Knowledge Management Framework using Wiki-based Front-end

Modules

Catarina Marques-Lucena

1,2

, Carlos Agostinho

1,2

, Sotiris Koussouris

and Jo

ao Sarraipa

1,2

Departamento de Engenharia Eletrot

ecnica, Faculdade de Ci

encias e Tecnologia, Universidade Nova de Lisboa,

2829-516 Caparica, Portugal

Centre of Technology and Systems, CTS, UNINOVA, 2829-516 Caparica, Portugal

School of Electrical & Computer Engineering, NTUA, 9 Iroon Polytechniou str., 15780 Athens, Greece

Keywords:

Semantic Wiki, Tacit Knowledge, Explicit Knowledge, Knowledge Management.

Abstract:

Nowadays organizations have been pushed to speed up the rate of industrial transformation to high value

products and services. The capability to agilely respond to new market demands became a strategic pillar

for innovation, and knowledge management could support organizations to achieve that goal. However, such

knowledge management approaches tend to be over complex or too academic, with interfaces difﬁcult to man-

age, even more if cooperative handling is required. Nevertheless, in an ideal framework, both tacit and explicit

knowledge management should be addressed to achieve knowledge handling with precise and semantically

meaningful deﬁnitions. Contributing towards this direction, this paper proposes a framework capable of gath-

ering the knowledge held by domain experts through a widespread wiki look interface, and transforming it into

explicit ontologies. This enables to build tools with advanced reasoning capacities that may support enterprises

decision-making processes.

1 INTRODUCTION

In the past, employees used to stay in a company for

their entire professional life, and consequently, their

knowledge as well. However, nowadays employees

are switching jobs several times and when they leave,

they take their knowledge with them (Kim, 2005).

As a consequence, organizations must be able to cap-

ture their employees knowledge and experience to be

able to change their personal knowledge into organi-

zational knowledge, so it can be used when they are

no longer with them (Jones and Leonard, 2009).

Knowledge can be considered as information that

has been understood and embedded in the brain.

Thus, it is difﬁcult to transfer between individuals

due its individual oriented nature (Osterloh and Frey,

2000). In this context, researchers consider tacit

knowledge as the background knowledge a person

uses when trying to understand anything that is pre-

sented to him (Polanyi, 1967). Explicit knowledge is

another type of knowledge, which can be expressed

in words and numbers, and can be easily communi-

cated and shared in the form of hard data, scientiﬁc

formulae, codiﬁed procedures or universal principles

(Nonaka and Takeuchi, 1995). By transforming tacit

knowledge into explicit knowledge, it can be con-

sulted and used by a full community, instead of being

locked in a single community’s element. However the

transformation of tacit knowledge into explicit knowl-

edge can be considered one of the most challenging

steps under knowledge management

The more communication, involvement, and in-

teraction of people, more is the chance for organiza-

tions to expose tacit knowledge residing in individu-

als’ heads. Thus, the importance of developing ser-

vices or mechanisms to gather knowledge from do-

main experts has increased. As main actors, they are

who better know how to characterize their domain.

The result of involving them directly in the knowledge

acquisition process and transformation into explicit

knowledge is that tacit knowledge can be managed

through communities’ knowledge processes, namely:

1) strategic planning; 2) decision making; 3) market-

ing; and 4) hiring personnel (Jasimuddin and Zhang,

2013).

In this paper, an initial assessment related to the

necessity of gathering individual’s tacit knowledge

and transforming it into explicit is conducted. Based

on this necessity, a knowledge based establishment

process is proposed where both knowledge engineers

Marques-Lucena C., Agostinho C., Koussouris S. and Sarraipa J..

Knowledge Management Framework using Wiki-based Front-end Modules.

DOI: 10.5220/0005351600790086

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 79-86

ISBN: 978-989-758-096-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

and domain experts contribute to increase communi-

ties’ knowledge (explicit knowledge). This approach

will support a framework for knowledge management

using simple wiki-based front-end modules where

tacit knowledge can be expressed in a form of explicit

knowledge directly by the different employees. After-

wards, an application scenario of the proposed frame-

work followed by some conclusions and future work

statements are presented.

1.1 Related Work

Knowledge management tools are pieces of soft-

ware that enable the user to create, edit or perform

other operations over explicit knowledge forms

(e.g. ontologies). In Youn et al. (2009), is stated

that ontology tools can be applied in all the stages

of the ontology life cycle (creation, population,

validation, deployment, maintenance and evolution).

Some ontology management tools to consider are

Ontopia

, TM4L (Dicheva and Dichev, 2006), and

Prot

. They are all very complete, since they

all provide support to several types of ontology

languages (OWL, RDF, XML) and graphic visual-

ization methods. However, in what concerns domain

experts usage, they may be difﬁcult to use without

knowledge engineers support. For that reason, the

suggested knowledge management approach relies in

the collaborative aspects of Semantic wikis to allow

collaborative knowledge management in a iterative

way by domain experts, which might not have the

technical skill required for complex solutions. By

using widespread and well-accepted wiki technology,

domain experts are able to model and update their

knowledge in a familiar environment by reusing

externalized knowledge already stored in wikis.

Semantic wikis enrich wiki systems for collabora-

tive content management with semantic technologies

(Kr

otzsch et al., 2007). An overview of relevant re-

search can be found in V

olkel (2006), where is pos-

sible to verify that prominent wikis like Semantic

MediaWiki (Kr

otzsch et al., 2006), ikeWiki (Schaf-

fert, 2006), and SemperWiki (Oren, 2005), manage

to disseminate semantic technologies and are used to

support several semantic applications. Thus, domain

experts and ontologies are able to cooperate in one

system while wiki pages are presented in a human-

readable format in parallel to the formal ontologies.

Some works to consider are Vrandecic and Kr

otzsch

(2006), and also Dello et al. (2006). In the ﬁrst work,

the authors gather wiki knowledge by deﬁning a set of

http://www.ontopia.net/page.jsp?id=about

http://protege.stanford.edu/

relations between Semantic MediaWiki annotations

and OWL DL concepts (Vrandecic and Kr

otzsch,

2006). In the latter, the authors also focus on Seman-

tic MediaWiki annotations, but with some interactive

assistance to support users in the knowledge represen-

tation process (Dello et al., 2006). They provide func-

tionality for collaboratively authoring, querying and

browsing Semantic Web information. In both their

works, explicit knowledge is achieved through a set of

mappings that relate with ontological concepts. This

is very powerful when one is aiming to build machine

reasoning and intelligence capabilities. Nevertheless,

in their proposal, all the textual and descriptive infor-

mation is lost, which can be a major drawback when

a feedback loop based on natural language needs to

be maintained with Human users. The proposed work

addresses this challenge complementing the state of

the art by building a knowledge base where not only

annotations are used to create ontological relations,

but also content from wiki articles, gathering natural

language descriptions in data properties, and conse-

quently obtaining a richer representation of a domain.

2 KNOWLEDGE BASE

ESTABLISHMENT PROCESS

The proposed knowledge base establishment process

intends to enable knowledge management features,

able to facilitate the gathering of tacit knowledge and

transforming it into explicit knowledge, ready to be

used by a speciﬁc community. It is a fact that when

an information system intends to represent a domain’s

knowledge it needs to be aligned to the community

that it represents. Consequently it is required to have

a solution where community members could present

their knowledge about the domain and discuss it with

their peers. Additionally, such knowledge must be

available and dynamically maintained by all the in-

volved actors. The proposed knowledge base estab-

lishment process is based on Sarraipa et al. (2014)

and it is presented in Figure 1. As can be observed,

one of the knowledge management approach compo-

nents is an explicit information front-end, where the

knowledge is kept in a format that allows domain ex-

perts to utilize it. In turn, domain experts need to be

able to use the explicit information to turn it into their

own personal knowledge in order to create and share

additional (explicit) knowledge from it. This corre-

sponds to the bottom cycle of Figure 1, which is ag-

gregated through automatic synchronization with the

upper cycle of the ﬁgure, in such way that if there

is new knowledge added by a domain user, it would

smoothly be available in the knowledge base for any

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

further community application (e.g. enhanced search-

ing or reasoning services).

The result is an ontology, whose model is con-

stantly reﬁned accordingly with the explicit informa-

tion front-end module in order to better handle the

knowledge provided by the domain experts. Depend-

ing on the ontology structure, synchronization ser-

vices between the front-end and the ontology are im-

plemented.

Figure 1: Knowledge Management approach.

2.1 Framework for Knowledge

Management

The framework instantiates the knowledge manage-

ment approach and uses ontologies and wiki front-

end modules, able to facilitate the achievement of ex-

plicit knowledge from domain experts’ tacit knowl-

edge. Since the knowledge is constantly reﬁned and

updated by the domain experts’ community, it would

allow to make decisions based on individual’s tacit

knowledge. As can be observed in Figure 2, the

proposed framework is composed by four modules:

1) wiki-based front-end; 2) Synchronization module;

3) Knowledge Base; and 4) Reasoning and Decision

Making module. Framework’s input is the front-end

user’s knowledge, which is processed and consumed

by the community that uses it to re-feed this cycle

with more knowledge.

The ﬁrst module is a wiki-based front-end which

corresponds to the explicit information front-end of

the knowledge base establishment process presented

in Figure 1. It is characterized by being collabora-

tively edited by domain experts based on the knowl-

edge consulted. However, the content of wiki-based

front-ends is characterized by being human readable

only. This means that its content is not formalized

to facilitate computerized use (e.g. reasoning). For

that reason, the synchronization module is composed

by 2 sub-modules: 1) Contents formalization; and 2)

Synchronization. In the contents formalization sub-

module a knowledge formalization methodology is

Figure 2: Framework for Knowledge Management using

wiki-based front-end modules.

required and will be presented in the next sub-section.

The synchronization sub-module is responsible for

the synchronization itself between the knowledge pro-

vided by the domain experts in the front-end and the

domain knowledge base.

The purpose of the ontology, modeled by knowl-

edge engineers, is to hold the explicit knowledge

about a domain in a formalized way so that it can be

used by the community for reasoning purposes. Such

functionality is performed by the module Reasoning

and Decision making, which is able to provide struc-

tured and useful contextual information.

2.1.1 Wiki-based Front-end Contents

Formalization Methodology

Wiki-based front-ends are encyclopaedias that are

collaboratively edited by its users, which contribute

with their (tacit) knowledge. A key factor to extract

knowledge from wiki-based front-ends is that such

pages often follow a global template that facilitates

the retrieval of information. Such front-ends pro-

vide categories that are used to classify articles and

other pages. These categories are implemented by

MediaWiki

. They help readers to ﬁnd, and navigate

around, a subject area, to see pages sorted by title,

and thus ﬁnd articles relationships. One particularity

is that the resulting category system can consist in a

hierarchical representation of categories related, as an

example, by the relation ‘is a’, as the classes in an

ontology.

Organized using several body sections, wikis use

their headings to clarify articles and break the text,

organizing its content (e.g. article title, sections, sub-

sections). Some sections of articles can contain hy-

perlinks, and they point to a whole category, article or

speciﬁc element of an article. A hyperlink between

http://en.wikipedia.org/wiki/MediaWiki

KnowledgeManagementFrameworkusingWiki-basedFront-endModules

several pages, can somehow, be compared to a rela-

tion between instances of an ontology. Therefore, the

organization of an article can be seen as a character-

ization by properties of its content (object and data

properties).

Based on that organization of wiki-based front-

ends the methodology for contents formalization of

Figure 3 is proposed. As can be observed, the step

0 of the methodology consists in the creation of a

wiki root class in the ontology. It will handle the

knowledge represented by the domain experts in the

wiki-based front-end. The process of assigning cate-

gories to other categories, in the proposed methodolo-

gies (step 1), will be used by the knowledge engineers

to build ontology’s classiﬁcation taxonomy, being the

tagging between them handled as the ontological re-

lation ‘is a’. The classiﬁcation of categories’ contents

can be facilitated if a classiﬁcation taxonomy of those

contents is deﬁned (step 2). This will allow to better

structure the gathered knowledge and visualize rela-

tions between knowledge base’s instances.

Figure 3: Methodology for wiki-based front-end contents

formalization.

In this methodology it is assumed that the content

of all pages under a speciﬁc category follows the same

structure. With that assumption, it is possible to fol-

low with the steps 3 and 4 of the methodology. In

step 3 and 4, for each article section is created a data

property or object property to represent that knowl-

edge in the ontology. The object properties created

will connect the classes under the wiki root class and

those under the classiﬁers taxonomy previously de-

ﬁned. Data properties will represent knowledge that

is not under that taxonomy.

The process of assigning articles to categories, in

the proposed methodology (steps 5 and 6) will be used

to instantiate the ontology. This is done by creating

an instance under the class with the article’s category

name (step 5). Then, based on HTML analysis of ar-

ticles’ content, the knowledge of its sections can be

represented in the data and object properties of the

previously created instance (step 6).

The methodology also covers the creation of a new

category on the front-end after the knowledge base is

deﬁned. It is aligned with the necessity of domain

experts to share new kind of knowledge, which is not

formalized yet. An example of how the methodology

here presented is used can be found on section 3.2.

2.1.2 Synchronization between Wiki-based

Front-end Modules and Ontologies

The synchronization module runs periodically and

starts by connecting to the wiki front-end database

in order to verify if any changes occurred since its

last run. JDBC (Java Database Connectivity) is used

to querying the front-end database

. By querying the

wikimedia table ‘recentchanges’, the authors have ac-

cess to the set of changed pages, and its type: edition,

creation, or removal. If the change is an edition or

a creation, through the link to the table text (links to

new & old page text) it is possible to have access to

the current content of the front-end page.

After the collection of the recent changes the

HTML of each article or category’s page is processed

in order to create/ populate the necessary instances,

data properties and object properties in the knowledge

base (steps 5 and 6 of the proposed methodology). In

these steps of the execution ﬂow it is also veriﬁed if

the information remains consistent (e.g. the pages (ar-

ticles) of the same category have the same structure).

After the processing of all detected changes, the up-

date of the ontology is made. This update is made us-

ing Jena OWL API. It provides the necessary classes

and methods to load and save OWL ﬁles and to query

and manipulate OWL data models.

3 SUPPORTING THE EISB

DURING THE ENSEMBLE

PROJECT

ENSEMBLE (Envisioning, Supporting and Promot-

ing Future Internet Enterprise Systems Research

through Scientiﬁc Collaboration)

, was a Support

Action funded by the European Commission (EC)

that coordinated and promoted research activities in

the domain of Future Internet Enterprise Systems

(FInES), providing a sustainable infrastructure for the

FInES community to contribute and support the EISB

(Enterprise Interoperability Science Base) initiative

http://upload.wikimedia.org/wikipedia/commons/

4/41/Mediawiki-database-schema.png

http://www.ﬁnes-cluster.eu/jm/ENSEMBLE-Public-

Category/ensemble-objectives.html

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

(Jardim-Goncalves et al., 2013), as well the 2015

Roadmap (FInES Research Roadmap Force, 2012).

The FInES cluster, now DBI community

, has been

supported by the EC in support of the Digital Agenda

for Europe, a ﬂagship initiative of the Europe 2020

strategy.

The following scenario is related to the gather-

ing of tacit knowledge from the FInES community

and transforming it into explicit knowledge, so that

it could be available to the full community, and sup-

port knowledge intensive initiatives such as the EISB.

To achieve that, a wiki-based front-end (explicit infor-

mation front-end) has been used, the FInESPedia

. It

provides explicit knowledge to the community users

and allows them based on that, to create new tacit

knowledge and post it in the front-end. Moreover, the

knowledge provided by the users is formalized in the

EISB reference ontology.

3.1 FInESPedia

FInESPedia aims at providing an overview of the state

of the art in Future Internet Enterprises Systems. This

source of knowledge, more focused on the collabo-

rative gathering and sharing of information from do-

main experts, is accessible through the FInES cluster

portal

. As can be observed in Figure 4, its homepage

is dived into four main sections, namely: 1) FInES

Research Roadmap 2025; 2) FInES Position Paper

Towards Horizon 2020; 3) Enterprise Interoperability

Science Base (EISB) where this use case is focused;

and 4) FInES Task Forces.

Figure 4: FinESPedia Main Page.

http://www.dbi-community.eu

http://ﬁnespedia.epu.ntua.gr/

http://www.ﬁnes-cluster.eu/jm/

Going into further detail (on the EISB), the FInE-

SPedia is essentially composed by Scientiﬁc Areas,

EISB Glossary and also the Neighbouring Domains

Glossary, that are being synchronized with the EISB

ontology, as explained next. To the formalization of

FInESPedia front-end knowledge, the methodology

of section 2.1.1 was followed.

3.2 Application of the Methodology

The EISB knowledge base is a component that intends

to capture ENSEMBLE community knowledge with

precise and semantically meaningful deﬁnitions. As

explained along the paper, it also serves as a facilita-

tor for knowledge reasoning, allowing different views

of the information gathered from the wiki. Having

this kind of knowledge would facilitate the search of

speciﬁc information, for instance papers or methods

of a speciﬁc EISB area, or a speciﬁc set of tutorials

related to a speciﬁc EISB topic, or even a set of expert

researchers. Furthermore, this ontology can be a valu-

able asset for the scientiﬁc base itself, gathering meta-

information relevant to both Enterprise Interoperabil-

ity and the neighbouring domains (Agostinho et al.,

2009).

3.2.1 Taxonomy Establishment based on the

Methodology

Figure 5 represents the application of the methodol-

ogy to establish the knowledge base taxonomy. As

can be observed, step 0 of the methodology consists

in the creation of the class ‘EISB Wiki’, which will

handle the categories’ taxonomy represented in the

wiki-based front-end.

Figure 5: Knowledge Base’s taxonomy establishment.

The step 1 of the methodology was accomplished

by navigating in the front-end articles’ category clas-

siﬁcation, as was explained in section 2.1.1 It results

KnowledgeManagementFrameworkusingWiki-basedFront-endModules

in the identiﬁcation of four main classes to be handled

under the class ‘EISB Wiki’:

• EISB Glossary - Representation of the contents

of the glossary page of FInESPedia, including:

EI Ingredients, including the detailed information

about the various EISB ingredients (e.g. methods,

tools, experiments); Scientiﬁc Area, regarding the

EISB scientiﬁc areas represented in the wiki page;

and Scientiﬁc SubAreas, regarding scientiﬁc sub

areas represented in the FInESPedia (Lampathaki

et al., 2012);

• EISB Neighbouring SDRG - Serves the same pur-

pose of the EISB Glossary, but refers to the Neigh-

bouring domains instead; (see Agostinho et al.

(2014) for technical details on the neighbouring

domains);

• Publication -Information regarding the publica-

tions presented in FInESPedia;

• Researchers -Information about the researchers

acting in the EISB community.

Step 2 of the methodology consists in the cate-

gories contents’ classiﬁers. This is a knowledge en-

gineers’ works in which they analyze the knowledge

that the domain experts want to represent in order to

create a classiﬁers taxonomy from it. The four main

classes of the classiﬁers taxonomy are:

• EI Contents Categorization - that aims to repre-

sent the information about the different categories

that the content of the wiki can take, namely: In-

teroperability Maturity, which holds the informa-

tion about the various maturity models available;

Development Lifecycle, which houses the infor-

mation about the different development phases of

certain publication (Assessment, Design, Imple-

mentation); and Interoperability Barriers, Indicat-

ing which type of EI barrier is targeted accord-

ingly with the image of the ISO standard 11354

(ISO/TC 184/SC 5, 2011);

• Content Classiﬁer - which stores information rel-

ative to classiﬁcations of the EISB contents: EI

Barrier Classiﬁers, which assigns (High-Low) rel-

evance of a certain content regarding its interop-

erability barrier (e.g. Technical- High); EI Ma-

turity Classiﬁer, which has the information rela-

tive to the maturity of the wiki content (e.g. ma-

ture, infant, ); Phase Classiﬁer, which classiﬁes

publications relatively to its development lifecy-

cle (e.g. Design-High); and Scientiﬁc Area Clas-

siﬁer, which classiﬁes a wiki content with the rel-

evance pertaining to a certain scientiﬁc area (e.g.

Data Interoperability - Medium);

• EISB Framework -the purpose of this class is to

hold information about the elements that com-

pose the EISB universe. It handles the knowledge

about the framework components: EISB Knowl-

edge Base (the scope of the previous descrip-

tions); EISB Problem Space; and EISB Solution

Space (Hypothesis, Laws, etc.) (Agostinho et al.,

2009).

Figure 6: New Publication demonstration Scenario.

3.2.2 Ontology Properties Establishment based

on the Methodology

In this subsection, the steps 3 and 4 of the methodol-

ogy are demonstrated. The type of pages that were

selected to exemplify the methodology were those

under the category ‘Publications’. It was assumed

that the articles under this category follow the same

structure of the page illustrated in the top of Fig-

ure 6. Concerning its content and the classiﬁers

taxonomy established on step 2, the data properties

(green dotted areas of Figure 6) deﬁned are: ‘Ab-

stract’; ‘FINES Page’; ‘Keywords’; ‘HasLicence’;

‘Link Mendeley’; and ‘Name’ (step 3 of the method-

ology). The object properties (blue line continued ar-

eas) deﬁned were: ‘hasIngredient’; ‘IsClassiﬁedAs’;

and ‘related to Bibliography’.

3.3 Ensemble’s Knowledge Base

Synchronization

After structuring the information retrieved from the

wiki front-end, and concerning the scenario of a new

publication creation, it is possible to do the synchro-

nization between the front-end and the ontology in or-

der to populate the knowledge base with domain ex-

perts’ knowledge. The synchronization tool is trig-

gered by a ‘cron job’ that runs daily. Then, the recent

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

changes are analysed in order to verify if there is any

new publication in the FInESPedia. That veriﬁcation

is made by analysis of the HTML content of the pages

to verify in which category the page belongs.

The wiki front-end to ontology synchronization of

a new publication is illustrated in Figure 6 It is possi-

ble to verify that the various sections of the wiki page

have a direct correspondence in the ontology (result

of knowledge engineers work), and all the contents

are therefore successfully migrated. It is also possi-

ble to verify that the object property ‘IsClassiﬁedAs’

relates the wiki pages content with a taxonomy under

the classiﬁers deﬁned in step 2.

After contents formalization, the knowledge man-

agement framework is capable of handle articles’ cre-

ation, edition and elimination without the interven-

tion of knowledge engineers. However, if other non-

modelled category occurs (other type of tacit knowl-

edge), knowledge engineers need to re-follow the

proposed knowledge structuring methodology. In

such way, the new sub-domain knowledge inserted by

the domain experts can be transformed into explicit

knowledge to be presented to the community.

3.4 Enabling the Integration with Other

Works

By having explicit knowledge formalized, it also

becomes much easier to integrate complementary

knowledge. In the case of ENSEMBLE this situa-

tion became very clear with the example of the FInES

2025 roadmap. Like the EISB, also the roadmap has

been supported by an ontology for knowledge man-

agement with a wiki front-end (in this case only to

visualize information). Due to both ontologies links

were easily deﬁned between knowledge domains, en-

abling readers to navigate through the wiki between

the roadmap and the EISB knowledge, increasing

their awareness of the FInES and enterprise interop-

erability domains.

4 CONCLUSIONS

To increase the competitiveness level, organizations

must be able to keep its employees knowledge inside

the organization, even when they leave. The same

happens with researchers and scientists so that the

community can capitalize their knowledge. Hence,

responding to those needs, a knowledge management

framework based on wiki front-end modules was im-

plemented. With the proposed framework, domain

experts can actively contribute to the knowledge of

their community through a simple and, considerably

well-known interface, as wiki-based front-ends. This

kind of front-ends are known by being easy to setup

and use, tracking of changes, and on-the-ﬂy publish-

ing. Thus are being largely selected to share knowl-

edge in several areas like teaching (Parker and Chao,

2007), collaborative modeling (Dengler and Happel,

2010), process development (Dengler et al., 2009),

and others. However, beside the mentioned advan-

tadges, wikis are also characterized for being human

readable only. This means that its content is not for-

malized to facilitate computerized use, an issue ad-

dressed by Semantic wikis. Most of the state of the

art solutions are based on mappings between semantic

annotations and ontological relations. The presented

solution is able to complement that, handling all of

the wiki articles content in natural language.

This paper proposes to use simple web-based in-

terfaces, in the form of wiki modules, which allow do-

main experts to contribute with their tacit knowledge

through an intuitive front-end. That knowledge is

then transformed into explicit knowledge, in the form

of ontologies, following a semi-automatic methodol-

ogy. With this process, knowledge becomes avail-

able for querying and intelligent reasoning. Other

knowledge bases can be integrated, providing users

extended awareness of the domain and enriched feed-

back information that can motivate the reﬁnement of

the front-end and more suitable decisions.

The Authors applied successfully these ideas in

the ENSEMBLE case and are currently working on

forms to decrease the level of participation of knowl-

edge engineers in tacit knowledge gathering. Cur-

rently, in order for a new concept to be detected and

formalized in the ontology, knowledge engineers need

to be constantly verifying the wiki contents and manu-

ally instruct the synchronization tool to recognize and

handle such knowledge. In future work, the authors

plan to automatize such procedure. Moreover, when

the knowledge is gathered by a full community, an ar-

ticle can be edited several times and, it may be useful

to keep track of those changes in the ontology (e.g.

versioning mechanism). As an instance, the amount

of changes that a page suffers in a speciﬁc period of

time can be an indicator of a community interest in a

speciﬁc topic. Also, the authors intend to apply this

approach to other domains of knowledge management

(e.g. requirements engineering, collaborative educa-

tion curriculum creation, etc.).

ACKNOWLEDGEMENTS

The research leading to these results have received

funding from the European Union 7th Framework

KnowledgeManagementFrameworkusingWiki-basedFront-endModules

Programme (FP7/2007-2013) under grant agreement:

ENSEMBLE

n 257548, ALTERNATIVA

, and

also through OSMOSE

nr 610905 which is enabling

to continue this line of research.

REFERENCES

Agostinho, C., Jardim-Goncalves, R., Sarraipa, J. a., and

Lampathaki, F. (2009). EnSEMBLE Envisioning , Sup-

porting and Promoting Future Internet Enterprise Sys-

tems Research through Scientiﬁc Collaboration EISB

Models & Tools Report.

Agostinho, C., Jardim-Goncalves, R., and Steiger-Garcao,

A. (2014). Underpinning EISB with Enterprise Interop-

erability Neighboring Scientiﬁc Domains. In Revolution-

izing Enterprise Interoperability through Scientiﬁc Foun-

dations, pages 41–76. IGI Global.

Dello, K., Simperl, E. P. B., and Tolksdorf, R. (2006). Cre-

ating and using Semantic Web information with Makna.

In Proceedings of the First Workshop on Semantic Wikis

– From Wiki To Semantics, Workshop on Semantic

Wikis. ESWC2006.

Dengler, F. and Happel, H.-J. (2010). Collaborative mod-

eling with semantic mediawiki. In Proceedings of the

6th International Symposium on Wikis and Open Collab-

oration, WikiSym ’10, pages 23:1–23:2, New York, NY,

USA. ACM.

Dengler, F., Lamparter, S., Hefke, M., and Abecker, A.

(2009). Collaborative process development using seman-

tic mediawiki. In Proceedings of the 5th Conference

of Professional Knowledge Management, Solothurn,

Switzerland. GI.

Dicheva, D. and Dichev, C. (2006). Tm4l: Creating and

browsing educational topic maps. British Journal of Ed-

ucational .

FInES Research Roadmap Force (2012). FInES Research

Roadmap 2015.

ISO/TC 184/SC 5 (2011). Advanced automation technolo-

gies and their applications Part 1 : Framework for en-

terprise interoperability. (ISO/DIS 11354-1:2011).

Jardim-Goncalves, R., Grilo, A., Agostinho, C., Lam-

pathaki, F., and Charalabidis, Y. (2013). Systematisation

of Interoperability Body of Knowledge: the foundation

for Enterprise Interoperability as a science. Enterprise

IS, 7(1):7–32.

Jasimuddin, S. M. and Zhang, Z. J. (2013). Knowledge

management strategy and organizational culture. Journal

of the Operational research society, pages —-.

Jones, K. and Leonard, L. N. K. (2009). From Tacit Knowl-

edge to Organizational Knowledge for Successful KM.

http://www.ﬁnes-cluster.eu/jm/ENSEMBLE-Public-

Category/ensemble-objectives.html

DCI-ALA/19.09.01/10/21526/245-575/ALFA III

(2010) 88

http://www.osmose-project.eu

In Knowledge Management and Organizational Learn-

ing, volume 4 of Annals of Information Systems, pages

27–39. Springer.

Kim, S. (2005). Factors Affecting State Government In-

formation Technology Employee Turnover Intentions.

American Review Of Public Administration, 35(2):137–

156.

otzsch, M., Schaffert, S., and Vrande

c, D. (2007). Rea-

soning in Semantic Wikis. In Proceedings of the 3rd Rea-

soning Web Summer School, Dresden, Germany, volume

4636 of LNCS, pages 310–329. Springer.

otzsch, M., Vrandecic, D., and V

olkel, M. (2006). Se-

mantic MediaWiki. In International Semantic Web Con-

ference, volume 4273 of Lecture Notes in Computer Sci-

ence, pages 935–942. Springer.

Lampathaki, F., Koussouris, S., Agostinho, C., Jardim-

Goncalves, R., Charalabidis, Y., and Psarras, J. E. (2012).

Infusing scientiﬁc foundations into Enterprise Interoper-

ability. Computers in Industry, 63(8):858–866.

Nonaka, I. and Takeuchi, H. (1995). The Knowledge-

Creating Company. How Japanese Companies Create

the Dynamics of Innovation. Oxford University Press,

New York.

Oren, E. (2005). SemperWiki: a semantic personal Wiki. In

Proceedings of the 1st Workshop on The Semantic Desk-

top at the ISWC 2005 Conference, pages 107–122, Gal-

way, Ireland.

Osterloh, M. and Frey, B. S. (2000). Motivation, Knowl-

edge Transfer, and Organizational Form. Technical re-

port, Institute for Empirical Research in Economics -

University of Zurich, IEW - Working Papers.

Parker, K. R. and Chao, J. T. (2007). Wiki as a teaching

tool. Interdisciplinary Journal of Knowledge and Learn-

ing Objects, 3:57–72.

Polanyi, M. (1967). The Tacit Dimension. Terry lectures.

Anchor Books.

Sarraipa, J. a., Marques-Lucena, C., Baldiris, S., Fabre-

gat, R., and Aciar, S. (2014). The ALTER-NATIVA

Knowledge Management Approach. Journal of Intelli-

gent Manufacturing.

Schaffert, S. (2006). IkeWiki: A Semantic Wiki for Col-

laborative Knowledge Management. In Enabling Tech-

nologies: Infrastructure for Collaborative Enterprises,

2006. WETICE ’06. 15th IEEE International Workshops

on, pages 388–396.

olkel, M., editor (2006). Proceedings of the First Work-

shop on Semantic Wikis – From Wiki To Semantics,

Workshop on Semantic Wikis. ESWC2006.

Vrandecic, D. and Kr

otzsch, M. (2006). Reusing On-

tological Background Knowledge in Semantic Wikis.

SemWiki.

Youn, S., Arora, A., Chandrasekhar, P., Mestry, P., and

Sethi, A. (2009). Survey about ontology development

tools for ontology-based knowledge management. Uni-

versity of South California.

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems