SEMANTIC INTEROPERABILITY OF E-SERVICES

IN COLLABORATIVE NETWORKED ORGANIZATIONS

Nikos Papageorgiou, Yiannis Verginadis

Institute of Communications and Computer Systems, National Technical University of Athens

9 Iroon Polytechniou Str., Athens, Greece

Dimitris Apostolou

Informatics Department, University of Piraeus, Karaoli & Dimitriou Str., Piraeus, Athens, Greece

Gregoris Mentzas

Institute of Communications and Computer Systems, National Technical University of Athens

9 Iroon Polytechniou Str., Athens, Greece

Keywords: Top level ontology, Virtual organization, Networked Collaboration.

Abstract: Semantic interoperability is a crucial issue in enterprises when they participate in Virtual Organizations

(VOs). Addressing semantic heterogeneities, detected in VOs, aims to ensure that the meaning of

information exchanged is interpreted in the same way by all communicating parties and their systems. In

this paper we examine how ontologies can be employed by a system of e-services for delivering

interoperability to enterprises, independent of particular IT deployments. In order to support interoperability

service utilities in VOs, this paper presents a top-level ontology for collaborative networked organizations

(code named OCEAN). The OCEAN ontology is designed as a lightweight top-level ontology that provides

a common terminological reference for e-services supporting VO collaborations. We demonstrate how the

usage of OCEAN enables e-service interoperability in knowledge-intensive collaborations presenting

concrete examples from the pharmaceutical industry.

1 INTRODUCTION

Enterprises wishing to take part in collaborative

networks participate in formations often referred to

as Virtual Organizations (VOs) (Davidow et al.,

1992), (Mowshowitz, 1997). A VO is a short-term

association with a specific goal of acquiring and

fulfilling a collaboration opportunity. A key

underpinning of VOs is the logical separation of VO

members’ requirements (e.g., requests for

information, advice, or transactions) from satisfiers

(e.g., information services, collaboration services, or

transactional services) (Mowshowitz, 1997). Having

such a capability allows management to continually

examine service requirements, scan for matching

service offerings and switch the assignment of

satisfiers to requirements so as to optimize

performance on the basis of explicit criteria such as

reducing service delivery costs or improving service

quality. Since each VO member undertakes

particular sub-processes in the joint effort,

information and services for enabling knowledge-

based collaboration should be available in an

interoperable way. Towards this end, adequate

semantic interoperability has to be established by

means of a common frame of reference or at least a

common terminology (Chituc et al., 2008).

Advances in Semantic Web (Berneers-Lee,

2007) technologies, which enable machines to

process and reason about resources in support of

businesses interactions, have paved the way for

ontology-based platforms enabling semantic

interoperability between heterogeneous information

systems. In this paper we examine how ontologies

can be employed by a system of e-services for

delivering interoperability to enterprises,

221

Papageorgiou N., Verginadis Y., Apostolou D. and Mentzas G. (2010).

SEMANTIC INTEROPERABILITY OF E-SERVICES IN COLLABORATIVE NETWORKED ORGANIZATIONS.

In Proceedings of the International Conference on e-Business, pages 221-228

DOI: 10.5220/0002935502210228

 SciTePress

independent of particular IT deployments.

The main contributions of the paper are the

following. First, the paper proposes an ontology

representing VO objects, processes, roles and

relationships as a formal framework for enabling

resolution of semantic heterogeneities. Second, the

paper presents the methodology that we have used

for customizing the ontology for the particular needs

of VOs and for achieving consensus of the shared

conceptualization of a VO among participants.

Third, concrete examples from the pharmaceutical

industry are used to demonstrate the applicability

and benefits of the proposed ontology and

architecture in supporting VO collaboration.

The remaining of the paper is organized as

follows: In section 2 we discuss the emerging need

for supporting semantic interoperability in VOs,

while in section 3 the methodology used for the

development of the OCEAN ontology are presented.

In section 4 we present the main concepts of this

ontology. Finally, our paper concludes with the

application of our work in the pharmaceutical

industry in section 5 and with a discussion on the

research implications and conclusions in section 6.

2 SEMANTIC

INTEROPERABILITY IN

VIRTUAL ORGANIZATIONS

2.1 Interoperability and Ontologies

In the context of networked enterprises (i.e.

enterprises that participate in a VO), interoperability

refers to the ability of interactions (exchange of

information and e-services) between enterprise

systems. The Enterprise Interoperability Research

Roadmap – EIRR argues that interoperability of

enterprises in future business ecosystems will be a

utility-like capability that enterprises can invoke on

the fly in support of their business activities. The

European Commission uses the term Interoperability

Service Utility (ISU) to denote a basic

“infrastructure” that supports information exchange

between diverse knowledge sources, software

applications, and Web Services.

Current interoperability solutions are often

oriented toward integration of data required for

executing a common business goal, often specified

in terms of a contract. Protocols and standards such

as ebXML(2009), Electronic Data Interchange (EDI,

2009), and RosetaNet (2009) have been enablers for

the progress made in the ability to integrate

heterogeneous information and data.

But, semantic interoperability aims to achieve a

more ambitious goal, that is to assure that the

meaning of the information exchanged (e.g.,

business documents, messages) is interpreted in the

same way by the communicating systems (Chituc et

al., 2008). For addressing semantic heterogeneity it

is essential that the semantic definitions of the

knowledge objects, processes, roles and

relationships within VOs are defined based on a

mathematically rigorous ontological foundation (Lin

et al., 2007). Moreover, as VO members might come

from different fields or have different professional

backgrounds, it is necessary to introduce a

mechanism to share common understanding of

knowledge, and to agree on a controlled vocabulary.

An ontology provides a representation of

knowledge, which can be used in order to facilitate

the comprehension of concepts and relationships in a

given domain, the communication between VO

members by making the domain assumptions

explicit and the resolution of semantic

heterogeneities between VO systems.

2.2 Existing Approaches

Αmong the wide spectrum of approaches which

differ in the amount of information and specificity,

four categories of approaches can be distinguished

for developing ontologies i.e., top level, domain,

task and application level ontologies (Huang et al.,

2007), (Rajpathak et al., 2006), (Andersson, 2006).

Top level ontologies are used to represent the

building blocks for a particular domain and basically

constitute the first step toward knowledge

representation for a domain. Basically, this kind of

ontology is limited to concepts that are meta,

generic, abstract and philosophical, and therefore are

general enough to address (at a high level) a broad

range of domain areas. In the last decade, many

projects aimed at creating top level ontologies for

different purposes: word net (Fellbaum,1998),

SUMO (Niles et al., 2001), DOLCE (Gangemi et al.,

2002), AIAI Enterprise Ontology (Uschold et al.,

1998), PROTON (Kiryakov, 2006), ECOLEAD

(Plisson et al., 2007) and the Business Management

Ontology (BMO), TOVE ontologies for enterprise

modeling (TOVE), and the DIP Business Data

Ontology (DIP) and ontologies for enterprise

interoperability (Ruokolainen et al., 2007), (Castano

et al., 2006).

Among these most relevant to our work is the

ECOLEAD ontology which proposes an ontology

for Virtual Breeding Environments, which are long-

ICE-B 2010 - International Conference on e-Business

222

term associations of enterprises that have the

potential and the will to form a VO. The OCEAN

ontology builds upon and extends the ECOLEAD

ontology to cover the creation, operation and

termination phases of VOs. In particular, we focus

on knowledge-oriented collaboration within VOs

and subsequently OCEAN aims to enable

interoperability of systems providing e-services for

enabling knowledge-based collaboration.

3 METHODOLOGY

Among the various ontology development methods

that have been proposed (Cristani et al., 2005), we

opted for a collaborative method because it

addresses the objective of achieving a shared

representation of domain knowledge. Following the

ontology development framework proposed in

(Holsapple at al., 2002), we aimed to support

domain experts to reach consensus through iterative

evaluations and improvements of an initial ontology.

Before starting designing the initial ontology, we

did an extensive literature review and discussed with

domain experts about the scope of the top-level

ontology. Domain experts were carefully selected in

order to complement each other and represent

diverse viewpoints resulting to a group of five

academics and five practitioners with extensive

experience in VOs.

To design the initial ontology we used the

ECOLEAD top-level ontology as a starting point for

our work. We then utilized ontology learning tools

to analyze a corpus of 79 papers from the related

literature with the aim to identify important terms

and relationships between terms. This process has

been leveraged with Text2Onto (Cimiano et al.,

2005), an ontology learning tool. We then identified

the terms of the ontology and derived class

definitions and class hierarchy. We followed the top-

down approach and took into account suggestions

for class hierarchies provided by Text2Onto. Next,

we determined the properties of classes; suggestions

for object properties from Text2Onto were again

taken into account. Finally, we determined the

restrictions of the data type and the object properties.

Having the initial ontology at hand, we worked with

experts to evolve the initial version by asking them

to evaluate it and finally reach consensus and agree

upon the final version. To reach consensus between

experts that were not co-located and did not

collaborate synchronously, we followed an

adaptation of the Delphi method (Fitch et al., 2001),

a technique which involves multiple iterative rounds

of anonymous responses to a questionnaire until

either the opinions converge or until no further

substantial change in the opinions can be elicited. In

each round, participants were asked to rank using a

5-point Likert scale each concept, and each

taxonomic and non-taxonomic relation of concepts

for relevancy to the project and for ambiguity.

Moreover, for each concept synonyms were

collected in order to broaden the vocabulary of the

domain. Finally, participants could enter new

concepts and relations in each round which were

then fed again into the evaluation process. The

facilitator provided details about particular items for

which no consensus was reached and participants

rated them again. The iterative process continued

until all participants agreed on all items.

4 THE OCEAN ONTOLOGY

The Ocean ontology aims to represent a conceptual

schema of the domain of VOs typically referred to as

terminology box or TBox. The domain of VOs

includes concepts such as collaborative network

organization, virtual breeding environment and

business opportunity that model the external

environment in which VOs are being bred; such

concepts are modeled in the ECOLEAD ontology.

OCEAN mainly focuses on knowledge-oriented

collaborations apposite for VOs. Nevertheless, to

fully cover the domain of VOs, we have used the

part of the ECOLEAD ontology which covers

extensively the VO breeding environment and built

upon it towards a unified model that captures the

general aspects of collaborative network

organizations and at the same time present details

about knowledge-oriented collaborations that are

important during the creation, operation and

termination phases of VOs .

For developing the OCEAN ontology we have

used Protégé (Protégé) and for validating it we have

used the OWL-DL reasoner Pellet (Pellet). Pellet

provides reasoning services and performs

consistency checking and computation of inferred

hierarchies, equivalent classes and inferred

individual types (Sirin et al., 2007). Due to spatial

restrictions we can not depict the whole (53 terms

and 77 relationships were identified and modeled) of

OCEAN; instead we depict the critical concepts,

only. The OWL-DL representation of the complete

OCEAN top level ontology is available online at:

http://www.imu.iccs.gr/ontologies/ocean/. We have

categorized the critical OCEAN concepts into:

Breeding Environment related OCEAN concepts and

SEMANTIC INTEROPERABILITY OF E-SERVICES IN COLLABORATIVE NETWORKED ORGANIZATIONS

223



Figure 1: Breeding Environment related OCEAN concepts.

Service & Collaboration related OCEAN concepts

(refer to knowledge-enabled collaboration services).

4.1 Breeding Environment Related

OCEAN Concepts

We have organised the presentation of OCEAN by

putting first concepts and relationships that describe

the VO’s breeding environment, as a necessary

artifact to describe the full picture of the domain

(figure 1). The highlighted concepts were taken from

the ECOLEAD ontology, while the remaining

concepts and relationships appear as extensions.

Some of the breeding environment related OCEAN

concepts and relationships are:

A virtual organization is a short-term association

(of organizations) with a specific goal of acquiring

and fulfilling a collaboration opportunity. A

VOmember represents an entity collaborating with

other entities in the VO (Plisson et al., 2007). In

simpler words VOmembers are the organizations

which participate in a VO. A virtual organization is

bread in a VBE, an association of organizations and

their related supporting institutions, which have both

the potential and the will to cooperate with each

other through the establishment of a base long-term

cooperation agreement and interoperable

infrastructure (Camarinha-Matos et al., 2005). VO’s

aim is to deliver Products (anything an organization

may produce: goods or services), has a

CommonGoal, undertakes a Project, uses

CollaborativeMethodsAndTools and exploits a

CollaborationOpportunity. With the term

CollaborativeMethodsAndTools we define all the

synchronous or asynchronous tools and methods that

are going to be developed in terms of a system to

support and enhance collaboration within a VO.

Every VOmember has (or should have)

CollaborationCapability which declares the

capability that is relevant to the participation of an

enterprise in collaboration with partner enterprises.

It includes both HR capabilities of personnel

involved in management and operation of

collaborative activities, and interoperability of

software systems. The concept of

CollaborationCapability concerns mainly the pre-

creation phase of a VO (i.e. identification phase for

(Plisson et al., 2007)) as it focuses on the knowledge

about the capability of future VO partners to

collaborate. A critical factor, that is often

disregarded in efforts that describe and support VOs,

is the fact that two potential partners may be unable

to collaborate, although they appear to have all the

necessary assets for participating in a specific VO

(e.g. two partners that had unsuccessful

collaborations in previous VOs, partners that have

been engaged in lawsuits against each other etc.).

Within the system that will use the OCEAN top

level ontology, a VO may use an ISUService

(described in the next section).

The structure of a VO is described with the term

topology which stands for the arrangement of the

participants inside the VO (e.g. Star Alliance: A

grouping of independent organizations, with a core

organization taking the lead). By declaring that a VO

ICE-B 2010 - International Conference on e-Business

224

ISU ISUService

GroupSupportService IntelligentService KMService CollaborationPatternService

DecisionMakingService

ConsensusBuildingService

ConflictResolutionService

Opportunity

Risk

Knowledge

CollaborationKnowledge

CollaborationPattern

isa

isa isa isa

isa

Condition CPatCategory ApplicationArea ComplexEvent

isProvidedBy*

assessesRisk*

detectsOpportunity* managesKnowledge*

exploitsCPats*

hasPreCondition*

hasPostCondition*

hasCategory*

hasApplicationArea*

hasTrigger*

Figure 2: Service & Collaboration related OCEAN concepts.

is a kind of CNO we express that a VO is a

collaborative network of organizations.

4.2 Service & Collaboration Related

OCEAN Concepts

In this section we present the top-level ontology

concepts that refer to collaborations and services

(figure 2) that are to be provided by the ISU. The

Interoperability Service Utility (ISU) is the enabling

system of services for delivering basic

interoperability to enterprises, independent of

particular IT deployment. It may also denote and an

enterprise providing such services. A service is a

provider-client interaction that creates and captures

value (IBM). An ISU service is technical,

commoditized functionality, delivered as services

provided by an ISU to support the collaboration

between enterprises. A non-exhaustive list of ISU

services is presented below. Lower-level domain

ontologies further specify each one of the ISU

services.

DecisionMaking, ConsensusBuilding,

ConflictResolution services and other Group

Support Services. For example, reach decision on

production plans, budget expenditure, etc.

KnowledgeManagementServices helping a

company that wants to enter the VO, to efficiently

build up and manage a knowledge base of

collaboration-oriented internal knowledge, together

with knowledge sharing and exchange services

which guarantee adequate treatment of

confidentiality concerns.

Specific IntelligentServices such as

OpportunityDetection (e.g., detection of opportunity

to develop a new product) and RiskAssessment (e.g.,

risk of failure of the new product, risk of conflict

between partners).

CollaborationPatternServices as a means to use

and reuse proven, useful, experience-based ways of

doing and organizing communication and

collaboration activities in specific knowledge-

oriented collaborative tasks. A Collaboration Pattern

has Pre-Conditions, Post-Conditions, category

(CPatCategory), Application Area, and Triggers that

are comprised of Complex Events.

5 APPLICATION OF THE

OCEAN ONTOLOGY

In this section, we present the application of the

OCEAN ontology and architecture for network

enterprise collaboration in the pharmaceutical

industry. The pharmaceutical industry is considered

a typical example of knowledge-intensive sector

where the problem of dealing with heterogeneous

and vast number of information appears to be

insurmountable.

According to Investigational New Drug

Application Process (IND), the process of

developing a new dermatological drug involves

several different stages starting from pre-clinic

studies (testing the drug in the lab, use it on guinea

pigs etc.) and continuing with the four phases

imposed by Foods and Drug Administration (FDA)

and the European Medicines Agency (EMEA).

During these phases a formal proposal is introduced

to the FDA or EMEA with all the details of the new

drug. Upon approval, phase one starts with the

testing on a group of healthy people in order to

decide on the drug toxicity, liver and spleen

reaction, the best dose amount, the best way to

administer the new drug (oral, patch, intravenous,

intradermal). The next two phases involve the

testing on a group of sick people in order to decide

on the new dermatological drug effectiveness. Phase

two involves 100-300 sick people while phase three

involves the testing on an extensive group with

ethnographic differences that takes place in different

SEMANTIC INTEROPERABILITY OF E-SERVICES IN COLLABORATIVE NETWORKED ORGANIZATIONS

225



VirtualOrganisation

VOMember

CollaborationCapability

Project

CollaborationOpportunity

Product

achievesCommonGoal

exploitsCO*

undertakesProj*

deliversProduct*

hasParticipant*

hasCommonGoal*

hasTopology*

Topology

DevelopDermaDrugX

NewFDAGuidelines

CommonGoal

DermaDrugDevelopmentVO

MakeProfit DevelopDermaDrugsAccordingRegulationsAnd…

DermaDrugX

StarAlliance

ioioio

undertakesProj*

achievesCommonGoal

ioio

hasGoal*

ownsAsset=

Euro_500K

Researchers_10

Knowledge2

FarmaCompany2

participatesInVO- DermaDrugDevelopmentVO

ownsAsset=

Euro_3M

Researchers_5

Knowledge1

FarmaCompany1

participatesInVO- DermaDrugDevelopmentVO

ownsAsset=

Volunteers_150

Euro_80K

TestingKnowledge

Hospital2

participatesInVO- DermaDrugDevelopmentVO

…

ownsAsset=

Volunteers_100

Euro_100K

TestingKnowledge

Hospital1

participatesInVO- DermaDrugDevelopmentVO

…

CollabMethodsAndTools

DermaDrugsTestingCapability

DermaDrugsDevelopmentCapability

DermaCollaborationSmartSystem

usesCMT*

hasColCapability*

VirtualOrganisation

VOMember

CollaborationCapability

Project

CollaborationOpportunity

Product

achievesCommonGoal

exploitsCO*

undertakesProj*

deliversProduct*

hasParticipant*

hasCommonGoal*

hasTopology*

Topology

DevelopDermaDrugX

NewFDAGuidelines

CommonGoal

DermaDrugDevelopmentVO

MakeProfit DevelopDermaDrugsAccordingRegulationsAnd…

DermaDrugX

StarAlliance

ioioio

undertakesProj*

achievesCommonGoal

ioio

hasGoal*

ownsAsset=

Euro_500K

Researchers_10

Knowledge2

FarmaCompany2

participatesInVO- DermaDrugDevelopmentVO

ownsAsset=

Euro_500K

Researchers_10

Knowledge2

FarmaCompany2

participatesInVO- DermaDrugDevelopmentVO

ownsAsset=

Euro_3M

Researchers_5

Knowledge1

FarmaCompany1

participatesInVO- DermaDrugDevelopmentVO

ownsAsset=

Euro_3M

Researchers_5

Knowledge1

FarmaCompany1

participatesInVO- DermaDrugDevelopmentVO

ownsAsset=

Volunteers_150

Euro_80K

TestingKnowledge

Hospital2

participatesInVO- DermaDrugDevelopmentVO

…

ownsAsset=

Volunteers_150

Euro_80K

TestingKnowledge

Hospital2

participatesInVO- DermaDrugDevelopmentVO

…

ownsAsset=

Volunteers_100

Euro_100K

TestingKnowledge

Hospital1

participatesInVO- DermaDrugDevelopmentVO

…

ownsAsset=

Volunteers_100

Euro_100K

TestingKnowledge

Hospital1

participatesInVO- DermaDrugDevelopmentVO

…

CollabMethodsAndTools

DermaDrugsTestingCapability

DermaDrugsDevelopmentCapability

DermaCollaborationSmartSystem

usesCMT*

hasColCapability*

Figure 3: Breeding Environment related OCEAN concepts – Instantiated.

hospitals. Since only a 5% of new drugs are

approved to be circulated in the public, not many

efforts continue with Phase four where the approved

drugs continue to be tested for side effects for many

years after their first circulation.

In our case, we consider that the new

dermatological drug has reached the critical phase

three where the testing must proceed in different

hospitals. According to the ICH (International

Conference on Harmonization of Technical

Requirements for Registration of Pharmaceuticals

for Human Use) (ICH) that was held in Helsinki four

decades ago, there was an agreement upon a set of

good clinical practices. Of course these best

practices may be altered by the ethics committees of

each country involved that may decide on the details

of the drug testing (e.g. people with age less than

fourteen should not be tested) or by the release of a

new regulation from the FDA or EMEA. Such a

change on the clinical practices can be considered as

a new opportunity in terms of a VO.

As shown in figure 3, the OCEAN ontology has

been instantiated in order to describe our domain.

The VO follows a certain topology: Star Alliance.

This specific topology for structuring a VO involves

the grouping of independent organizations, with a

core organization taking the lead management role.

The VO comprises two pharmaceutical

companies with expertise in dermatological drug

development and two hospitals with their own assets

(testing knowledge, doctors supervising and

volunteers). The common goal for this VO has been

agreed to be the development of dermatological

drugs according to the regulations and ethics taking

into account the profit maximization. The VO has

been bred by a drug development virtual breeding

environment (VBE) that combines pharmaceutical

companies that are capable of developing any new

drug and hospitals for the testing processes.

5.1 Enabling shared Understanding

The ability of OCEAN to provide a common

terminological reference and a shared understanding

for human participating in VOs, is demonstrated by

the following set of questions for which we were

able to get answers from our instantiated ontology.

We have used the SPARQL language for assessing

the expressiveness capability of OCEAN. SPARQL

is a query language for the Semantic Web that can

be used to query an RDF Schema or OWL model in

order to filter out individuals with specific

characteristics (SPARQL).

Figure 4: Retrieval of VO members’ assets.

ICE-B 2010 - International Conference on e-Business

226

One such question could be: Which are the assets

of each VO member? In figure 4, it is shown how

we can make such a question using SPARQL.

Regarding our application, we get as an answer the

group of assets per VO members (Hospital 1,

Hospital 2, Pharma Company 1 & 2).

In table 3 the reader can find more questions that

can be answered using SPARQL queries through the

instantiated OCEAN top level ontology.

Table 3: SPARQL Queries.

QUERY SPARQL QUERY

In which VOs have a

specific

pharmaceutical

company participated

in the past?

SELECT ?VO

WHERE {

:PharmaCompany1 :particapatesInVO

?VO .

}

Which are the projects

that the

DermaDrugDevelpmen

tVO has undertaken so

far?

SELECT ?Proj

WHERE {

:DermaDrugDevelopmentVO

:undertakesProj ?Proj .

}

What are the possible

moderator services

depending on common

goals for the

DermaDrugDevelpmen

tVO?

SELECT DISTINCT ?ModSrv ?CGoal

WHERE {

:DermaDrugDevelopmentVO

:hasCommonGoal ?CGoal .

:DermaDrugDevelopmentVO

:usesModeratorService ?ModSrv

}

Unlike databases, ontologies built in OWL such

as OCEAN has a so-called open-world semantics in

which missing information is treated as unknown

rather than as false and OWL axioms behave like

inference rules rather than as database constraints.

For example, if we have asserted that BiotechOne is

a VO Member and that it Participates In (which is

the inverse property of hasParticipant) BioAlliance,

then, because only Virtual Organizations have VO

Members as participants, this leads to the

implication that BioAlliance is a Virtual

Organization. If we were to query the ontology for

instances of Virtual Organization, then BioAlliance

would be part of the answer. We can also ask if any

Collaborative Network Organization that has VO

Members as Participants is necessarily of Virtual

Organization. Query answering in OWL is

analogous to theorem proving; therefore the

OCEAN top level ontology plays itself an important

role and is actively considered at query time.

Considering both the schema and the data

represented in OCEAN can be very powerful,

making it possible to answer conceptual and

extensional, queries as well as to deal with

incomplete information.

6 CONCLUSIONS

In this paper we presented OCEAN, a top-level

ontology for collaborative networked organizations.

The OCEAN ontology covers the creation, operation

and termination phases of VOs and is designed as a

lightweight top-level ontology that provides a

common terminological reference for VO concepts

and relations. We validated the OCEAN ontology as

an expressive tool for describing such VOs using

SPARQL queries.

We believe that the OCEAN ontology formalizes

and enables network enterprise collaboration as it

models formally the main factors that affect/enable

the network enterprise collaboration orchestrated by

an entire system. It targets specifically the

relationships between “high level pieces” of domain

knowledge, explaining how they contribute

altogether to the network enterprise collaboration.

This top level ontology also enables better

communication by defining a common-agreed

vocabulary that: ensures shared meaning and

understanding regarding project goals; facilitates

knowledge acquisition in situations where teams

have to work together because the ontology becomes

a common, agreed-upon understanding of the terms,

which can be understood by team members with

different background knowledge (Valente et al.,

1996). Ultimately, the OCEAN ontology supports

semantic interoperability between software

components by formalizing the used vocabulary

explicitly in a machine-readable form. This is

possible due to the openness of the OCEAN top

level ontology which will act as “glue” between

other domain ontologies that describe specifics of

any VO, VO member, knowledge related

functionalities and assets. Although, we briefly

described here the application of the OCEAN

ontology in the pharmaceutical sector, we intend to

also use it in the manufacturing industry, in terms of

the SYNERGY ICT project for considering its

applicability and address possible limitations with

appropriate extensions of the top-level ontology.

ACKNOWLEDGEMENTS

This work has been partially funded by the European

Commission under project SYNERGY ICT No

63631. The authors would like to thank the project

team for comments and suggestions.

SEMANTIC INTEROPERABILITY OF E-SERVICES IN COLLABORATIVE NETWORKED ORGANIZATIONS

227

REFERENCES

B. Andersson, M. Bergholtz, A. Edirisuriya, T.

Ilayperuma, P. Johannesson, B. Grégoire, M. Schmitt,

E. Dubois, S. Abels, A. Hahn, J. Gordijn, H. Weigand,

B. Wangler, Towards a Reference Ontology for

Business Models, in: Proceedings of the 25th

International Conference on Conceptual Modeling

(ER2006), Tucson, Arizona, USA, 2006.

T. Berneers-Lee, Welcome to the Semantic Web, The

Economist, 21 (2007) 146. Business Management

Ontology, available online at:

http://www.bpiresearch.com/Resources/RE_OSSOnt/

re_ossont.htm

L. M. Camarinha-Matos, H. Afsarmanesh, Collaborative

networks: A new scientific discipline, Journal of

Intelligent Manufacturing 16(2005) 439–452.

S. Castano, A. Ferrara, S. Montanelli. Ontology-based

Interoperability Services for Semantic Collaboration in

Open Networked Systems. Springer, Interoperability

of Enterprise Software and Applications, ISBN: 978-

1-84628-151-8, 2006

C. M. Chituc, C. Toscano, A. Azevedo, Interoperability in

Collaborative Networks: Independent and industry-

specific initiatives – The case of the footwear industry,

Computers in Industry 59 (2008) 741–757.

P. Cimiano, J. VÄolker, Text2Onto A Framework for

Ontology Learning and Data-driven Change Discovery

in: Proceedings of the 10th International Conference

on Applications of Natural Language to Information

Systems, 2005.

M. Cristani, R. Cuel, A survey on ontology creation

methodologies, International Journal on Semantic

Web & Information Systems 1 (2) (2005) 49–69.

W. H. Davidow, M. S. Malone, The Virtual Corporation,

Harper-Collins (Eds.), New York, 1992.

DIP Business Data Ontology, available online at:

http://dip.semanticweb.org

ebXML, available online at: http://www.ebxml.org

EDI, UN/EDIFACT. available online at:

http://www.unece.org/trade/untdid

European Medicines Agency (EMEA), available online at:

http://www.emea.europa.eu/

C. Fellbaum, WordNet, An Electronic Lexical Database,

MIT Press, Cambridge, MA, 1998.

K. Fitch, S. J. Bernstein, M. S. Aguilar, B. Burnand, J. R.

LaCalle, P. Lazaro, M. van het Loo, J. McDonnell, J.

Vader, J. P. Kahan, The RAND/UCLA

Appropriateness Method User's Manual, 2001.

Foods and Drug Administration (FDA), available

online at: http://www.fda.gov/

A. Gangemi, N. Guarino, C. Masolo, A. Oltramari, L.

Schneider, Sweetening Ontologies with DOLCE, in:

A. Gomez-Perez and V. R. Benjamins (Eds.)

Knowledge Engineering and Knowledge

C. W. Holsapple, K. D. Joshi, Collaborative Approach in

Ontology Design, in: Proceedings of the

Communications of the ACM 45 (2002).

N. Huang, S. Diao, Ontology based enterprise knowledge

integration, Robotics and Computer Integrated

Manufacturing, 2007.

IBM Services Sciences, Management and Engineering,

available online at:

http://www.research.ibm.com/ssme/

ICH, International Conference on Harmonisation of

Technical Requirements for Registration of

Pharmaceuticals for Human Use, available online at:

http://www.ich.org

Investigational New Drug Application Process (IND),

available online at: http://www.fda.gov/cder/

Regulatory/applications/ind_page_1.htm

A. Kiryakov, Ontologies for Knowledge Management, in:

Davies, J., Studer, R., Warren, P. (Eds) Semantic Web

Technologies: Trends and Research in Ontology-

based Systems, Wiley, 2006.

H. K. Lin, J. A. Harding, A manufacturing system

engineering ontology model on the Semantic web for

inter-enterprise collaboration, Computers in Industry

58 (2007) 428–437.

A. Mowshowitz, Virtual Organization, in: Proceedings of

Communications of the ACM 40 (1997) 30–37.

I. Niles, A. Pease, Towards a standard upper ontology, in:

Proceedings of the International Conference on

Formal ontology in information Systems (FOIS '01,

ACM), 2-9, USA, 2001.

PELLET, OWL-DL ontology reasoner, available online at:

http://clarkparsia.com/pellet/

J. Plisson, P. Ljubic, I. Mozetic, N. Lavrac, An ontology

for virtual organisation breeding environments, IEEE

Transactions on Systems, Man, and Cybernetics – Part

C: Applications and Reviews, 37(6): 1327-1341, 2007.

Protégé, Ontology Development Platform, available online

at: http://protege.stanford.edu/

D. Rajpathak, M. Zdenek, R. Roy, A Generic Library of

Problem Solving Methods for Scheduling

Applications, IEEE Transactions on Knowledge Data

Engineering 18(6): 815-828, 2006.

RosetaNet, available online at: http://www.rosettanet.org

T. Ruokolainen, Y. Naudet, T. Latour. An Ontology of

Interoperability in Inter-Enterprise Communities.

Springer, Enterprise Interoperability II, ISBN: 978-1-

84628-857-9, 2007

E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, Y. Katz.

Pellet: A practical OWL-DL reasoner in: Proceedings

of the Web Semantics: Science, Services and Agents on

the World Wide Web 5(2): 51-53, 2007.

SPARQL, ontology query language, available online at:

http://www.w3.org/TR/rdf-sparql-query/

TOVE ontologies, available online at:

http://www.eil.utoronto.ca/tove/ontoTOC.html

M. Uschold, M. King, S. Moralee, Y. Zorgios, Y. The

enterprise ontology, Knowledge Engineering Review

13, 1998.

Y. Verginadis, D. Apostolou, N. Papageorgiou, G.

Mentzas. An Architecture for Collaboration Patterns in

Agile Event-Driven Environments. Fourth IEEE

Workshop on Agile Cooperative Process-Aware

Information Systems (ProGility 2009), June 29 - July

1, 2009, Groningen

A. Valente, J.A.P.J Breuker. Towards principled core

ontologies. SRDG Publications, University of Calgary,

1996, available online at: http://dare.uva.nl/

record/15931

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