ONTOLOGY-ORIENTED FRAMEWORK FOR VIRTUAL
ENTERPRISES
Accomplished within the Project: Future Network-based Semantic Technologies
(FUNSET-Science)
Gottfried Koppensteiner, Munir Merdan, Wilfried Lepuschitz
Automation and Control Institute, Vienna University of Technology,Gußhausstrasse 27-29, E376, 1040 Vienna, Austria
Erhard List, Lisa Vittori
Department of Information Technology, Vienna Institute of Technology (TGM), Vienna, Austria
Keywords: Virtual Enterprise, Ontology, Negotiation, Multi Agent System.
Abstract: In current networked organizations knowledge is distributed among the organization and their partners
resulting in the loss of transparency regarding the kind and the place of knowledge within the network. Our
approach is to use the semantic technology together with software agents in order to improve knowledge
capturing, reuse and transfer. Our paper describes an ontology-based multi-agent approach for the
knowledge exchange and process control with and within virtual enterprises. Different case-studies with
different ontologies are combined with a negotiation ontology, which is used as intercrossing, to support
semantic interoperability between heterogeneous inter- as well as cross-company levels.
1 INTRODUCTION
Current markets are operating in turbulent and
dynamic environments being influenced with
permanent requirements for higher quality and lower
price of the products and services. Such
circumstances as well as rapid technological
achievements force manufacturing companies to
emerge in a new way of organizational and
production paradigms, such as virtual enterprises
(Kulvatunyou et al. 2005). A virtual enterprise (VE)
is seen as an integrated network of regular
companies that join their core services and resources
in order to respond to unexpected business
opportunities collaborating on an ad hoc basis. Such
a network includes also suppliers, distributors,
retailers and consumers requiring from involved
companies to gather and share data and information
about markets, customers and internal competences
(Aerts et al. 2002). The potential benefits from
virtual organizations are: agility reflected through a
fast reaction on the unpredictable changes in the
environment, utilization of synergies between
companies that can improve business opportunities
and gain new markets, reaching a critical mass and
appearing in the market with a larger “visible” size,
improved competitiveness and resource optimization
as well as innovation potential (Camarinha-Matos,
2002). The capability of companies to form virtual
enterprises and cooperate with partners is an
important factor for keeping a competitive position
on the market.
Nevertheless, the presented advantages alone are
not enough to ensure the widespread adoption of the
VE concept, which is still missing. Especially
“Small & medium enterprises” (SME) are still
missing an adequate approach to co-operative
manufacturing (Ktenidis and Paraskevopoulos,
1999). A new approach for virtual enterprise
modeling as well as the fulfilment and consideration
of several research challenges, such as improved
knowledge exchanging and sharing, fast reaction to
customer demand, re-organization capability, and
integration of heterogeneous entities, are required
(Roche et al. 1998). The introduction of tools,
techniques and methodologies that will support
interoperability, information search and selection,
contract bidding and negotiation, process
management and monitoring, etc., is also highly
300
Koppensteiner G., Merdan M., Lepuschitz W., List E. and Vittori L. (2009).
ONTOLOGY-ORIENTED FRAMEWORK FOR VIRTUAL ENTERPRISES - Accomplished within the Project: Future Network-based Semantic
Technologies (FUNSET-Science).
In Proceedings of the International Conference on Knowledge Engineer ing and Ontology Development, pages 300-307
DOI: 10.5220/0002311103000307
Copyright
c
 SciTePress

required (Camarinha-Matos, 2002). In this context,
the information and knowledge exchange between
partners plays a critical role for the success of such
networks. This particularly due to the extreme
heterogeneity of the VE environment, in which it is
usually not transparent to the partners, which
knowledge is available at whose partner's site or
even if so, then in most cases the knowledge is not
understandable due to the usage of different formats
and tools. It is of biggest importance to have an
optimized information flow to find the appropriate
knowledge source in the desired quality within an
adequate time. The information search and
representation are seen as the two biggest challenges
for the information technology (Stuckenschmidt and
Harmelen, 2005).
Ontologies have been developed and investigated
for quite a while in artificial intelligence and natural
language processing to facilitate knowledge sharing
and reuse (Kulvatunyou et al. 2005). They are of
vital importance for enabling knowledge
interoperations between partners and, at the same
time, a fluent flow of different data from diverse
domains. Ontologies allow the explicit specification
of a domain of discourse, increasing the level of
specification of knowledge by incorporating
semantics into the data, and promote its exchange in
an explicitly understandable form (Silva and Rocha,
2003). Semantic means in this context that all
relevant concepts important for partners will be
modeled in an ontology by capturing the
associations between the domains ensuring at the
same time the understanding of exchanged
knowledge during the inter- as well as inside-
company communication. This allows business
partners to build open communities that define and
share the semantics of the information exchanged in
their domain.
Furthermore, the distributed nature of the VE
sets requirements related to the supervision,
coordination and execution of local (company
intern) goals as well as global (VE) goals. The
challenge is to introduce technologies that can
support understanding as well as automation, and
control processes connected with the creation,
operation, and dissolution of VEs (Marik and
McFarlane, 2005). Moreover, the companies are
internally confronted with permanent requirements
to optimize the workflow and improve effectiveness
as well as efficiency. The currently mostly applied
centralized control structures respond weakly to
frequently changing customer demands in terms of
performing necessary changes in the manufacturing
environment itself due to their rigid character and
limited adaptation capabilities (Parunak, 1996; Shen
and Norrie, 1999). Making the control of the system
decentralized, intelligent agents offer a convenient
way of modeling processes and systems that are
distributed over space and time, thereby reducing the
complexity, increasing flexibility and enhancing
fault tolerance (Jennings and Bussmann, 2003).
Our approach is to use semantic technology
together with software agents in order to improve
knowledge capturing, knowledge reuse and
knowledge transfer as well as to answer the
shortcomings mentioned above. The software agents
are used, on the one side within companies to
control certain components and processes (domains)
and on the other side to establish the link to other
partners within the VE. In this paper we use three
diverse SMEs as test cases, representing their basic
concept in ontologies and supporting their internal
control with related multi-agent architectures to
demonstrate a concept which offers the directions
towards solving the interoperability problems within
the VE. The suggested ontology-based
communication and coordination between the agents
enables also companies to improve and adjust their
internal processes (Merdan, 2009).
2 ONTOLOGY
Knowledge and information sharing within a
company (product design, process planning, and
scheduling, supply, intern transportation, inspection,
handling, etc.) as well as with other companies
(selling, cooperation, servicing, etc.) is from crucial
importance for the company’s survival. Their
representation needs to go beyond heterogeneous
formats to enable exchange across the intranets and
extranets as well as between various enterprise
applications.
Encoding the meanings separate from the data,
content and applications, and integrating them via a
shared ontology, semantic technology enables their
easier sharing and managing. An ontology is defined
as an explicit specification of conceptualization
(Gruber, 1993), with conceptualization meaning the
shared view of environment representation. The
ontologies and embedded semantics can be used to
formalize the knowledge representation and to
achieve overall “understanding”. Nevertheless, the
lack of common ontologies among the cooperating
organizations (Camarinha-Matos, 2002) is seen as a
serious limitation to tap the full potential of the VE
concept. Common ontologies allow an easier
integration of the underlined domain concepts, thus
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enabling the effective share of information between
heterogonous environments. Such ontologies can
specify and address the related concepts and classes
when two different ontologies have to be merged or
part of one mapped to other ones. However, due to
the complex and dynamic nature of the VE it is hard
to capture all related concepts in a persistent
ontology. It is much simpler to isolate the ontology
part that is only related to data exchange and
communication processes associated with it. In this
context it is necessary to define an ontology that will
detail the representation and semantics of data about
negotiation and present the link to all other concepts
in the company. This negotiation ontology will
include a description of basic company internal
concepts (order, user, product/service, interfaces,
etc.).
Figure 1: Negotiation Ontology.
Adhering to the reasons above this ontology
should be accepted and implemented by all partners
within the VE in the same form. We specified an
order as a major concept that links diverse partners
between each other as well as the VE with other
companies. The order, labeled with the
product/service type (though this is related to the
product/service design/description), deadline and
quantity, sets the key borders to the production
planning process impacting directly the resource
exploitation. This is the main reason why the
product/service order parameters should be
“understandable” and presented in the whole
production chain, from the order over production
until the final delivery.
As stated in the introduction, the VE concept is
supported by a related multi-agent architecture. In
our previous work we developed a knowledge-based
multi agent architecture applied in the assembly
domain (Merdan, 2009). Although implemented in
the assembly domain, due to its generic nature this
architecture can cover any other manufacturing
domains. In this architecture, an agent is defined as
an autonomous semantic entity that has specific
tasks and knowledge about its domain of
application, about strategies that can be used to
achieve a specific goal, and about (other) relevant
agents involved in the system. This architecture
consists of four major agent classes. The Contact
Agent (CA) has responsibilities that encompass
organizational and system supervisory functions.
The Order Agent is responsible for the
accomplishment of one order, related process
planning respecting due dates and the like, and
handling customer requests for modifying or
cancelling their orders. The Supply Agent is in
charge of coordinating the production execution in
order to achieve the best possible production results,
including on-time delivery, cost minimization, etc.
The Machine Agents represent manufacturing
resources (typically a machine) providing particular
processes and services. In this paper we extended
this architecture by adding the NegotiationAgent and
by assigning also the negotiation administration role
to the CA. In the next sections, we will present
defined ontologies for three diverse manufacturing
domains as well as the correlation of these
ontologies to the associated negotiation ontology.
Our architecture is based on agents that have a rule-
based behavior. Rules are considered as if-then
statements applied to the agent’s knowledge base.
3 USE CASES
In order to present our concept, we selected four
different companies with related products and
services. The major aspect here is that associated
ontologies cover different concepts and workflows.
Moreover, while such companies can be placed
anywhere, the possibility exists that they use
different words for the description of the same
concepts or vice versa.
Figure 2: VE Concept.
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Following Use-Cases were separately analysed and
developed: from four project groups:
- KASA-Ontology: To represent a company for
agent based assembly automation,
- MASPE-Ontology: An agent based batch
processing factory for liquids,
- WareLoXX-Ontology: A warehouse system for
the commitment of orders,
- KABA-Ontology: A bottling plant for the filling
of bottles combined with LiStoSys-Ontology.
However, the usage of the negotiation ontology that
links them enables the determination of an
equivalent or the semantically closest concepts.
Specified companies need to cooperate and negotiate
with each other to be able to place products on the
market. The negotiation ontology is used to ensure
the overall understanding during communication and
to enable the mapping of external information and
knowledge into an internal company representation.
3.1 KASA-Ontology
In the KASA ontology (Merdan, 2009), a company
from the assembly domain is used to offer particular
product. In related ontology Product Order defines
the type of the requested product, its quantity, design
e.g. color, etc. The ontology based product model is
used to extract the production/assembly operations
from the product design and link particular Steps,
which have to be performed for the
production/assembly of a product, to particular
resources (Figure 3).
Figure 3: Assembly Ontology.
A Product is presented as a hierarchy of
subassemblies and parts together with all their
properties and relationship between them. Parts are
defined as components, described by a set of
attributes, properties, constraints and relations to
other parts. The relationship between parts within a
subassembly defines operations that have to be done
to connect these parts and represents how these
subassemblies should be put together to complete
the product. A Resource is a physical component
able to perform a certain Operation.
3.2 MASPE-Ontology
Concerning process automation, in particular batch
automation, modifiable recipes provide a certain
grade of flexibility at least from a process-oriented
kind of view. However, the underlying control
systems are still based on centralized structures that
impede easy modifications of the system. This
affects intended modifications, such as an extension
of the system functionality by further components
due to changing market demands, as well as
unintended modifications in the case of occurred
failures. Multipurpose facilities that provide
reconfigureability of software and hardware
components are therefore required (Kuikka, 1999;
Sünder et al. 2006). Agent-technology brings certain
advantages to the domain of batch automation as it
provides means for the dynamic allocation of
resources (such as reactor tanks), path optimization
(as for instance pipes need to be cleaned before
another product may be transferred) and material
tracking.
Figure 4: Batch Ontology.
MASPE (Multi Agent Systems for process
engineering) represents an approach to integrate
agent-technology into a batch control system. The
FESTO compact unit, a laboratory process plant,
acts as the first target system for this approach. The
MASPE ontology (Figure 4) comprises the
environment of this process plant, with all essential
information, which are needed by the plant to work
on its own.
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The ontology incorporates a set of classes that
describe the process of manufacturing a product.
The concepts of certain classes, such as the concept
of a batch or a recipe, are derived from the relevant
standard IEC 61512 Batch Control – Part 1: Models
and terminology (IEC, 1997). The class Product
serves as a unique naming class for a product (i.e.
for instance a specific amount of a pharmaceutical
product) by using an ID and refers to the class
Recipe, which contains all required material
resources (such as raw material) and operations to
manufacture this product step by step. One recipe
can only describe one certain product and backwards
– one product can only be described by one recipe.
The actual execution of a recipe delivers one batch
of a type of product. Hence, the class Batch refers to
one product and one recipe. Recipes refer to one or
several Operation classes and require one or several
types of material to be executed. Operations (e.g.
heating up material to a specific temperature to
generate a reaction) are performed during the
execution of a recipe on a batch and require at least
one type of material as well as at least one type of
hardware (e.g. a heater of a reactor tank). Hence, the
class Operation refers to one or several Material as
well as Hardware classes.
3.3 WareLoXX-Ontology
The efficiency and effectiveness in any distribution
network is significantly influenced by the operation
of the nodes in such a network, i.e. the warehouses
(Rouwenhorst et al., 2000). Warehousing involves
all movements of goods within warehouses namely:
receiving, storage, order-picking, accumulation, as
well as sorting and shipping (Van den Berg, 1999).
In opposition to conventional warehouse
systems, our concept combines software agent
technology and an ontology-based model to map a
warehouse system and support automated
warehousing. Therefore the following simplified
ontology-concept was created (Figure 5):
First there an Order is issued which requests one
or more Crates. The crates have to be requested in
the reverse delivery order. A crate consists of a
Ware. The crates are stored in a Stock, which is
separated in multiple storage positions. Every
storage position has an x-coordinate and a y-
coordinate. Every storage position hosts multiple
segments, which can contain only one crate. Every
crate has a unique segment-coordinate. The crates
are moved by a ConveyorSystem to the
PalletMachine. When there are no more requests for
crates, the crates are placed on a Pallet. If a pallet is
full and more crates are required for one order,
another pallet is provided. The pallets must arrive at
the Goodissue (the place where they are loaded on a
truck) in the right delivery order.
Independently from this the system ontologies
offer services to external project partners like the
storage of goods, the transport from the supplier to
the customer and therefore the warehousing of
diverse goods within the logistic chain.
Figure 5: WareLoXX Ontology.
3.4 KABA-Ontology
The KABA ontology (Figure 6) is meant to support
the information and knowledge exchange in a bottle
filling plant.
Such company has to manage different kinds of
bottles, crates, liquids, machines, conveyors and
their disturbances. In our previous work we
presented an ontology-based approach that improves
flexibility and enhance fault tolerance of the
transportation system (Merdan et al., 2008;
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Koppensteiner et al., 2008), which is seen as
backbone of such a plant.
The class Job is major class in the KABA-
Ontology and is aimed to summarize all operations
done within one particular Order. The class
Operations contains subclasses which represent all
available machine functions. The Resources class
has two subclasses named Equipment and Package.
Equipment is represented as entity able to perform a
certain operation. Conveyors are used as buffers,
which capacity is defined with their dimension and
speed. On another side, the class Package has a
Pallet, Crate and Bottle subclasses, which are means
to encapsulate particular item within a order.
Figure 6: KABA Ontology.
4 NEGOTIATION ONTOLOGY
Negotiation can be understood as the process of
reaching agreement on one or more matters of
common interest. Traditional negotiation approaches
have several constraints on the type of interactions
(only pre-determined protocols are allowed or agents
identified) and have protocols, which are coded
implicitly within agents and hard to modify (Tamma
et al, 2005). The negotiation ontology (Figure 7) acts
as a general framework that defines the basic
terminology, interaction and protocols enabling
agent to reach agreement (Tamma et al, 2002). The
common purpose of our ontology is a support of
different negotiation types with multiple users at the
same time in a VE. Besides the ability to support
different auction types, auction properties and
negotiation tactics, it can also handle different
products/services, their properties and users. Our
ontology has its roots in (Vetter, 2006).
It enables that every user has the possibility to
start its own negotiation with an individual
configurable Negotiation Agent (NA) which can
then handle multiple negotiations. Firstly, the user
specifies a Good that he wants to get or offer and
over the Contact Agent (CA), if any kind of
negotiation is required, creates the NA loading to it
Auctioneer behaviors. This Auctioneer will lead the
auction itself.
Figure 7: Negotiation Ontology.
Every new user who joins the auction has a
configured MarketAgent that has participant role and
related behaviors. These MarketAgents are sending
offers to the Auctioneer. The Auctioneer takes these
bids and compares them. Then it sends messages
back to all MarketAgents. These messages contain
information about the state of the auction (highest
bid). Now the MarketAgents know if they want
place another bid or to left the auction. When the
time is up (or a maximum of negotiation steps is
reached), the Auctioneer takes the best bid and
creates a Trade object. This Trade object contains all
information about the seller, the buyer and the
auction itself. In the case that requested service or
information doesn’t require auction, the CA map
these, using the related parts of negotiation ontology
concept, in internal company ontology on its own
and starts appropriate behaviours.
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5 SYSTEM IMPLEMENTATION
As mentioned in the previous sections, the concept is
based on distributed multi agent architecture, which
is currently implemented at Automation and Control
Institute. As Framework for the agent
implementation JADE (JADE, 2009) is used. The
Java Agent Development Environment provides
Platforms for each Company and the ACL-Message
System to exchange Messages according to the
FIPA-Standard (FIPA, 2009). To connect the
different platform, the already implemented and
tested DF-Federation for Ontology-based resource
allocation (Koppensteiner, 2008b) is used. To
provide the JADE-agents with an ontology the
system architecture is based on (Merdan, 2009). The
different company ontologies, to handle the internal
representation of company products and tasks, where
therefore modelled in the ontology design tool
Protégé (Protégé, 2009). All these agents within the
VE are also equipped with an negotiation ontology
to share a common understanding.
JADE -
Framework
Negotiation -
Ont ol ogy
System-Ontology
JADE -
Framework
Negotiation -
Ontology
System-Ontology
1
2
3 4
5
6
ACL-Message
Sender
Recei ver
Conversation-ID:
XML about Auction
Content of ACL-Message:
XML about HandledGood
Descri pti on
ID
Figure 8: System Implementation.
As the Figure 8 shows, the proposed systems could
be divided into three layers. On the bottom is the
company’s system ontology. The next layer
combines this system ontology with the proposed
negotiation ontology and the final layer is the JADE-
Framework itself. To show the idea behind this
architecture, the example of a message exchange is
explained. It doesn’t have any relevance, whether
the message is to start an auction, place a bid or
request information, the procedure is each time the
same.
(1) If an agent has something to communicate, it
derives a description of its demand from its own
system ontology and generates an XML
representation of it. This XML is stored later in
the content field of the ACL-Message.
(2) Related to the type of the communication act the
conversation-id of the ACL-Message is created
from the negotiation ontology and formatted
also in XML. In case of an auction the whole
auction - information is captured in this
representation.
(3) Afterwards, the message is equipped with the
sender information, the receivers address and all
other necessary information according to the
FIPA standard. In case that the agent doesn’t
have any information about possible agents
which offer particular service the DF-Agent of
the JADE-Platform is used to find all reliable
agents within the DF-Federation
(4) The receiver gets the message over the JADE-
runtime and starts its behavior that maps the
message to its ontology.
(5) After an agent has mapped the message, it
checks the conversation-id to assign the
message to the right context of its negotiation
behavior.
(6)
Finally, it extracts the information given in the
content field of the message and stores it in his
knowledge base. Consequently, it acts based on
the new circumstances, e.g. place a bid or
request more information.
6 CONCLUSIONS
The virtual enterprises paradigm is seen as
promising approach that can help companies to face
the current dynamic market trends and conditions.
However, the limited knowledge and information
exchange between involved partners is significant
drawback that prevents their wider establishment.
We present the ontology-based concept combined
with multi-agent approach that enables easier flow
of information and knowledge. For the development
and implementation of the concept we used four
uses cases that are connected through overlapping
(negotiation) ontology. We proved the feasibility of
the presented approach. Having multi-agent
architecture as a basis of our approach, our future
work will be concerned with further development
and tuning of defined agent behaviors. Furthermore,
another part of our research is going to focus on
ontology merging and mapping, which is
complementary to our concept.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the financial
support by the Sparkling Science program, an
initiative of the Austrian Federal Ministry of Science
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and Research. We also want to thank all students
involved in the FUNSET Science Project at Vienna
Institute of Technology (TGM), department for
Information-Technology – especially the students
Reinhard Grabler (WareLoXX), Michael Peitl
(KABA), Simon Strobl (Negotiation), Nguyen Than
Tung (LiStoSys) and Mirza Zenkic (MASPE) for
their contribution in ontologies development.
REFERENCES
Aerts A.T.M., Szirbik N.B., and Goossenaerts J.B.M., “A
flexible, agent-based ICT architecture for virtual
enterprises,” Computers in Industry, vol. 49, Dec.
2002, pp. 311-327.
Camarinha-Matos L.M., “Virtual organizations in
manufacturing: trends and challenges,” Proceedings of
the 12th International Conference on Flexible
Automation and Intelligent Manufacturing, Dresden,
Germany, 2002.
FIPA, Foundation for intelligent physical agents,
Communicative Act Library Spec., http://
www.fipa.org/specs/fipa00037 and ACL Message
structure Specification, http://www.fipa.org/specs/
fipa00061, accessed Mai 2009
Gruber T.R., “A translation Approach to Portable
Ontology specifications,” Knowl. Acquis., vol. 5,
1993, pp. 199-220.
IEC TC65, IEC 61512-1: Batch Control – Part 1: Models
and terminology, International Electrotechnical
Comission (IEC), 1997.
JADE – Java Agent Development Framework, http://
jade.tilab.com/, accesed April 2009
Jennings N. and Bussmann S., “Agent-based control
systems: Why are they suited to engineering complex
systems?” Control Systems Magazine, IEEE, vol. 23,
2003, pp. 61-73.
Koppensteiner G., Merdan M., Hegny I., and
Weidenhausen G., “A change-direction-algorithm for
distributed multi-agent transport systems,” Mecha-
tronics and Automation, 2008. ICMA 2008. IEEE
International Conference on, 2008, pp. 1030-1034.
Koppensteiner G., Merdan M., Zoitl A., and Favre-Bulle
B., “Ontology-based resource allocation in distributed
systems using director facilitator agents,” Industrial
Electronics, 2008. ISIE 2008. IEEE International
Symposium on, 2008b, pp. 1721-1726.
Ktenidis P. and Paraskevopoulos A., “Information and
communication technology (ICT) infrastructure for
small and medium enterprises (SMEs) networks:
achievements and work-in-progress from three
European projects,” Research Issues on Data
Engineering: Information Technology for Virtual
Enterprises, 1999. RIDE-VE '99. Proceedings., Ninth
International Workshop on, 1999, pp. 44-51.
Kuikka S., A batch process management framework:
Domain-specific, design pattern and software
component based approach, PhD thesis. Helsinki
University of Technology, 1999.
Kulvatunyou B., Cho H., and Son Y.J., “A semantic web
service framework to support intelligent distributed
manufacturing,” Int. J. Know.-Based Intell. Eng. Syst.,
vol. 9, 2005, pp. 107-127.
Marik V. and McFarlane D., “Industrial Adoption of
Agent-Based Technologies,” IEEE Intelligent Systems,
vol. 20, 2005, pp. 27-35.
Merdan M., “Knowledge-based Multi-Agent Architecture
Applied in the Assembly Domain,” PhD Thesis,
Vienna University of Technology, 2009.
Merdan M., Koppensteiner G., Hegny I., and Favre-Bulle
B., “Application of an ontology in a transport
domain,” IEEE International Conference on Industrial
Technology, 2008. ICIT 2008, 2008, pp. 1-6.
Parunak H.V.D., “Applications of distributed artificial
intelligence in industry,”
Foundations of distributed
artificial intelligence, John Wiley \& Sons, Inc.,
1996, pp. 139-164.
Protégé, Stanford Medical Informatics, Stanford
University, http://protege.stanford.com, accesed Mai
2009
Roche C., Fitouri S., Glardon R., and Pouly M., “The
potential of multi-agent systems in virtual
manufacturing enterprises,” Database and Expert
Systems Applications, 1998. Proceedings. Ninth
International Workshop on, 1998, pp. 913-918.
Rouwenhorst B., Reuter B., Stockrahm V., van Houtum
G.J., Mantel R.J., and Zijm W.H.M., “Warehouse
design and control: Framework and literature review,”
European Journal of Operational Research, vol. 122,
May. 2000, pp. 515-533.
Shen W. and Norrie D. H., “Agent-based Systems for
Intelligent Manufacturing : A State of-the-Art
Survey,” Knowledge and Information Systems, an
International Journal, vol. 1(2), 1999, pp. 129-156.
Silva N. and Rocha J., “Ontology mapping for
interoperability in semantic web,” Proceedings of the
IADIS International Conference WWW/Internet, 2003.
Stuckenschmidt H. and Van Harmelen F., Information
sharing on the semantic web, Springer, 2005.
Sünder C., Zoitl A., and Dutzler C., “Functional structure-
based modelling of automation systems,” in Int. J.
Manufacturing Research, vol. 1, iss. 4, pp. 405-420,
2006.
Tamma V., Phelps S., Dickinson I., and Wooldridge M.,
“Ontologies for supporting negotiation in e-
commerce,” Engineering Applications of Artificial
Intelligence, vol. 18, Mar. 2005, pp. 223-236.
Tamma V., Wooldridge M., and Dickinson I., “An
ontology for automated negotiation,” Proceedings of
the Workshop on Ontologies in Agent Systems, 2002.
Van den Berg J.P., “A literature survey on planning and
control of warehousing systems,” IIE Transactions,
vol. 31, 1999, pp. 751-762.
Vetter M., “Ein Multiagentensystem zur
Verhandlungsautomatisierung in elektronischen
Märkten,” PhD Thesis, Stuttgart University , 2006.
ONTOLOGY-ORIENTED FRAMEWORK FOR VIRTUAL ENTERPRISES - Accomplished within the Project: Future
Network-based Semantic Technologies (FUNSET-Science)
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