes based in

intellectual actives and knowledge capital. 6

The review of literature concerning KM support tools results with very long list of possible

application. For the purposes of prototype ontology the most common types of systems were

chosen and assigned to Gartner Group classification. To the first presented by GG group (infor-

mation and access to information management) we can include:

• document management systems, public folders – it enables documents storage, organization

of edition and browsing, classification and searching,

• Internet, intranet, extranet – as the environment of KMS,

• electronic mail – the oldest KM tool,

• electronic forums, chats – synchronous and asynchronous exchange of opinions,

• tele- or videoconferences – geographical constraints reduction,

4 Kisielnicki J., System pozyskiwania i zarz�dzania wiedz� we współczesnych organizacjach [w:] Zarz�dzanie wiedz� we

współczesnych organizacjach. red. J. Kisielnicki. Monografie i opracowania 4, Wy�sza Szkoła Handlu i Prawa w War-szawie, Warszawa 2003, s. 15–39.

5http://mfiles.pl/pl/index.php/Informatyczne_narz%C4%99dzia_zarz%C4%85dzania_wiedz%C4%85.

6 http://www.gartner.com/technology/

161



• content management systems.

The second type supports knowledge about organization processes management. Here can be

found:

• workflow systems,

• best practices support systems – based on reference models,

• project management systems.

The third class support knowledge based workers and examples are:

• collaborative knowledge network or employee knowledge network,

• case bases – used for CBR (Case Based Reasoning),

• applications supporting creative thinking – mind maps, communication systems,

• information retrieval, categorization, filtering and exploration (document mining), natural

language processing – based on structured datasets,

• data mining. 7

The fourth class is focused on e-business applications. In this group we can find:

• e-service – enabled access into internal data of organizations for identified customers or

suppliers,

• newsletter,

• agent systems,

• ontologies – flexible tool for presentation of information structure and data integration.

The fifth group – intellectual capital management system can be systems:

• competence knowledge base system – based on competence matrix,

• e-learning applications,

• report, statistic or questionnaire systems – enabling monitoring of employee development and

opinion. 8

This presented classification is a kind of functional combination of different types of KM support-

ing tools. In each of pointed type we can find several named solutions being separate products,

modules of bigger systems or integral parts of such a systems. Analysis and making the optimal

choice from such a big number of groups and possible variants can be a tough task. This is

a reason why ontology was proposed as KMS domain description method.

4. Ontology of tools supporting knowledge management

The wide range of IT solutions which support knowledge management causes problem in

identification of features, which they should contain. In the all previously pointed groups (based

on Gartner Group classification) the functionality features will be rather different. For the purpose

of building a prototype ontology of knowledge management tools Author decided to use a model

of KMS architecture, proposed by W. Staniszkis (completed by additional literature studies).9

The research on ontology building was conducted with following order:

7 http://mfiles.pl/pl/index.php/Informatyczne_narz%C4%99dzia_zarz%C4%85dzania_wiedz%C4%85.8 http://ceo.cxo.pl/artykuly/38430_0/Proba.porzadku.w.sprawach.wiedzy.html.9

Staniszkis, W.. Architektura systemu zarz�dzania wiedz�; Praca zbiorowa pod redakcj� Ludosława Drelichowskiego,

2005 s. 186.

162

Tomasz Ordysi�ski


1. analysis of the domain – the results were partially presented in previous points of the arti-

cle,

2. testing and choice of ontology editor,

3. building the ontology of KM supporting tools

4. ontology testing and evaluation.

During the second stage several editors were tested (open source projects and commercial soft-

ware). The results pointed two solutions” OntoStudio and Protégé (versions from 3.1 to 4.1 beta).

Cause of high costs of full version of OntoStudio (only 3 month testing period is available) the

final research editor became Protégé. However comparison test clearly pointed OntoStudio as

more friendly and functional editor for planned research purposes (e.g. SPARQL editor included).

The ontology presented in following points was created in Protege 4.1 Alpha, with reasoners like

HermiT, Fact ++ and Pellet.

The next step was identification of tools supporting knowledge management, their features and

named applications. The results of this stage very clearly presented the complexity of phenomenon

of KM systems, due to their range, functionality and implementation type.

The last stage was implementation of results into Protégé. Each element added was checked by

reasoners to meet the consistency of final ontology. An example RDF/XML code defining the

domain of KM is presented below.

…



<owl:Class

rdf:about="&OntologyOfKnowledgeManagementTools;InformationManagementAndAccessToInf

ormationArea">

<rdfs:subClassOf

rdf:resource="&OntologyOfKnowledgeManagementTools;KnowledgeManagementAreas"/>

</owl:Class>



<owl:Class

rdf:about="&OntologyOfKnowledgeManagementTools;IntelectualCapitalManagementArea">

<rdfs:subClassOf

rdf:resource="&OntologyOfKnowledgeManagementTools;KnowledgeManagementAreas"/>

</owl:Class>

…

Next step was implementation of groups of IT tools, which is the reflection of the list present-

ed in previous point of article. Example code is presented below.

…

163



<owl:Class

rdf:about="&OntologyOfKnowledgeManagementTools;CollaborativeKnowledgeNetworks">

<rdfs:subClassOf

rdf:resource="&OntologyOfKnowledgeManagementTools;TypesOfKnowledgeManagementTools

"/>

<rdfs:subClassOf>

<owl:Restriction>

<owl:onProperty

rdf:resource="&OntologyOfKnowledgeManagementTools;belongsToKnowledgeManagementAre

a"/>

<owl:someValuesFrom

rdf:resource="&OntologyOfKnowledgeManagementTools;KnowledgeBasedWorkArea"/>

</owl:Restriction>

</rdfs:subClassOf>

</owl:Class>



<owl:Class

rdf:about="&OntologyOfKnowledgeManagementTools;CompetenceKnowledgeBaseSystems">

<rdfs:subClassOf

rdf:resource="&OntologyOfKnowledgeManagementTools;TypesOfKnowledgeManagementTools

"/>

<rdfs:subClassOf>

<owl:Restriction>

<owl:onProperty


a"/>

<owl:someValuesFrom

rdf:resource="&OntologyOfKnowledgeManagementTools;IntelectualCapitalManagementArea"/>

</owl:Restriction>

</rdfs:subClassOf>

</owl:Class>

…

164

Tomasz Ordysi�ski


Next of implemented classes was a list of features of KM tools with their possible variants.

…



<owl:Class rdf:about="&OntologyOfKnowledgeManagementTools;RepositorySolution">

<rdfs:subClassOf

rdf:resource="&OntologyOfKnowledgeManagementTools;FeaturesOfKnowledgeManagementToo

ls"/>

</owl:Class>



<owl:Class rdf:about="&OntologyOfKnowledgeManagementTools;XML">

<rdfs:subClassOf

rdf:resource="&OntologyOfKnowledgeManagementTools;RepositorySolution"/>

</owl:Class>

…

The last stage was implementation of ex ample application supporting KM and its description

with previously preapered classes. An example code of Google Apps for Business is presented

below:

…



<owl:Class rdf:about="&OntologyOfKnowledgeManagementTools;GoogleAppsForBusiness">

<rdfs:subClassOf

rdf:resource="&OntologyOfKnowledgeManagementTools;NamedKnowledgeManagementTools"/

>

<rdfs:subClassOf>

<owl:Restriction>

<owl:onProperty


a"/>

<owl:someValuesFrom

rdf:resource="&OntologyOfKnowledgeManagementTools;InformationManagementAndAccessTo

InformationArea"/>

</owl:Restriction>

</rdfs:subClassOf>

<rdfs:subClassOf>

<owl:Restriction>

165



<owl:onProperty rdf:resource="&OntologyOfKnowledgeManagementTools;hasFeature"/>

<owl:someValuesFrom

rdf:resource="&OntologyOfKnowledgeManagementTools;DocumentManagementSystems"/>

</owl:Restriction>

</rdfs:subClassOf>

…

</owl:Class>

…

The whole shape of built ontology of KM tools is presented on the following Picture (Fig. 2).

Figure 2. Ontology of KM supporting tools

Source: Self study.

166

Tomasz Ordysi�ski


5. Conclusions

Designed ontology of the IT tools supporting KM is just the beginning of planned research

program. This attitude enabled Author to reflect the complexity of KM systems domain, leaving

the opportunity of this description opened for further development and corrections. Nowadays KM

in one of the most dynamic segment of software production – so new, fresh ideas are implemented

a can be very easily added to the ontology. The research showed very clearly, that because of long

list of types and big number of named solutions, the decision support in choosing proper KM tool

is strongly recommended. Ontology treated as knowledge base, gives the future expert system

solution platform and software independence.

The conclusions about ontology editors (in this case Protégé) are coherent with common opin-

ions about open source applications – giving very wide functionality they are not free from

mistakes, which have to be removed or bypassed by the researcher by him own. This makes the

ontology building process much longer and discouraging.

The following Authors research will focus on developing built ontology (possibly as open

platform for domain specialists) and later, using it as a source for expert system, supporting the

process of KM tool’s analysis and choice. There is also planned using and modification of

SPARQL for building more natural ontology query language.

[1] Kisielnicki J., System pozyskiwania i zarz�dzania wiedz� we współczesnych organizacjach

[w:] Zarz�dzanie wiedz� we współczesnych organizacjach. red. J. Kisielnicki. Monografie

i opracowania 4, Wy�sza Szkoła Handlu i Prawa w Warszawie, Warszawa 2003, s. 15–39.

[2] Staniszkis,W. Architektura systemu zarz�dzania wiedz�. w: Drelichowski, L. (red.) Studia

i materiały Polskiego Stowarzyszenia Zarz�dzania Wiedz�, Bydgoszcz 2005 s. 186.

[3] http://ceo.cxo.pl/artykuly/38430_0/Proba.porzadku.w.sprawach.wiedzy.html.

[4] http://i-p-k.co.za/wordpress/allowing-human-ingenuity-to-unfold/a-conceptual-framework-of-

the-evolution-of-knowledge-management/

[5] http://mfiles.pl/pl/index.php/Informatyczne_narz%C4%99dzia_zarz%C4%85dzania_wiedz%

C4%85.

[6] http://www.expertmanage.com/

[7] http://www.gartner.com/technology.

[8] http://www.indianmba.com/Faculty_Column/FC1210/fc1210.html.

167



ONTOLOGIA NARZ�DZI ZARZ�DZANIA WIEDZ�

Streszczenie

Aplikacje zarz�dzania wiedz� znale� mo�na ju� praktycznie w ka�dej organiza-

cji. Sytuacja ta wynika z dwóch przyczyn – bardzo szerokiego zakresu poj�cia

zarz�dzania wiedz� oraz marketingowych zabiegów producentów oprogramowania.

Liczba dost�pnych rozwi�za� informatycznych jest bardzo du�a, co w sytuacji ko-

nieczno�ci wyboru konkretnego narz�dzia powoduje spory kłopot (nale�y uwzgl�dni�

znaczn� liczb� cech). Artykuł jest propozycj� uporz�dkowania tego zagadnienia

za pomoc� budowy ontologii rozwi�za� informatycznych wspomagaj�cych zarz�-

dzanie wiedz�.

Słowa kluczowe: ontologia, zarz�dzanie wiedz�, budowa ontologii

Tomasz Ordysi�ski

Institute of IT in Management

The Faculty of Economics and Management

University of Szczecin

ul. Mickiewicza 64, 71-101 Szczecin

e-mail: [email protected]

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