B2B TRANSACTIONS ENHANCED WITH

ONTOLOGY-BASED SERVICES

Andreia Malucelli

LIACC-NIAD&R, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias,4200-465 Porto, Portugal

PUCPR – Pontifical Catholic University of Paraná, R. Imaculada Conceição,1155, 80215-901- Curitiba-PR, Brazil

Ana Paula Rocha, Eugénio Oliveira

LIACC-NIAD&R, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias,4200-465 - Porto, Portugal

Keywords: Ontologies, e-Commerce and e-business: B2B and B2C, Virtual Enterprises

Abstract: In an efficient Virtual Enterprise (VE), where all the partners, both sending and receiving messages have to

lead to acceptable and meaningful agreements, it is necessary to have common standards (an interaction

protocol to achieve deals, a language for describing the messages’ content and ontology for describing the

domain’s knowledge). This paper introduces first the ForEV platform, implemented through a Multi-Agent

System. This platform facilitates partners’ selection automatic process in the context of VE and includes a

negotiation protocol through multi-criteria and distributed constraint formalisms, as well as a reinforcement

learning algorithm. Then, Ontology-based Services are proposed to be integrated in ForEV architecture in

order to help in the VE formation process. These services will make the platform more open, enabling the

establishment of the negotiation process between agents with different ontologies although representing the

same domain of knowledge. An Ontology-based Services Agent is the responsible for providing the

Ontology-based Services and monitoring the whole agents interaction just in time, without needing of a

previous and tedious complete ontology mapping process. In our architecture each agent (either market or

enterprise) has its own architecture and functionalities (some developer will design and build the ontology

with some tool and, later, the agent will access the generated file/database), which implies the heterogeneity

of the all Multi-Agent System.

1 INTRODUCTION

ForEV is an agent-based platform we have

developed aiming to facilitate the partners’ selection

automatic process in the context of Virtual

Enterprise (VE). ForEV provides tools for agents, as

enterprises’ delegates, to engage themselves in

complex negotiations including multi-attribute

evaluation and qualitative appreciation of proposals.

Both the former and latter features are important in a

B2B negotiation.

In business transactions, due to the nature of the

goods/services traded, these goods/services are

described through multiple attributes (e.g. price and

quality), which imply that negotiation process and

final agreements between seller and supplier must be

enhanced with the capability to both understand the

terms and conditions of the transaction (e.g.

vocabularies semantics, currencies to denote

different prices, different units to represent measures

or mutual dependencies of products).

A critical factor for the efficiency of the future

negotiation processes and the success of the

potential settlements is an agreement among the

negotiating parties about how the issues of a

negotiation are represented in the negotiation and

what this representation means to each of the

negotiating parties. This problem is referred to as the

ontology problem of electronic negotiations

(Ströbel, 2001). Distributors, manufactures, and

services providers may have a radically different

Malucelli A., Paula Rocha A. and Oliveira E. (2004).

B2B TRANSACTIONS ENHANCED WITH ONTOLOGY-BASED SERVICES.

In Proceedings of the First International Conference on E-Business and Telecommunication Networks, pages 10-17

DOI: 10.5220/0001391300100017

 SciTePress

ontology that differs significantly in format,

structure, and meaning.

Given the increasingly complex requirements of

applications, the need for rich, consistent and

reusable semantics, the growth of semantically

interoperable enterprises into knowledge-based

communities, and the evolution and adoption of

semantic web technologies, ontologies represent the

best answer to the demand for intelligent systems

that operate closer to the human conceptual level

(Obrst et al., 2003).

A specific Ontology-based Services Agent is

proposed to help in the VE formation process,

providing useful advices on how to better negotiate

specific items and how different terms may be

understood as equivalent. It is up to this Ontology-

based Services Agent to make it possible

negotiations in the Multi-Agent System and finally

lead to acceptable and meaningful agreements.

The remainder of this paper discusses first the

ForEV platform, including the architecture as well

as explains the Q-Negotiation algorithm and the

distributed constraint satisfaction of parallel

negotiations. Some ontology approaches are

presented in Section 3. This Section discusses also

about Ontologies and Business Transactions and

about the interoperability problems. The Ontology-

based Services proposed as well as the needs change

in ForEV to integrate the Ontology-based Services

Agent, are presented in Section 4. The technologies

used for implementation are discussed in Section 5

and the conclusions are discussed in the last Section.

2 FOREV PLATFORM

ForEV is an appropriate computing platform, which

includes and combines negotiation’s methods in the

context of Multi-Agent Systems, which is suitable

for Virtual Enterprise formation scenario.

The negotiation’s methods try to satisfy the

electronic market dynamics and competitiveness

requirements. Maintaining enterprise utility related

information private is here enforced without

endangering its own negotiation power in that

market. An entity participating in a business

transaction, and an enterprise in particular, tries to

hide from the market its own private evaluation of

the goods under negotiation.

On the other hand, adaptation should also be

another important characteristic to be included in

any entity present in an electronic market. In fact,

although an enterprise doing business does not

know, in advance, its own potential partners, it has,

nevertheless, to pay attention to the eventual market

changes and to adapt as soon as possible to those

changes.

Therefore, the privacy and learning

characteristics have been included in the heart of the

proposed negotiation process – the “Q-Negotiation”

algorithm. “Q-Negotiation” is an iterative, adaptive,

multi-attribute negotiation algorithm using

qualitative argumentation.

Moreover, in order to solve the simultaneous

partial inter-dependent negotiations dependency

problem, arising during the Virtual Enterprise

formation phase, a specific algorithm has been

developed. This algorithm is a decentralized

distributed dependency satisfaction problem solver,

which is based on the progressive utility decrement

concept. Through the application of this algorithm,

the Multi-Agent System is able to, in a non-

centralised way, select that solution which

minimizes the total agents’ utility decrement value

for

those agents involved in that specific dependency.

2.1 ForEV Architecture

ForEV was developed to model the interactions

between different entities, named enterprises and

customers, in order to select among a set of

individual enterprises, the subset that may satisfy a

specific customer need. The set of selected

enterprises will form a temporary consortium named

Virtual Enterprise, which will exist during the time

needed to satisfy the customer requirements.

The use of a Multi-Agent System methodology

seems to be an adequate paradigm for the system

architecture, since enterprises are independent and

have individual objectives and behaviours.

Agents represent the enterprises (Enterprise

Agent) and customers (Market Agent) in the system.

The Enterprise Agents (EA) and Market Agent

(MA) meet each other in a marketplace where they

cooperate with the objective of providing or buying

some good (product/service), keeping their own

preferences and goals. The goods under transaction

are described by an ontology (Ont) that should be

known and understood by all the participants. The

system contains another agent, the Register Agent

(RA), which is responsible for creating and

maintaining the domain ontology.

The Multi-Agent System is then represented by

the n-tuple: ForEV=<RA, {MA}, {EA}, Ont>. A

brief description of the agents presented in the

system is given in next paragraphs.

market creation and maintenance. The Enterprise

and Market Agents announce their competencies and

needs, respectively, in the market. The Register

B2B TRANSACTIONS ENHANCED WITH ONTOLOGY-BASED SERVICES

Agent establishes the contact between the Market

and the Enterprise Agents that may have related

interests. Actually, the RA is also responsible for

creating and presenting the domain ontology to all

participants.

Figure 1 presents the RA role while establishing

contact between relevant Enterprise and Market

Agents. The RA receives a message from MA

informing that it needs the good “X” (message 1).

The RA also receives a message from EA

informing

the possibility of providing components “X

” and

“Y

” (message 2), where X

is one of the X’s

components, and Y

is one of the Y’s components.

The RA presents MA

to EA

, because MA

interested in a competency that EA

may offer

(message 3). The EA

sends then a message directly

to MA

offering “X

” (message 4).

Another task assigned to RA is to maintain and

manage the domain ontology. For the moment, this

task only includes the syntactic representation of the

domain concepts, which are the customer’ needs

(product or service) and the enterprises’

competencies (components). The RA responsibility

in this field should be enhanced with Ontology-

based Services, which allow that different agents

may use their private ontology instead of being

obliged to use the RA pre-defined ontology. A

solution to this issue is proposed in Section 4.

Market Agent (MA) represents the customer.

This agent announces in the market the need for a

specific good. The MA

is responsible for selecting

among a set of EA

s, the subset that will form the

Virtual Enterprise. The MA need (good) is described

by a list of components (Cmpt), and each component

is described by a list of attributes (Atb). The MA’s

preferences are codified in the relative order by

which attribute are defined in the component

structure definition: Cmpt

={Atb

, Atb

}

with Atb

imp

Atb

imp

Atb

, where

imp

means

“more important than”. In this case it means that

Atb

is more important than

Atb

, and Atb

more important than Atb

. This order is relevant for

the negotiation process. The attributes’ values are

also represented in a preference order.

Val_Atb={Value

, Value

, …Value

} with Value

imp

Value

imp

…>

imp

Value

The selection of the individual enterprises (EAs),

which will compose the VE, starts with the messages

sent by EAs

to MA

offering components (i.e.,

message 4 in Figure 1). The MA

negotiates with

several EAs to select among them the ones that are

the most promising, at the moment, to satisfy its

actual need. This negotiation results in process of

multiple rounds (Oliveira and Rocha 2000) where

the MA

plays the role of coordinator. The MA

proposals’ evaluation is done by comparing between

them all the proposals received from the several

EAs.

Enterprise Agent (EA) represents a specific

enterprise. Each EA sends an announcement

message describing its competencies in the market

according to the established ontology (i.e., message

2 in Figure 1). A particular EA

will compete with

other EAs that have similar competencies to be a

partner of the VE. As explained before, the EA’

competencies (components) are identified by a list of

attributes, that includes the EA own preferences and

constraints related to those attributes. The structure

used to identify an attribute Atb

is composed of the

attribute’s values ordered by preference (Val_Atb

)

and also the dependencies associated with the

different attribute’s values (Dep

). These

dependencies may be of three types: time, event and

value. Dep

={{Dept

}, {Depe

}, {Depv

}}, where

time dependencies constrains the attribute’s values

in specified time intervals, event dependencies

constrains the attribute’s values when some

specified event happen, and the value dependencies

constrains the attribute’s values to other attributes’

values of other components. During the negotiation

process that leads to the VE formation, the EA

formulates proposals in an adaptive way, learning

with qualitative feedbacks formulated by the MA

negotiation rounds.

2.2 Q-Negotiation and Distributed

Constraint Satisfaction Algorithm

In the VE formation process, the selection of the

enterprise partners, which will integrate the VE, is

done through a negotiation algorithm called Q-

Negotiation. The Q-Negotiation algorithm uses a

reinforcement learning strategy based in Q-learning

(Rocha and Oliveira, 2001) for the formulation of

new proposals.

The use of a reinforcement learning algorithm

seems to be appropriate in this specific scenario,

since enterprise agents evolve in an, at least,

partially unknown environment. In particular, Q-

learning enables on-line learning, which is an

Figure 1: RA establishing contact

ICETE 2004 - GLOBAL COMMUNICATION INFORMATION SYSTEMS AND SERVICES

important capability for B2B negotiations where

agents may effectively learn in a continuous way

during all the negotiation process, with information

extracted from each negotiation round, and not only

in the end of the negotiation. The adaptation of the

Q-learning algorithm to our specific scenario, that is,

the VE formation, leads to the inclusion of two

important features (the reward value calculation and

the decrement of the exploration space) detailed in

(Rocha and Oliveira, 2001).

One of the requirements for the negotiation

protocol used in ForEV, besides dealing with

attributes intra-dependencies, is the capability to

deal with attribute’s inter-dependencies. This is an

important requirement to be considered in our

scenario, because in the VE formation process

interdependent negotiations take place

simultaneously, and proposals received from

different enterprise agents may have incompatible

dependencies.

Our distributed dependencies satisfaction

algorithm, besides reaching the optimal solution,

keeps agent’s information as much as possible

private. Each agent involved in the distributed

dependent problem resolution should know all

possible values for its own dependent attributes.

Agents will then exchange among them alternative

values for the dependent attributes, in order to

approach an agreement. As in any iterative

negotiation process, agents start the negotiation by

proposing its optimal solution and, in the next

rounds start trying to reach a consensus. A more

detailed description of this algorithm may be found

in (Rocha and Oliveira, 2001).

Since enterprises may be formally unknown to

each other and represented by means of

heterogeneous agents, ForEV have been enhanced

with new functionalities that make those

heterogeneous communicating agents to understand

each other no matter the differences in their own

ontologies.

3 ONTOLOGIES

Ontologies are a popular research topic in various

communities such as knowledge engineering, natural

language processing, cooperative information

systems, intelligent information integration, and

knowledge management. The reason for ontologies

being so popular is in large part due to what they

promise: a shared and common understanding of

some domain that can be communicated across

people and computers (Duineveld et al., 1999).

Agents may use different ontologies to represent

their view of a domain. Each domain may be

specified in many different ways and this ontology

mismatch is a question under intensive research.

(Wache et al., 2001) presents three different

directions on how to employ the ontologies: (i)

single ontology approach, (ii) multiple ontology

approach, (iii) hybrid ontology approach. Figures 2,

3 and 4 illustrate the three architectures derived from

these approaches:

Single ontology approach: uses a global

ontology providing a shared vocabulary for the

specification of the domain semantics. All

information sources are related to a global ontology.

The global ontology may also be a combination of

several specialized ontologies.

Multiple ontology approach: each information

source is described by its own ontology. In principle,

the “source ontology” may be a combination of

several other ontologies but it may not be assumed

that the different “source ontologies” share the same

vocabulary.

Hybrid ontology vocabulary: similar to

multiple ontology approach the semantics of each

source is described by its own ontology. But in order

to make the source ontologies comparable to each

other they are built upon one global shared

vocabulary. The shared vocabulary contains basic

terms of the domain.

Local Ontology

Figure 3: Multiple ontology approach

Global Ontology

Figure 2: Single ontology approach

Local Ontology

Shared Vocabulary

Figure 4: Hybrid ontology approach

Local Ontology

B2B TRANSACTIONS ENHANCED WITH ONTOLOGY-BASED SERVICES

In this work, we are using the multiple ontology

approach where each agent playing a specific role,

explores its own ontology. It is a decentralized and

distributed approach according to our Multi-Agent

System architecture (Malucelli and Oliveira, 2003).

3.1 Ontologies and Business

Transactions

Ontologies provide support in integrating

heterogeneous and distributed information sources.

E-commerce is currently facing revolutionary

changes: Marketplaces are enabling new kinds of

services interactions between suppliers and buyers

(

Fensel, 2001). However, how can one ensure that

suppliers and buyers have the same understanding

regarding the issues that are subject to the

negotiation?

Ontology is required to ensure that agents are

negotiating about the very same item/good/service.

Products heterogeneity is a critical impediment to

efficient business information exchange. Specifying

a simple product like a compact disc is relatively

easy and there is a chance of always finding

similarities in the description, but specifying a more

complex product like a car or a plane may be really

tough work.

In e-commerce activities involving interactions

among different sellers (B2B model) or between one

buyer and multiple sellers (consumer-to-business

model), a common ontology is crucial (

Ng and Lim,

2001). It is not sure that all the agents will use a

common ontology, usually, in a decentralized and

distributed approach, each agent has its specific,

private ontology and it may not fully understand

other agent’s ontology once different representations

and terminologies may exist for the same concepts

and there is no formal mapping procedure available.

3.2 Interoperability Problem

In a decentralized and distributed approach,

interoperability refers to the way we communicate

with people and software agents, the problems

which hamper the communication and collaboration

between agents. Our objective is to help in the

interoperability problem, enhancing agents with

abilities to provide services to and accept services

from other agents, and to use these services so

exchanged to enable agents to effectively negotiate

together.

By making the enterprise agents interoperable,

we enable them to meet the basic requirement for

multilateral cooperation. In ForEV platform, the

enterprise agents have homogeneous structures as

well as the same domain of discourse. However, in

real-life situations, real problems involve

heterogeneity. This kind of problems makes the

negotiation process difficult for the VE partners’

selection, and for the cooperation process in the VE.

There are two major types of cooperative

interaction which may be identified in a Multi-Agent

System: the first concerns which agents perform

which tasks (the task allocation problem) and the

second concerns the sharing of information (both

results and observations on the outside world)

between agents. Purpose heterogeneity is primarily

concerned with the former type and semantic

heterogeneity with the latter (Roda, et. al, 1991).

The use of a common ontology guarantees the

consistency (an expression has the same meaning for

all the agents) and the compatibility (a concept is

specified by the same expression, for any agent) of

the shared information in the system (Macedo,

2001). However, ontologies are often developed by

several persons and continue to evolve over time.

Moreover, domain changes adaptations to different

tasks, or changes in the conceptualisation might

cause modifications on the ontology. This will likely

cause incompatibilities (Klein et al., 2002). Some

research has been done trying to solve the problem

of interoperability (Klein et. al, 2002), (Welty and

Guarino, 2001), (Rodríguez and Egenhofer, 2003),

using semantic relations lexical taxonomy, semantic

similarities, linguistic similarities of terms,

taxonomic relationships, and using text information.

3.3 Agents’ Ontology Creation

In the last years, the number of tools for building

ontologies developed both by the American and

European research communities was large.

Whenever a new ontology is going to be built,

several basic questions arise related to the tools to be

used (Gómez-Pérez, 2002). Several development

tools are similar and there is not a complete tool for

all the ontology life cycle. A good selection depends

on the necessity of the user, and thus the user has to

read about the characteristics (description,

architecture, interoperability, representation support,

inference services, and usability) to choose the tool,

which is the most suitable for his objectives.

Ontology creation for our particular domain

(cars’ assembling domain) involved literature search

on cars’ assembling domain and discussion with

experts. After careful consideration and test of

several different ontology building tools, we have

selected the appropriated ones. First we have

modelled our ontology by means of UML and then

an ontology-building tool has been used.

ICETE 2004 - GLOBAL COMMUNICATION INFORMATION SYSTEMS AND SERVICES

In this work the ontologies have been developed

using the Protégé (Gennari et al., 2002), WebODE

(Arpírez et al., 2003) and OntoEdit (Sure et al.,

2002) tools. We are using a decentralized and

distributed approach according to our Multi-Agent

System architecture, where each agent explores its

own ontology created by means of different tools

and knowledge structure.

4 ONTOLOGY-BASED SERVICES

A central point of the agents paradigm of software

development is that communities of agents are much

more powerful than any individual agent, which

immediately raises the necessity for interoperable

agent systems.

Consider the following simple negotiation

example: the market agent, representing the

customer, needs to buy a “wheel” (a simple machine

consisting of a circular frame with spokes (or a solid

disc) that can rotate on a shaft or axle in vehicles)

and an enterprise agent offers “wheel” (a handwheel

that is used for steering). These two components

belong to the same cars’ domain, they are

syntactically the same but semantically different,

and probably the agents will negotiate under these

components. Otherwise, when the market agent

needs to buy “tyre” (a thick rubber ring, often filled

with air, which is fitted around the outer edge of the

wheel of a vehicle…) an enterprise agent does not

offer the component because it sells “tire” (a thick

rubber ring, often filled with air, which is fitted

around the outer edge of the wheel of a vehicle…).

In this case, both words are also in the same cars’

domain, but they are syntactically different and

semantically the same. In the first case the agents

will lose time negotiating under different products

and in the second example, when the negotiation

could be fruitful, they will not negotiate because

they do not understand each other.

Sometimes also, agents are negotiating a

good/product/service using different attributes. The

market agent, for example, needs car’s motor and

announces the basic requirements such as power,

consumption, number of cylinders, torque, quantity,

delivery time, injection and transmission. The

enterprise agent offers the car’s motor using

characteristics as price, delivery time, quantity,

cylinder piston position, motor type and power. Both

agents have to negotiate using the same

characteristics and some requirements are even

mandatory. In this example, it is essential to describe

power, torque, consumption and number of cylinder,

because if one of these characteristics is not the

same, the other characteristics do not matter.

Besides these problems, other problems may

occur like using different currencies to denote

different prices, different units to represent

measurement, a different structural properties

representations and mutual dependencies between

attributes. These problems may make the negotiation

process even harder.

In order to help the resolution of potential

incompatibilities as the ones explained before, some

ontology-based services are proposed:

(i) definition

of each product attribute’s dependencies, (ii)

abilities to translate terms between two different

ontologies, (iii) capability of learning with the

ontology-based services already provided, so that it

could be possible to use this information in a future

negotiation, (iv) converting values when agents

work with different metrics, (v) advising about

mandatory or different attributes of items under

negotiation.

An Ontology-based Services Agent is created

and it has a basic local ontology. The Ontology-

based Services Agent monitors all the

communication and negotiation. When Market

Agent sends an announcement asking for some

item/good required, all the Enterprise Agents may or

may not understand the description (item/good)

announced. If the enterprise agents understand the

item under negotiation, and if it is of its interest, it

may formulate a proposal and the negotiation

process starts.

During the negotiation new interoperable

problems may occur. The agents involved in the

negotiation may ask to Ontology-based Services

Agent for helping whenever they need. However,

Ontology-based Services Agent will be helping even

without being solicited. If some problem is detected,

in order to help the resolution of the incompatibility,

the Ontology-based Services Agent exchange

messages with the involved agents asking for more

information, consulting the local ontology and web

services whenever it is necessary.

4.1 Integration of Ontology-based

Services Agent with ForEV

In ForEV platform, as explained in the Section 2.1,

the Register Agent (RA) is responsible for: (i) the

local market creation and maintenance, where the

multiple agents meet each other and interact, and (ii)

creating and presenting the domain ontology for all

the participants. Now, ForEV has been enhanced

with new functionalities that make those

heterogeneous communicating agents to understand

each other no matter the differences in their own

ontologies. RA is still responsible for the local

market creation and maintenance, however instead

B2B TRANSACTIONS ENHANCED WITH ONTOLOGY-BASED SERVICES

of a common ontology, ForEV accepts

heterogeneous agents including their own specific

ontology, which may be just in correspondence

through the new Ontology-based Services Agent.

This new Ontology-based Services Agent is

integrated in ForEV architecture to help in the VE

formation process, providing useful advices on how

to better negotiate specific items and how different

terms may be understood as equivalent. It is up to

this Ontology-based Services Agent to make it

possible negotiations in the Multi-Agent System and

finally lead to acceptable and meaningful

agreements.

Ontology-based Services Agent keeps

monitoring the whole agents interaction just in time,

without needing a previous and tedious complete

ontology mapping process. Each agent (market or

enterprise) has its own architecture and

functionalities (some developer will design and

build the ontology with some tool and, later, the

agent will access the generated file/database).

The Ontology-based Services Agent is

monitoring the negotiation and communication,

accessing a local ontology and web services

whenever it is necessary and updating the local

ontology whenever new concepts are discovered,

this way the Ontology-based Services Agent may

use previous knowledge in the next negotiation

rounds.

5 IMPLEMENTATION

In our proposed architecture each agent may be

geographically distant exploring its own ontology,

built by different developers.

We are using Java technologies such as: (i)

Applet/Servlet to create a protected communication

channel between the client machine and the server. It

will be used to send information about the user and

the agent to the server databank, (ii) RMI (Remote

Method Invocation) to access the agent object and

use its methods through the applet, (iii) JATLite to

implement the communications infrastructure, which

has specific facilities to messages passing and

queueing treatment using KQML language, (iv)

SOAP/WSDL for the ontology agent to use distant

services on the Web.

The central component of JATLite platform is

the ARM (Agent Message Router) application,

where all the agents in the system have to register in.

The AMR stores the information about all the agents

with their identity (name and password) and

localization (machine and physical location). AMR

is the responsible for all the distributed system

communication management. KQML (Knowledge

Query and Manipulation) is the language used for

communication.

6 CONCLUSION

In this paper we have presented the ForEV agent-

based platform, which have been developed aiming

to facilitate the partners’ selection automatic process

in the context of Virtual Enterprise formation.

ForEV platform has implemented the Q-Negotiation

algorithm, which includes appropriate features for

dealing with the specific requirements of the VE

formation scenario. An important requirement in this

process, is that information must be kept private to

individual enterprises, since they are competitive by

nature and do not want to reveal their market

strategy to others. The Q-Negotiation algorithm has

the ability to maintain information private and, at the

same time, it includes the capability to evaluate

multi-attribute proposals, to guide learning during

the negotiation process, and to resolve attributes’

mutual inter dependencies.

The presence of several heterogeneous customer

and supplier agents makes it necessary ontologies to

ensure that agents are negotiating about exactly the

same item. One of the problems of using different

ontologies is that there exist different representations

and terminologies and on the other hand, there is no

formal mapping available between high-level

ontologies. Product description heterogeneity is a

critical impediment to efficient business information

exchange.

Several problems involved in the resolution of

interoperability are difficult to be solved, at least

nowadays, but it is important to look for possible

ways to resolve parts of the problem. Many different

ontology technologies are already available

(methodologies, tools and languages), and the future

points towards the use of these technologies and to

develop functionalities to improve the business

information exchange. Therefore, we have proposed

Ontology-based Services to enhance B2B

transactions given support in the negotiation needed

for Virtual Enterprise formation process. In our

Figure 5: Technologies Platform

ICETE 2004 - GLOBAL COMMUNICATION INFORMATION SYSTEMS AND SERVICES

proposed architecture each agent may be

geographically distant, exploring its own ontology,

built up by using different ontology technologies. In

our experimental scenario ontologies were built

using Protégé, WebODE, and OntoEdit.

The Ontology-based Services Agent proposed

here, monitors agents communication and

negotiation, accessing a local ontology and web

services to help whenever the negotiation process is

hard. This may be caused by problems like agents

using different attributes to negotiate the same kind

of product, different currencies to denote different

prices, different units to represent measurement or a

different structural property representations. The

local ontology will be updated whenever new

concepts are discovered, enabling the Ontology-

based Services Agent to use previous knowledge in

the next negotiation rounds.

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B2B TRANSACTIONS ENHANCED WITH ONTOLOGY-BASED SERVICES