Agent-based Electronic Commerce with Ontology Services and Social

Network based Support

Virgínia Nascimento, Maria João Viamonte, Alda Canito and Nuno Silva

GECAD, Knowledge Engineering and Decision Support Research Center, Institute of Engineering,

Polytechnic of Porto (ISEP/IPP), Porto, Portugal

Keywords: Agent Mediated Electronic Commerce, Semantic Heterogeneity, Ontology Matching, Ontology Alignment

Negotiation, Emergent Social Networks.

Abstract: In this paper we approach the semantic heterogeneity problem which arises in agent mediated electronic

commerce, presenting the AEMOS system as a promising answer. AEMOS is an agent mediated electronic

commerce platform which main goal is to enable an efficient and transparent negotiation between agents

even when they use different ontologies to represent the same domain of knowledge. The system provides

ontology services, more specifically ontology matching services, which are improved by the exploitation of

emergent social networks. In this paper we present the currently implemented system and demonstrate how

an agent mediated electronic commerce system may benefit from the inclusion and combination of ontology

services and social network based support.

1 INTRODUCTION

Electronic commerce (e-commerce) is a widely used

technology with an increasing popularity in today’s

business (Du et al., 2005). In this type of commerce

the information becomes more easily available,

increasing the possibility of achieving more

satisfactory deals. However, the amount of available

information also becomes a problem, being difficult

for a human user to compare all possible deals in

order to achieve the best one.

Intelligent agents present characteristics that

make them a powerful tool to overcome this

problem. However, the diversity of the available

information, which is normally represented for

human comprehension only, turns the development

of fully automated systems into a challenge.

In order to overcome this problem, ontology

centered approaches have been proposed (Mei et al.,

2009, Cao et al., 2009) and e-commerce key players

such as Google, Yahoo, Amazon and O’Reilly are

progressively supporting these through micro-

formats, micro-data and RDFa (O'Brien, 2009).

However, given the natural diversity of such an

open and accessible environment, the involved

entities may possess different conceptualization

about their needs and capabilities, giving rise to a

semantic heterogeneity problem that is seen as a

corner stone for agents’ interoperability.

Based on these issues we developed AEMOS –

Agent-based Electronic Market with Ontology

Services (Nascimento et al., 2012, Viamonte et al.,

2012, Viamonte et al., 2011, Silva et al., 2009), an

innovative project (PTDC/EIA-EIA/104752/2008)

supported by the Portuguese Agency for Scientific

Research (FCT).

The main goal of this project is to provide an

agent mediated e-commerce (AMEC) platform

capable of enabling an efficient and transparent

negotiation between agents even when they use

different ontologies, ensuring that they are able to

understand each other and correctly assess the terms

and conditions of each transaction. For that we

follow an ontology based information integration

approach, exploiting the ontology matching

paradigm (Euzenat and Shvaiko, 2007), which is

improved by the application and exploitation of

emergent social networks (SN).

During the development of this project different

models have been proposed and tested, e.g. see

(Nascimento et al., 2012, Viamonte et al., 2012,

Viamonte et al., 2011, Silva et al., 2009). In this

paper we present the currently implemented system,

clarifying the fundaments leading to our choices,

and presenting the more recently achieved results.

497

Nascimento V., João Viamonte M., Canito A. and Silva N..

Agent-based Electronic Commerce with Ontology Services and Social Network based Support.

DOI: 10.5220/0004448904970504

In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 497-504

ISBN: 978-989-8565-60-0

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

We start by briefly describing the research

background (Section 2). Then we present an

overview of the AEMOS system (Section 3),

highlighting the implemented ontology services

(Section 4) and SN-based support (Section 5). In

order to assess our proposal we present details about

the AEMOS prototype and describe some

experiments analyzing the achieved results (Section

6). Finally we draw some conclusions and suggest

follow-up research efforts (Section 7).

2 BACKGROUND

The most frequent approaches for AMEC systems

consider simplified and limited solution in order to

avoid semantic problems. Some consider the

existence of a commonly agreed ontology, such that

to participate in the market each agent has to adopt

this ontology, e.g. (Viamonte et al., 2007). Other

approaches, consider the existence of different

ontologies, but only allow communication between

agents that use the same ontology, e.g. (Cui-Mei,

2009).

The Foundation for Intelligent Physical Agents

(FIPA) has analyzed the interoperability problem in

heterogeneous multi-agent systems (MAS) and has

proposed the introduction of an agent that, among

other responsibilities, would be capable of

translating expressions between different ontologies

(FIPA, 2001). An implementation of such an agent

is proposed in (Briola et al., 2008), where the

translation service is achieved through ontology

matching.

Ontology matching (Euzenat and Shvaiko, 2007)

can be described as the process of discovering

semantic relations (i.e. correspondences) between

the concepts and properties of two ontologies. The

discovered relations are represented in an ontology

alignment document so they can be applied in data

transformation.

There are already some approaches for AMEC

systems which include ontology matching services

in order to solve semantic problems. Some examples

are the models presented in (Malucelli et al., 2006)

and in (Wang et al., 2010). However, each approach

tends to focus on a particular aspect or phase of the

known behavior models, and often ignore the effect

that the complexity of the matching processes can

have in the communications’ efficiency.

Ontology matching is a naturally ambiguous and

subjective process, leading to different alignments

that may be more or less adequate to each

negotiation and therefore influence its efficiency and

result. The quality and adequacy of an ontology

alignment is very important in the negotiation, since

it may determine the efficiency of the interaction.

For example, a consumer may request a product that

a supplier has on its inventory, but by using an

inadequate alignment, relevant information may be

lost during the transformation process causing the

supplier not to be able to match it.

On the other hand, detecting incorrect or

inadequate ontology alignments is not a trivial task.

A negotiation may fail because the alignment is

inadequate to the current context, but it can also fail

simply because the supplier does not have the

desired products, or even because the agents have

different goals (e.g. conflicting prices).

3 AEMOS SYSTEM OVERVIEW

The AEMOS system is based on ISEM (Viamonte et

al., 2007), which is an agent-based simulator system

for e-commerce, originally developed for studying

agents’ market strategies. In reality, AEMOS is an

evolution of the ISEM system, keeping all its

original functionalities, but allowing agents to use

different ontologies.

AEMOS provides ontology services in order to

enable negotiation between agents that use different

ontologies. The system exploits the ontology

matching paradigm (Euzenat and Shvaiko, 2007),

selecting and suggesting possible alignments

between the agents’ ontologies, and letting the

agents choose which one should be used to translate

the subsequent exchanged messages.

In order to overcome issues related to how the

chosen ontology alignment may influence the

business negotiation efficiency, the relevance of

social network analysis (SNA) (Wasserman and

Faust, 1994) in recommending ontology alignments

for e-commerce negotiations is claimed, by

including in the system a SN-based support

component, capable of improving the ontology

alignments recommendations and supporting the

agents’ decisions about which alignment to choose.

3.1 Multi-Agent Model

The AEMOS multi-agent model, illustrated in

Figure 1, includes several types of agents classified

in two main categories, namely: the business agents

and the supporting agents.

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

498

Figure 1: AEMOS multi-agent model.

The business agents represent real world entities,

which possess business goals to be satisfied. These

agents are highly customizable and dynamic. In each

situation, they adapt their strategies, according to the

present context and based on their updated

knowledge (Viamonte et al., 2007). Currently two

types of business agents are considered, namely:

 Buyer (B) – agent representing a consumer,

i.e., an entity, normally a person wishing to

acquire a set of products;

 Seller (S) – agent representing a supplier, i.e.

an entity, usually a company wishing to sell a

set of products.

The supporting agents are those providing

services that allow business agents to carry

transactions with each other in order to satisfy their

goals. This category can be further divided in two

groups, namely: the service intermediary agents and

the system management agents.

The service intermediary agents support the

business agents, providing services that enable an

efficient interoperability between them. In this

category are the agents:

 Market Facilitator (MF) – agent that

coordinates the interaction between business

agents, being responsible for ensuring that the

communicating agents are able to understand

each other. When a B agent is registered, a

MF agent is associated; from that moment on,

all messages related to the business

negotiation process pass through the

associated MF agent;

 Ontology Matching intermediary (OM-i) –

agent responsible for the ontology services,

recommending possible ontology alignments

for each business negotiation, and

transforming the exchanged messages

according to the agreed alignment. When a

MF agent is initiated an OM-i agent is

associated; from that moment on, all the

requests related to ontology matching services

are sent to the associated OM-i agent;

 Social Network intermediary (SN-i) – agent

responsible for the SN-based support,

providing advice about the adequacy of the

ontology alignments to each business

negotiation. When an OM-i agent is initiated,

or a business agent registers in the market, a

SN-i agent is associated; from that moment

on, all requests related to SN-based support

are sent to the associated SN-i agent.

Normally there are multiple agents of these types

per marketplace, being initialized when necessary.

The system management agents are responsible

for granting the system’s dynamism, flexibility and

correct functioning. In this category are the agents:

 Market Manager (MM) – agent responsible to

manage all supporting agents and to register

business agents so they can participate in the

market. Normally there is only one agent of

this kind per marketplace;

 Market Extension Manager (MEM) – agent

that aids the MM on its functions, allowing the

dynamic addition of machines where

supporting agents may be initialized. The

presence of this kind of agent is optional,

although normally there are multiple agents of

this type per marketplace;

 Market Data Manager (MDM) – agent that

collects and maintains information about the

market participants and their activities, writing

statistical reports which allow evaluating the

system’s performance. Normally there is only

one agent of this type per marketplace;

 Clock – agent that simulates the evolution of

time, notifying the appropriate agents about

periodic (or scheduled) events. Normally there

is only one agent of this type per marketplace.

In addition to these types of agents there are the

Communication Facilitator agents, which are

responsible for establishing communications

between the different agents of the system. Since our

system is based in OAA (OAA, 2001), this role is

played by the Facilitator agent provided by this

platform.

3.2 Interaction Protocol

To participate in the market, the business agents

must register first, providing information about the

ontologies that they use, and sharing (parts of) the

profile of the entity they represent. This information

is stored by MF and SN-i agents. Once registered,

the agents are allowed to negotiate. For that, B

agents start announcing their buying products and

wait for S agents to formulate proposals. Figure 2

Agent-basedElectronicCommercewithOntologyServicesandSocialNetworkbasedSupport

499

illustrates the interactions between the main

intervenient during a business negotiation.

Figure 2: General agents’ interaction.

When the negotiation starts, the responsible MF

selects the S agents that might be able to satisfy the

B agent’s request. For that it follows an ontology-

based approach, selecting: (i) the S agents that use

the same ontology as the B; and, (ii) supported by an

OM-i, the ones that use ontologies that can be

aligned with it. Therefore, the business negotiations

may occur in two different scenarios:

 A scenario where both agents use the same

ontology – the MF acts as a proxy between B

and S, simply receiving and forwarding

messages;

 A scenario where the agents use different

ontologies – it is necessary to find an

agreement about the alignment between the

respective ontologies that should be used to

translate the exchanged messages. For that the

MF requests an OM-i to mediate an ontology

alignment negotiation between B and S. If an

agreement is achieved, the subsequent

exchanged messages are sent to the OM-i,

which translates their content according to the

agreed alignment ensuring that the message

receiver will be able to understand it.

During the business negotiation the involved

agents, B and S, exchange proposals and

counterproposals, following a protocol based on the

FIPA’s “Iterated Contract Net Interaction Protocol

Specification” (FIPA, 2002), terminating the

negotiation when an agreement is achieved or when

they have no more proposals to formulate.

When a business agent satisfies all its business

goals, or its deadlines are reached, it must terminate

its activity, notifying the market and declaring the

achieved results.

3.3 Ontology Alignment Negotiation

Protocol

The ontology alignment negotiation initiates when a

MF sends a request to an OM-i identifying (i) both

agents, (ii) the respective ontologies and (iii)

providing information about the B agent’s request.

The OM-i selects, from its repository, all the

possible alignments between the indicated

ontologies. Then, it performs sorting and filtering

actions, following its internal criteria and/or

requesting a SN-i to rank the alignments, obtaining a

list of possible alignments and their respective score.

Both B and S, analyze the recommended alignments

taking into account their preferences, replying to the

OM-i with the list of the alignments which they

consider acceptable.

The OM-i analyzes both replies and checks if

there is an agreement, i.e., if some alignment was

selected by both agents. If there is no agreement,

depending on the system configuration, the

negotiation may terminate, or proceed, with the OM-

i refining its list of recommended alignments and

asking agents to reconsider their options and criteria.

Otherwise, if there is an agreement, the OM-i

notifies both agents and the MF about the agreement

and proceeds with the transformation of the B

agent’s request. From that moment on, all the

subsequent exchanged messages between the agents

are forward to the OM-i for transformation.

4 ONTOLOGY SERVICES

The ontology services are provided by OM-i agents.

An OM-i is responsible for (i) discovering

ontologies and ontology alignments, (ii) providing

information about ontologies and alignments, (iii)

proposing alignments for negotiation, (iv)

coordinating alignment negotiations, and (v)

transforming ontology’s instances when requested.

Although these responsibilities are attributed to

the OM-i, this agent normally requests services from

other specialized agents in order to perform these

tasks (especially for the ontology matching process),

e.g. see (Viamonte et al., 2011).

To improve performance, currently, the ontology

matching process is performed externally to the

negotiation process. It is considered a registry of

ontologies that are recognized by the agent and a

repository of possible alignments between them.

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

500

This information can be updated at any time, as new

ontologies are discovered and ontology alignments

are created.

We also consider that agents may represent their

domain of knowledge using public ontologies, i.e.

ontologies publicly accessible, having their own

URL, either through a dedicated web page or being

stored in web repositories. Therefore it is possible to

gather ontologies used in an e-commerce context

and discover possible alignments between them, or

even collect already existent alignments from public

web sources.

When the alignment negotiation is requested, the

OM-i selects from its alignments repository the ones

that involve both of the indicated ontologies. It then

ranks, sorts and filters the alignments either by (i)

requesting a SN-i to rank the alignments for the

business negotiation, or (ii) by analyzing their

coverage of the ontology’s concepts and properties

used by the B to describe the requested product. It

then coordinates the alignment negotiation following

the protocol previously described (cf. Section 3.3).

In order to improve its recommendations, in each

alignment negotiation’s iteration, the OM-i stores

and maintains information about the recommended

alignments and the achieved agreements.

The transformation of a message’s content (i.e.

ontology’s instance) is performed using the agreed

alignment. This process is provided by information

integration tools such as MAFRA Toolkit (Maedche

et al., 2002) and it is transparent to the agents.

5 SOCIAL NETWORK BASED

SUPPORT

The SN-based support is introduced in the system in

order to enhance the communication’s efficiency, by

improving the evaluation of the alignments’

adequacy to each business negotiation.

In previous work, we proposed two different

models for this component: one based in explicit

social networks (Viamonte et al., 2012) and another

based in emergent social networks (Nascimento et

al., 2012). In the first the business agents provide

information about their own evaluations of their

business partners and used ontology alignments,

while in the latter the SN-i agents analyze the

similarities between the agents’ profiles and

behavior, as well as the outcomes of their

interactions. We consider this last model more

interesting and adequate to our current problem

since it is less demanding for the business agents

(increasing the transparency of the process), and less

dependent on them as well, allowing us to overcome

problems such as the feedback credibility (Das et al.,

2011). This model also allows us to take advantage

of the collaboration between the system’s agents.

During the market activity, the SN-i collects

information about its participants and their

interactions. Then it builds and maintains the

relationship graph, applying SNA techniques

(Wasserman and Faust, 1994) in order to capture

proximity relations between agents, and adequacy

relations from alignments to agents, which emerge

during the agents’ activities in the market. By

combining this information, the SN-i is able to

evaluate the adequacy of the alignments to each

business negotiation.

A detailed description of the SN-i agent’s model,

as well as the fundaments behind it, can be found in

(Nascimento et al., 2012). In this paper we present

only the key aspects of its responsibilities, which are

as follows.

Collect information throughout the market: the

SN-i receives information from the other agents on

the market which will allow it to, in return, support

them in their tasks, e.g.: (i) business agents provide

information about the profile of the entities they

represent and about ontologies’ usage and

preferences; (ii) MF agents provide information

about the business negotiations between the agents

(e.g. both agents’ identification, the used alignments,

the negotiation outcome, the satisfaction of B with

the deal); and (iii) OM-i agents provide information

about ontologies, alignments and previous alignment

negotiations.

Evaluate Agent-To-Agent proximity: based in

some theories supported in the literature, the SN-i

combines a series of factors in order to capture

proximity relations between agents. These factors

are: (i) the similarity between the agents’ profiles

and ontologies usage and preferences; (ii) the

similarity between their interactions with other

agents; (iii) the success rate of their own previous

business negotiations; and (iv) the average

satisfaction of B about purchased products from S.

Evaluate Alignment-To-Agent adequacy: to

determine the adequacy of an alignment to an agent,

the SN-i evaluates: (i) the alignment’s coverage of

the agent’s used ontologies’ concepts and properties

(considering their respective relevance); (ii) the

agent’s success rate in business negotiations using

the alignment; and (iii) the agent’s average

satisfaction in deals using the alignment.

Evaluate Alignment-To-Business-Negotiation

adequacy: the adequacy of an alignment to a

Agent-basedElectronicCommercewithOntologyServicesandSocialNetworkbasedSupport

501

business negotiation will depend on many factors,

namely: (i) the coverage of the alignment in relation

to the requested product’s description; (ii) the

general success rate in negotiations using the

alignment; (iii) the general average satisfaction in

deals using the alignment; (iv) the adequacy of the

alignment to each of the involved agents; and (v) the

adequacy of the alignment to the agents closest (i.e.

with high proximity relations) to the involved

agents.

6 EXPERIMENTS AND RESULTS

In order to test and validate the AEMOS proposed

model a new system was developed and several

experiments were performed considering different e-

commerce scenarios. In this section we present

details about the implemented system and analyze

the achieved results.

6.1 The AEMOS Prototype

The AEMOS system was developed in Open Agent

Architecture (OAA). The OAA’s Interagent

Communication Language is the interface and

communication language shared by all agents, and

each agent is implemented in Java. The model can

be distributed over a network of computers, which is

a very important advantage considering the large

amount of agents that may exist per market.

In order to test and validate the AEMOS model,

and compare it with other AMEC approaches, we

developed an application that enables the simulation

of different e-commerce scenarios. The AEMOS

simulator is very flexible as it allows defining the

model to simulate, including the available services’

configuration, the number of business agents, each

agent’s type, ontologies and strategies. The set-up of

the AEMOS system is characterized by three

dimensions: (i) the business agents’ dimension,

which includes the business entities’ profiles,

inventories/shopping lists and satisfaction measuring

functions; (ii) the ontology services dimension,

which includes the considered ontologies and

alignments; and (iii) the SN dimension, which

includes the SN-i agents’ parameters to capture the

emergent SN and perform evaluations. Each of these

dimensions includes several parameters which can

be configured.

6.2 Case Study

In this experiment we intend to demonstrate how the

inclusion of ontology services, and combination

with SN-based support, can improve the business

negotiation’s efficiency, leading to a higher

satisfaction in the performed transactions.

Due to lack of space, the used configuration is

not fully presented here. We describe only key

aspects in order to provide a better understanding of

the achieved results.

We study a simple market composed by 4

suppliers and 7 consumers, whose profiles are

randomly generated. In order to correctly assess our

proposal the agents negotiate the same type of

product. To demonstrate the usefulness of our

system we include situations where agents that use

different ontologies could provide more satisfactory

deals, i.e. supply products with more similar

characteristics to the ones desired by the B, or that

contemplate the ones that the B values most.

Note that in this experiment we are focusing on

the ontology dimension of the negotiation, so other

factors in the formulation/selection of a proposal

(e.g. price, delivery time, quality of service) are

considered to be similar and compatible for each

agent. Therefore, the satisfaction of a B with a deal

will correspond to the similarity between the

purchased product and the desired one. The

satisfaction value is obtained by averaging each

attribute’s similarity, weighing by the relevance that

B attributes to each attribute. More details on this

function can be found in (Nascimento et al., 2012).

We consider three different ontologies. For each

pair of ontologies, two alignments were specified: (i)

one containing all the correct correspondences

between the ontologies; and (ii) another containing

less of these correct correspondences and including

some others which are incorrect.

6.3 Scenarios and Results

In order to test and validate our model, and based in

the most frequent approaches for AMEC (cf. Section

2), we considered four different scenarios:

 Scenario 1 – A scenario without both ontology

services and SN-based support. The agents

negotiate only with agents that use the same

ontology;

 Scenario 2 – A scenario with ontology

services but no SN-based support. However,

this scenario only considers the correct

alignments. The OM-i agents evaluate the

alignments’ coverage of the concepts and

properties used by the B to describe the

requested product, and the agents choose the

ones with higher coverage;

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

502

 Scenario 3 – A scenario similar to scenario 2

but considering all created alignments;

 Scenario 4 – A scenario with both ontology

services and SN-based support. OM-i request

an SN-i to evaluate the alignments and the

agents choose the ones with higher score.

Each scenario ran several times in the AEMOS

simulator. Table 1 presents the average satisfaction

in deals obtained in each scenario, as well as the

average adequacy of the used alignment.

Table 1: Average results from each scenario.

Satisfaction

in Deals

Alignments’

Adequacy

Scenario 1

0.55 -

Scenario 2

0.65 -

Scenario 3

0.52 0.14

Scenario 4

0.62 0.30

Comparing the first two scenarios allows us to

demonstrate how the system could benefit from the

inclusion of the ontology services by itself.

However, while in the first scenario the agents are

limited to communicate with agents that use the

same ontologies, the second represents an unrealistic

situation, by considering that the ontology

alignments are always semantically correct and

equally adequate. When we include the incorrect

alignments in the system the achieved satisfaction in

deals is even lower than the one achieved in the first

scenarios. This is due to the fact that, in this third

scenario the agents will continue choosing the less

adequate alignments which will cause a severe

impact on their business satisfaction.

Figure 3: Trending of satisfaction in deals and adequacy of

the used alignment in scenarios 3 and 4.

As Figure 3 illustrates, by including the SN-

based component, the alignment recommendations

tend to improve with time, allowing the agent to

choose the most adequate alignments achieving a

higher business satisfaction.

Moreover, by comparing the results achieved in

scenarios 3 and 4, we can conclude that, when the

SN-based support is included the agents need to

negotiate less (20%) to achieved their business

goals, there are less (29,4%) failed interactions, and

there are more (14.3%) transacted products.

7 CONCLUSIONS AND FUTURE

WORK

The exploitation of the ontology matching paradigm

has been proposed in order to overcome the

semantic heterogeneity problem which arises in

open MAS. However, ontology matching may turn

into a highly complex and time consuming process

affecting the system’s performance. Ontology

matching is also a naturally ambiguous and

subjective process, which may lead to different

alignments that may be more or less adequate to

each negotiation, affecting its efficiency and result.

On the other hand, detecting incorrect or inadequate

alignments is not a trivial task due to the different

variables that may contribute for the negotiation

outcome.

The system presented in this paper takes these

issues into account, providing an AMEC system

capable of enabling an efficient and transparent

negotiation between agents, even when they use

different ontologies. For that the system includes

and combines ontology matching services and SN-

based support. The ontology services allow agents to

interact with a higher range of business partners,

increasing the probability of achieving more

satisfactory deals, while the SN-based component

improves the business negotiations’ efficiency by

improving the recommendation/usage of ontology

alignments.

The performed experiments have demonstrated

the usefulness and effectiveness of the implemented

model, being successful in the fulfilling of our initial

goals. However, we believe there are some aspects

in the systems which can be improved and future

research directions can be referred. For example, the

SN-based support component could be significantly

improved, exploiting other SNA techniques, in other

to achieve a more sophisticated model. Another

aspect to improve soon is the negotiation protocol,

both for business and ontology alignment

negotiations. The currently implemented protocol is

a legacy from the ISEM system and we believe that

a more sophisticated/efficient protocol could be

designed/adopted.

Agent-basedElectronicCommercewithOntologyServicesandSocialNetworkbasedSupport

503

ACKNOWLEDGEMENTS

This work is supported by FEDER Funds through

the “Programa Operacional Factores de

Competitividade - COMPETE” program and by

National Funds through FCT “Fundação para a

Ciência e Tecnologia” under the projects: FCOMP-

01-0124-FEDER-PEst-OE/EEI/UI0760/2011;

PTDC/EIA-EIA/104752/2008.

REFERENCES

Briola, D., Locoro, A. & Mascardi, V. 2008. Ontology

Agents in FIPA-compliant Platforms: a Survey and a

New Proposal. In: Baldoni, M., Cossentino, M., De

Paoli, F. & Seidita, V. (eds.) Atti del Workshop Dagli

Oggetti agli Agenti , WOA'08. Seneca Edizioni.

Cao, M., Feng, Y. & Liu, Z. 2009. E-Commerce Oriented

Negotiating Agent Communication Model. 42nd

Hawaii International Conference on System Sciences

(HICSS '09). Waikoloa, Big Island, Hawaii, USA:

IEEE Computer Society.

Cui-Mei, B. 2009. Combining Intelligent Agent with the

Semantic Web Services for Building An e-Commerce

System. 2009 IEEE International Conference on E-

Business Engineering (ICEBE '09). Macau, China:

IEEE computer society.

Das, A., Islam, M. M. & Sorwar, G. 2011. Dynamic Trust

Model for Reliable Transactions in Multi-agent

Systems. 2011 13th International Conference on

Advanced Communication Technology (ICACT '11).

Phoenix Park, Korea (South): IEEE computer society.

Du, T. C., Li, E. Y. & Chou, D. 2005. Dynamic vehicle

routing for online B2C delivery. Omega, 33, 33-45.

Euzenat, J. & Shvaiko, P. 2007. Ontology matching,

Secaucus, NJ, USA, Springer-Verlag New York, Inc.

FIPA. 2001. FIPA Ontology Service Specification.

Available: http://www.fipa.org/specs/fipa00086/

[Accessed 28-01-2013].

FIPA. 2002. FIPA Iterated Contract Net Interaction

Protocol Specification. Available: http://

www.fipa.org/specs/fipa00030/ [Accessed 28-01-

2013].

Maedche, A., Motik, B., Silva, N. & Volz, R. 2002.

MAFRA - A Mapping Framework for Distributed

Ontologies in the Semantic Web. 13th International

Conference on Knowledge Engineering and

Knowledge Management. Ontologies and the Semantic

Web (EKAW '02). Siguenza, Spain: Springer.

Malucelli, A., Palzer, D. & Oliveira, E. 2006. Ontology-

based Services to help solving the heterogeneity

problem in e-commerce negotiations. Electron.

Commer. Res. Appl., 5, 29-43.

Mei, P. Q., Hong, Z., Cun, C. Y. & Qin, P. X. 2009. An E-

negotiation Model Based on Multi-agent and

Ontology. Proceedings of the 2009 International

Conference on Computational Intelligence and

Natural Computing (CINC '09). Wuhan, China: IEEE

Computer Society.

Nascimento, V., Viamonte, M. J., Canito, A. & Silva, N.

2012. Enhancing ontology alignment recommendation

by exploiting emergent social networks. 2012

IEEE/WIC/ACM International Conference on

Intelligent Agent Technology (WI-IAT '12). Macau,

China: IEEE Computer Society.

O'Brien, T. M. 2009. Google Announces Support for

Microformats and RDFa. Available:

http://radar.oreilly.com/2009/05/google-announces-

support-for-m.html [Accessed 28-01-2013].

OAA. 2001. OAA [Online]. Available: http://

www.ai.sri.com/~oaa/ [Accessed 28-01-2013.

Silva, N., Viamonte, M. J. & Maio, P. 2009. Agent-Based

Electronic Market With Ontology-Services. 2009

IEEE International Conference on e-Business

Engineering (ICEBE '09).

Macau, China: IEEE

Computer Society.

Viamonte, M. J., Nascimento, V., Silva, N. & Maio, P.

2012. AEMOS: An Agent-Based Electronic Market

Simulator With Ontology-Services And Social

Network Support. 24th European Modeling &

Simulation Symposium (Simulation in Industry) (EMSS

'12). Viena, Austria.

Viamonte, M. J., Ramos, C., Rodrigues, F. & Cardoso, J.

2007. ISEM: A Multi-Agent System That Simulates

Competitive Electronic MarKetPlaces. International

Journal of Engineering Intelligent Systems for

Electrical Engineering and Communications: Special

Issue on Decision Support, 15, 191-199.

Viamonte, M. J., Silva, N. & Maio, P. 2011. Agent-Based

Simulation of Electronic Marketplaces With

Ontology-Services. 23rd European Modeling &

Simulation Symposium (Simulation in Industry) (EMSS

'11). Rome, Italy.

Wang, G., Wong, T. N. & Wang, X. H. A Negotiation

Protocol to Support Agent Argumentation and

Ontology Interoperability in MAS-Based Virtual

Enterprises. Information Technology: New

Generations (ITNG), 2010 Seventh International

Conference on, 12-14 April 2010. 448-453.

Wasserman, S. & Faust, K. 1994. Social Network

Analysis: Methods and Applications (Structural

Analysis in the Social Sciences), Cambrige, Cambrige

University Press.

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

504