Domenico Rosaci
DIMET Department - University of Reggio Calabria
Via Graziella - Loc. Feo di Vito 89060 Reggio Calabria (Italy)
Intelligent Agents, B2C E-Commerce, Ontologies
This paper proposes a formal model of agent ontologies, suitable to represent the realities of both customers
and sellers in a B2C electronic commerce scenario. This model is capable of describing the entities involved
in the above realities (products, product features, product categories) as well as the behaviour of customers
and sellers in performing their activities. A system architecture, based on the presented ontology model, is
also briefly described.
In the last few years, many agent-based systems for
supporting business-to-customer (B2C) e-commerce
activities have been proposed. In this context, agents
can be seen as mediators between the actual involved
subjects, i.e. customers and businesses. Traditional
marketing research has developed many descriptive
theories and models that attempt to capture the Con-
sumer Buyer Behaviour (CBB) and that differ in var-
ious aspects; however, we can highlight the following
five relevant steps:
1. Need Identification. In this stage the consumer is
stimulated to become aware of some unmet need.
For instance, consider the case of a customer inter-
ested in a certain category of books. Agents can
continuously monitor the Web and advert the cus-
tomer when a new book of that category is avail-
2. Product Brokering. Once a consumer has identi-
fied a need to satisfy, he has to find what to buy
through a careful evaluation of the various prod-
ucts possibly satisfying that need. This requires a
comparison of product alternatives based on some
consumer-provided criteria. At the end of this step,
a set of products, usually called the consideration
set, capable of satisfying the consumer desires, has
been identified.
3. Buyer Formation Coalition. The customers, after
having chosen the product to buy in the product
brokering stage, before choosing the most suitable
merchant in the merchant brokering stage, may in-
teract with other (similar) buyers to form a buyer
coalition, in order to approach the merchant with a
large order and thus obtain a leverage.
4. Merchant Brokering. In this step, the consideration
set is combined with merchant-specific alternatives
based on consumer-selected criteria (e.g, availabil-
ity, price, delivery time, warranty, reputation, etc.)
for helping the consumer to determine whom to buy
5. Negotiation. During this step, the various terms of
the transaction as, for instance, the price, are de-
termined. The benefit of negotiating the price of
a product, instead of fixing it, is that negotiation
relieves the merchant from needing to determine
the value of the good a priori (Maes et al., 1999).
Rather, the price is dynamically determined by the
Several agent-based systems have been proposed
for mediating the above activities. For a detailed sur-
vey of these systems, see (He et al., 2003). How-
ever, none of the existing approaches supports in a
unified manner all the described stages, allowing cus-
tomers and sellers to interact in a virtual marketplace
by using a unique, integrated, framework. This paper
aims to propose such a unified approach, by defin-
ing a formal ontology model which both the customer
and seller realities are represented in, and that can be
fruitfully exploited in all the CBB stages. Such an
Rosaci D. (2004).
In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 3-9
DOI: 10.5220/0002619000030009
ontology provides a flexible way to access customer
and seller information, since it contains both an in-
tensional representation of the above realities (i.e., a
meta-schema), as well as an extensional representa-
tion that allows, when it is necessary, to handle the
actual data. Furthermore, the ontology represents cus-
tomer and seller behaviour (e.g., negotiation strategy)
by using logic propositions able of dynamically acti-
vating some pre-defined agent’s actions.
The paper is structured as follows: in the Section
2, we deal with some related work; in the Section 3,
we give a formal description of our ontology model.
As an example of application of this model, in the
Section 4, an architecture of a system supporting a
virtual marketplace is also briefly described. Finally,
in the Section 5, we draw our conclusions.
The necessity of representing, in the profile of a
customer, not only concepts of his interest but also
his behaviour in accessing those concepts, has been
considered in several works in the Information Sys-
tems field as, for instance, in (Bergamaschi et al.,
2001; Terracina and Ursino, 2000; Buccafurri et al.,
2002a). As far as the e-commerce research is con-
cerned, MOMIS (Mediator envirOnment for Multiple
Information Sources) (Bergamaschi et al., 2001) han-
dles both integration and querying of multiple, het-
erogeneous information sources, storing both struc-
tured and semi-structured data. Data source inte-
gration is carried out by following a semantic ap-
proach based on Description Logics, clustering and
a common data model capable of representing all in-
volved data sources. In (Buccafurri et al., 2002b;
Rosaci et al., 2002), a multi-agent system for repre-
senting and handling e-commerce activities is pro-
posed. In such an approach, an agent is present
in each e-commerce site, handling the information
stored therein. Furthermore, an agent is associated
with each customer, handling his profile. The infor-
mation associated with both sites and customer pro-
files is represented and handled using a particular con-
ceptual model called B-SDR network. This latter al-
lows to uniformly manage heterogeneous data sources
and to construct and maintain a profile storing infor-
mation about the visits the customer carries out to the
various e-commerce sites. MORPHEUS (Yang et al.,
2001) is a comparison-shopping agent that automat-
ically collects product descriptions from a group of
on-line stores on user’s behalf. Since the Web stores
are heterogeneous, a wrapper must be built and main-
tained for each store. Ontologies, i.e. intensional
descriptions of product characteristics and customer
and seller behaviour, have been already exploited in
B2C e-commerce context (Omelayenko, 2001) and
also many ontology-based approaches have been pro-
posed in multi-agent systems field (OAS 2002, 2002).
All the previously described approaches try to
solve the heterogeneity by adopting techniques based
on the integration of the various sources or on the use
of wrappers. They are very interesting from the view-
point of a user that want to consider the whole Web for
searching goods of his interest. However, the price to
pay for obtaining the integration of a potentially over-
whelming amount of Web sources is often high. In-
deed, integration techniques are onerous in terms of
time to spend for constructing the integrated global
representation of the various sources. Moreover, such
a global representation is often very difficult to han-
dle, since it has large dimensions and needs a con-
tinuous pruning of the less important concepts. On
the contrary, the approach we propose in this paper
is based on the construction of virtual marketplaces
whose actors (sellers and customers) are represented
in a uniform manner, due to the use of agents that op-
erate as a personal assistant and translate the user in-
terests and preferences in a pre-defined standard, rep-
resented by the agent ontology model that we here
The ontology of an agent is a representation of the re-
ality of the agent’s owner. In a B2C e-commerce con-
text, such a owner can be either a customer or a seller.
In the first case, the agent ontology has to store the
customer interests and preferences w.r.t. the virtual
marketplace and the customer behaviour in purchas-
ing goods (e.g., the way of visiting e-commerce sites,
the strategies in negotiating, etc.). In the second case,
the agent ontology has to represent the product cate-
gories of the seller, the product characteristics (price,
availability, etc.) and the service features (warranties,
time delivery, etc.).
In this section, we propose a formal definition of
an agent ontology. In order to better explain the vari-
ous concepts we introduce below, we propose a sim-
ple situation of a customer and a seller that interact in
a virtual marketplace. This situation will serve as a
leading example along this paper.
Example 1 Let John be a customer interested in
purchasing books and CDs. His interest in books
mainly concerns with narrative and poetry. In the
past, he purchased on the Internet the books Anna
Karenina, The Buddenbrok, Les Fleurs du mal and
La Divina Commedia. He also purchased the CD The
Ghost of Tom Joad. In the case of a non negotiable
price, John usually behaves as follows: (i) when he
purchases a book, he also purchases a CD; (ii) more-
over, he considers a book (resp., a CD) as an inter-
esting book (resp., an interesting CD) if the price is
smaller than 20 US$ and the delivery time is smaller
than 3 days. In the case of a negotiable price, suppose
John negotiates with a seller for a good that has a
base proposed price equal to p(0) at the step 0 of the
negotiation. The Johns behaviour is as follows. At
the step 0, John makes an offer o(0) = 0.8p(0). At
the step i of the negotiation (i = 1, 2...), in response
to a new proposed price p(i) of the seller, John offers
a value o(i) = (p(i) + o(i 1))/2.
Let W ord&Music a seller of books and CDs. It
deals with three categories of books, namely narra-
tive,essay and poetry. This seller, in a bilateral ne-
gotiation with a customer, behaves as follows. Let
p(0) be the base proposed price of a book, that it pro-
poses at the step 0 of a negotiation with a customer.
Suppose it receives an offer o(i), at the step i of the
negotiation (i = 0, 1, ....), by the customer. If o(i)
is smaller than 0.7 p(i), the seller aborts the nego-
tiation. Otherwise, it proposes a new price equal to
p(i + 1) = (o(i) + p(i))/2.
From this simple situation, we can observe that dif-
ferent kinds of knowledge need to be represented in
the ontologies of the customer and the seller. Namely:
Entities. In both the cases of the customer and the
seller in the example above, the agent ontology has
to represent some products relating to the agent’s
owner. Each product must have an ID that iden-
tifies it, and a set of associated features that gives
some information about it. For instance, a book
may have a feature title, a feature author, a feature
delivery time and a feature price. Such a represen-
tation of books is an intensional entity (a metadata)
and each actual book can be viewed as an instance
of this entity (a data). For example, the book Anna
Karenina may be represented by an entity instance
with ID=1, title =“Anna Karenina”, author =“Tol-
stoj”, delivery time =2 and price =17. More for-
Definition 1 Let T be a set {String,Integer,Real...}
of data types. We define the feature set, denoted by
F , as a set of variable names, each variable hav-
ing a type that belongs to T . A feature is an ele-
ment f F . Let D be a function from F to T ,
that associates to each feature f F its data type
D(f) T . An instance of the feature f is a value
v D(f).
Definition 2 Let f, g F be two features with
D(f) = D(g) and let k D(f) be a constant.
With the notation f = g we mean that the value of
f is set equal to the value of g, and with the nota-
tion f = k we mean that the value f is set equal to
the value k.
This definition can be applied, besides to prod-
uct characteristics, also to each object belonging
to the customer’s (resp., the seller’s) world as, for
instance, proposed price and offer that may be
represented by the features proposed price and
off er, respectively. In sum, features can be ex-
ploited both in the definition of products than in the
definition of more general entities. For instance, an
entity negotiation may represent the behaviour of
both John and W ord&Music in negotiating, and
proposed price and offer may be the features of
this entity. We give below a formal definition of
Definition 3 The entity domain E is the set of all
the tuples hID, f
, f
, ..., f
i, where ID is an In-
teger variable and f
, f
, ..., f
F . An entity is
an element of E.
Definition 4 Let e = hID, f
, f
, ..., f
i be an
entity. An instance of e is a tuple i =
hidv, f v
, f v
, ..., fv
i, where idv Integer and
), f v
), ..., fv
Categories. A category is a collection of enti-
ties. For example, in the ontology of the seller
W ord&Music, the category BOOKS may group
all the books available to be purchased. More-
over, a category can be organized in some sub-
categories. For instance, in our leading example,
the category BOOKS of John contains the sub-
categories P OETRY and NARRAT IV E. Thus,
generally, if we consider a set of entities as a limit-
case of category (i.e., a category that does not con-
tains any sub-categories), we can inductively say
that a category is either a set of entities or a set of
sub-categories. More formally:
Definition 5 The category domain C is the set of
all the tuples
hID, e
, e
, ..., e
i, where ID is an Integer vari-
able and e
, e
, ..., e
E, and of all the tuples
hID, c
, c
, ..., c
i, where ID is an integer and
c1, c2, ..., c
C. A category is an element of C.
In the Figure 1, all the categories, instances and fea-
tures involved in the John
s reality are described.
Note that we have inserted the entity N egotiation
in a particular category BEHAV IOUR.
Categories are intensional information. An in-
stance of a category is a set of instances of all the
entities belonging to it. We can inductively define
a category instance as follows:
Definition 6 Let c = hID, e
, e
, ..., e
i be a
category, where ID is an Integer variable and
, e
, ..., e
E. An instance of c is a tuple
i = hidv, ei
, ei
, ..., ei
i, where idv Integer
and ei
, ei
, ..., ei
Integer are identifiers of
entity instances.
NARRATIVE=hID, Book, Booki;
POETRY=hID, Book, Booki;
CDS=hID, Cdi;
BEHAVIOUR=hID, Negotiationi;
Negotiation=hID, proposed
price, offeri;
Book=hID, title, author, delivery time, pricei;
Cd=hID, title, author, delivery
time, pricei;
title, author=String;
delivery time, price, proposed price,offer=Integer;
Figure 1: The categories, entities and features of the customer J ohn
Let c = hID, c
, c
, ..., c
i, where ID is an integer
and c1, c2, ..., c
C. An instance of c is a tuple
i = hidv, ci
, ci
, ..., ci
i, where idv Integer
and ci
, ci
, ..., ci
Integer are identifiers of
category instances.
In the Figure 2, all the category instances involved
in the John
s reality are described.
This figure represents the following information:
The category instance JOHN1 (representing all the
interests of the customer John) has ID=5 and
contains the category instance with ID=4 (i.e.,
BOOKS1), the category instance with ID=1 (i.e.,
CDS1) and the category instance with ID=6 (i.e.,
BEHAVIOUR1). In its turn, the category instance
BOOKS1 has ID=4 and contains the category in-
stance with ID=3 (i.e., NARRATIVE1) and the cat-
egory instance with ID=2 (i.e., POETRY1). The
category instance CDS1 has ID=1 and does not
contain any category instances since, as specified
by the schema of its category CDS, it is com-
posed by an entity instance, namely that one hav-
ing ID=5 (i.e., Cd1). Similarly, the category in-
stance BEHAVIOUR1 has ID=6 and is composed
by the entity instance with ID=6 (i.e., Negotia-
tion1). The category instance NARRATIVE1 has
ID=3 and is composed by the entity instance with
ID=1 (i.e, Book1) and by the entity instance with
ID=2 (i.e., Book2). The category instance PO-
ETRY1 has ID=2 and is composed by the entity
instance with ID=3 (i.e, Book3) and by the entity
instance with ID=4 (i.e., Book4).
The entity instances with IDs=1..4 represent books.
The entity instance with ID=5 represents a CD and
the entity instance with ID=6 represents a step of a
Knowledge patterns. An ontology has to store,
besides information about the involved entities,
also other information relative to the behaviour of
the customer (resp.,the seller) in purchasing (resp.,
in selling) the products. In our leading example, the
negotiation behaviour of the customer John and
the seller W ord&Music are some examples of this
kind of knowledge that have to be represented.
Such a knowledge can be stored into an ontol-
ogy by considering a set of events, that rep-
resents actions belonging to the activity of the
customer (resp., the seller), that may happen or
not. For examples, in the leading example’s sit-
uations, the concepts of interesting book, in-
terestingCD, buy a book, buy a CD, make
an offer, propose a new price can be modeled
as events and represented by boolean variables
as interestingBook, interestingCD, buyBook,
buyCD, makeO, proposeP , respectively. More-
over, also a relational expression involving features
is an event as, for instance, (deliveryT ime < t)
or (price < p). An event can be thus represented
by a boolean expression.
Definition 7 An event is either: (i ) a boolean vari-
able or (ii ) an expression of the form b, where
a, b F , D(a) = D(b) and θ {=, <, , >, ≥}
is a relational operator or (iii ) an expression of the
form c, where a F and c D(a) and θ is a
relational operator. The events of the type (ii ) and
(iii ), that involve only features and do not involve
any boolean variable, are called feature-events.
The existing relationships between events can be
represented by propositional rules as, for instance,
relative to the situations described in our leading
example for the John customer:
: buyBook buyCD
: (price < 20), (time < 3) interestingBook
: (price < 20), (time < 3) interestingCD
: interestingBook, proposeP makeO
or, for the W ord&Music seller:
: makeO proposeP
JOHN1=h5, 4, 1, 6i;
BOOKS1=h4, 3, 2i;
NARRATIVE1=h3, 1, 2i;
POETRY1=h2, 3, 4i;
CDS1=h1, 5i;
BEHAVIOUR1=h6, 6i;
Negotiation1=h6, 5, 4i;
Book1=h1, AnnaKarenina
, T olstoj
, 17, 2i;
Book2=h2, T heBuddenbrook
, M ann
, 18, 2i;
Book3=h3, Lesf leursdumal
, Beaudelaire
, 15, 2i;
Book4=h4, LaDivinaCommedia
, Alighieri
, 25, 2i;
Cd1=h5, T heGhostof T omJoad
, Springsteen
, 18, 2i;
Figure 2: The category instances and the entity instances of the customer J ohn
These rules, that we call knowledge patterns, affirm
that some events happen when other related events
happen. For instance, the rule k
affirms that if the
event buyBook has the value true, also the event
BuyCD has the value true.
More formally:
Definition 8 A knowledge pattern k
is a propositional rule of the form
, a
, ..., a
, ¯a
, ¯a
, ..., ¯a
b , where a
, a
, a
, a
, ..., a
, b are events. This nota-
tion means that, if both a
, a
, ..., a
assume at the
same time the value true, and ¯a
, ¯a
, ..., ¯a
assume at the same time the value false, then b
assume the value true. Let fs = {f
, f
, ..., f
be a set of features. We say that k is a knowledge
pattern on fs, if each feature of f s appears in
almost one of the events a
, a
, ..., a
contained in
k, and all the events a
, a
, ...a
are feature-events.
Actions. Often, when an event happens in the
world of a customer or a seller, an action is conse-
quently produced. For instance, in our leading ex-
ample relative to the customer John, when John
decides to make an offer in a negotiation phase, the
value of the offer is equal to the mean between
his previous offer and the price proposed at the
present by the seller. We can thus say that, when
the event makeO becomes true, a program, that we
denote by of, is called that sets the value of the fea-
ture offer equal to (proposed
price + off er)/2,
where proposed price is the feature representing
the price proposed by the seller. Similarly, in the
case of the W ord&Music seller, we can say that,
when the event proposeP becomes true, a pro-
gram pf is activated that behaves as follows: if
off er < 0.7 proposed price, it sets the event
end to the value true and then terminates, other-
wise it sets the value of proposed price equal to
(proposed price + off er)/2. In this latter case,
we observe that the program modifies both a fea-
ture value and an event value.
Whe call action a 5-tuple composed by an event, as
makeO, a program, as co, that is activated by the
event, a set of features, as {proposed price}, that
are the arguments of the program, and another two
sets of features and events, as {offer} and {end},
respectively, whose value is modified by the pro-
gram. More formally:
Definition 9 An action is a tuple
he, P, fs1, es, fs2i where e E, P is a pro-
gram, fs1, fs2 2
, es 2
, such that the
program P is activated if e = true by passing it as
input arguments the features belonging to the set
fs1 and P modifies both the value of the features
belonging to the set fs2 and the events belonging
to the set es.
In our leading example, we can define the actions
J ohn
= hmakeO, of, {proposed price}, {},
, {of fer}i
W ord&M usic
= hproposeP, pf, {of fer}, {end},
, {proposed price}i.
Now, we can give our definition of agent ontology.
This is a collection of four sets: a set of entities, rep-
resenting all the products which the customer is in-
terested in (resp., which are in the seller’s catalog)
and all the other entities belonging to the customer
(resp., the seller) activities; a set of categories, de-
scribing the hierarchical structure of the entities; a set
of knowledge patterns, describing the rules followed
by the customer in purchasing (resp., followed by the
seller in selling) products and a set of actions, speci-
fying what actions the customer (resp., the seller) per-
forms when the environment changes. Since all these
sets contain only intensional information, such an on-
tology is purely intensional and can be viewed as a
schema. We also define the instance of an ontology,
that contains, for each entity (resp., category) of the
ontology, also a set of instances of that entity (resp.,
category). More formally:
Definition 10 An agent ontology schema is a 4-tuple
hE, C, K, Ai, where: (i) E is a set of entities; (ii) C
is a set of categories; (iii) K is a set of knowledge
patterns; (iv) A is a set of actions.
Definition 11 An ontology instance of an ontology
O=hE, C, K, Ai is a 4-tuple hEP, CP, K, Ai, where:
(i) EP is a set of of pairs (e, ei), such that e E is an
entity and ei is a set of instances of e; (ii) CP is a set
of of pairs (c, ci), where c C is a category and ci
is a set of instances of c; (iii) K is a set of knowledge
patterns; (iv) A is a set of actions.
As an example, the ontology of the customer John
is shown in the Figure 3. An instance of this ontology
is shown in the Figure 4.
The OBA-B2C system allows to realize a virtual mar-
ketplace. It is composed by a central unit, called
agency, and several agents connected to the agency.
There are two agent typologies in the system, namely
the customer agents and the seller agents. On the
one hand, each customer agent is associated with a
real customer, and operates on his behalf in order to
automatically carry out the various stages of the e-
commerce. On the other hand, each seller agent is
associated to a real seller and performs e-commerce
activities on the seller’s behalf.
Each agent has to be registered on the agency in
order to be enabled to participate to the virtual mar-
ketplace. The agency is a Web site that provides sev-
eral services to the actors of the virtual marketplace.
The registration of an agent consists in the assignment
of two access codes (an agent name and a password)
to the agent. Each registered agent can enter into
the marketplace by accessing the agency site and au-
thenticating itself by means of the above codes. The
agency maintains a list of all the registered agents, and
is able to provide information about them. By means
of the agency, an agent of the virtual marketplace can
find those agents that have similar interests, in order
to form a coalition of buyers. It is worth pointing out
that the agency does not make public the information
about the registered agents, in order to protect cus-
tomer privacy. Thus, the agency behaves as a media-
tor that supports the agent communication. As an ex-
ample, an agent a that desires to know the agents that
have similar preferences, send to the agency those in-
formation about itself (personal preferences, buying
behaviour, etc.) it wants to make public. Automati-
cally, the agency contacts all the agents of the market-
place, sends them the above information, receives the
answers by the contacted agents and finally transmits
these answers to a. Agency also mediates virtual auc-
tions, by allowing the seller agents of the marketplace
to create auctions for selling goods and the customer
agents to bid offers for desired goods.
In the client-agent architecture of the OBA-B2C
system, the customer agent is a client that a real cus-
tomer exploits for participating to the virtual market-
place. This agent (see Figure 5) is composed by two
knowledge bases called ontology and address book,
and by two main programs, called ontology manager
and communication manager, respectively. The core
of the agent architecture is the ontology, that has the
structure defined in the Section 3. The ontology is
handled by the ontology manager, that exploits the
information contained in it for supporting the various
stages of the e-commerce. For this reason, the ontol-
ogy manager is composed by five program modules
that mediate, respectively, need identification, prod-
uct brokering, buyer coalition formation, merchant
brokering and negotiation. Each module performs, on
the behalf of the customer, the associated activity. For
instance, the merchant brokering manager exploits the
information contained in the customer ontology rela-
tive to the products of interest, and finds in the vir-
tual marketplace the best merchants for those prod-
ucts. Each program module also monitors the cus-
tomer behaviour in the associated activity. All the
program modules of the ontology manager need to
contact the agents of the marketplace for performing
their tasks. For instance, the buyer coalition forma-
tion has to contact all the agents of the marketplace
in order to find possible collaborators. The commu-
nication among the agents is handled by a component
called communicator manager . This module exploits
a database of agent addresses, called address book,
for performing its tasks.
The seller agent is a client software that a real seller
can exploit for participating in the virtual market-
place. This agent is composed by an ontology, an ad-
dress book and tree main programs, called ontology
manager, site builder and communication manager,
respectively. Similarly to the customer agent, the core
of the seller agent architecture is the ontology that,
in this case, is not built by observing the owner be-
haviour but, obviously, the seller itself is able to build
it by specifying the categories of interests and the
products to sell. This activity is performed by a dedi-
cated component called site builder. The site builder
operates as a seller assistant, allowing the seller to
build his Web store by using a uniform representa-
tion w.r.t. the virtual marketplace. For instance, if the
seller tries to insert in his ontology a product (resp., a
product category) that has the same characteristics of
a product (resp., a product category) always present in
the ontology of another seller of the marketplace, the
site builder identifies the product(resp., the product
category) in a unique manner, by assigning it a unique
identifier, and thus avoiding heterogeneous represen-
tations of the same good.
The ontology manager and the communication
manager have the same role than in the customer
agent architecture. Obviously, the ve modules that
support the CBB stages operate in this case on the
seller’s viewpoint.
, k
, k
, k
J ohn
Figure 3: The J ohns ontology
E={(Book,{Book1,Book2,Book3,Book4}), (Cd,Cd1),(Negotiation,Negotiation1)}
, k
, k
, k
J ohn
Figure 4: An instance of the J ohns ontology
Figure 5: The customer agent’s architecture.
This paper describes a new model for representing
agent ontologies in a B2C e-commerce scenario, suit-
able to support the various stages of the Consumer
Buyer Behaviour (CBB) model. The proposed model
is capable of representing both the concepts involving
in customers and sellers realities as well as the be-
haviour of customers and sellers in performing their
activities. An architecture of a multi-agent system
based on the presented ontology model is also briefly
described. Our ongoing research mainly deals with
the definition of machine learning techniques for effi-
ciently extracting the knowledge patterns of the agent
ontologies by directly observing customer and seller
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