Ontology as a description method of knowledge management systems classification

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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).

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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 transferrexchang-ing 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/.}

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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

→

_{Exchange knowledge}Interact, share and

→

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/

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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 infor(infor-mation 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 zarzdzania wiedz we współczesnych organizacjach [w:] Zarzdzanie wiedz we współczesnych organizacjach. red. J. Kisielnicki. Monografie i opracowania 4, Wysza Szkoła Handlu i Prawa w War-szawie, Warszawa 2003, s. 15–39.

5

http://mfiles.pl/pl/index.php/Informatyczne_narz%C4%99dzia_zarz%C4%85dzania_wiedz%C4%85. 6_{http://www.gartner.com/technology/}

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• 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 besupport-ing 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 zarzdzania wiedz; Praca zbiorowa pod redakcj Ludosława Drelichowskiego, 2005 s. 186.

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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 presentpresent-ed below.

… <!--

http://www.semanticweb.org/ontologies/2010/11/OntologyOfKnowledgeManagementTools.owl# CollaborativeKnowledgeNetworks -->

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<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 rdf:resource="&OntologyOfKnowledgeManagementTools;belongsToKnowledgeManagementAre a"/> <owl:someValuesFrom rdf:resource="&OntologyOfKnowledgeManagementTools;IntelectualCapitalManagementArea"/> </owl:Restriction> </rdfs:subClassOf> </owl:Class> …

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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 rdf:resource="&OntologyOfKnowledgeManagementTools;belongsToKnowledgeManagementAre a"/> <owl:someValuesFrom rdf:resource="&OntologyOfKnowledgeManagementTools;InformationManagementAndAccessTo InformationArea"/> </owl:Restriction> </rdfs:subClassOf> <rdfs:subClassOf> <owl:Restriction>

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<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.

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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 applicatopin-ions – 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.

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[1] Kisielnicki J., System pozyskiwania i zarzdzania wiedz we współczesnych organizacjach [w:] Zarzdzanie wiedz we współczesnych organizacjach. red. J. Kisielnicki. Monografie i opracowania 4, Wysza Szkoła Handlu i Prawa w Warszawie, Warszawa 2003, s. 15–39. [2] Staniszkis,W. Architektura systemu zarzdzania wiedz. w: Drelichowski, L. (red.) Studia

i materiały Polskiego Stowarzyszenia Zarzdzania 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.

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ONTOLOGIA NARZĉDZI ZARZĄDZANIA WIEDZĄ Streszczenie

Aplikacje zarzdzania wiedz znale mona ju praktycznie w kadej organiza-cji. Sytuacja ta wynika z dwóch przyczyn – bardzo szerokiego zakresu pojcia zarzdzania wiedz oraz marketingowych zabiegów producentów oprogramowania. Liczba dostpnych rozwiza informatycznych jest bardzo dua, co w sytuacji ko-niecznoci wyboru konkretnego narzdzia powoduje spory kłopot (naley uwzgldni znaczn liczb cech). Artykuł jest propozycj uporzdkowania tego zagadnienia za pomoc budowy ontologii rozwiza informatycznych wspomagajcych zarz-dzanie wiedz.

Słowa kluczowe: ontologia, zarzdzanie wiedz, budowa ontologii

Tomasz Ordysiski

Institute of IT in Management

The Faculty of Economics and Management University of Szczecin

ul. Mickiewicza 64, 71-101 Szczecin e-mail: tomaszordysinski@gmail.com