2434
Semantic Knowledge Transparency in E-Business Processes
managing the integration of value chain activities
over distributed and heterogeneous information
platforms such as the Internet, is a challenging
WDVN ZLWK ODUJH SRWHQWLDO EHQH¿WV $OWKRXJK
technical integration of systems is essential, a
FRPPRQODQJXDJHWRH[SUHVVFRQWH[WVSHFL¿F
constructs and relevant business rules to assist
autonomous system entities and decision makers
WRVROYHVSHFL¿FEXVLQHVVSUREOHPVLVHVVHQWLDO
(Stal, 2002). Disparate technical systems need
the ability to share data, information, and knowl-
edge. A common and shared understanding of
WKH GRPDLQVSHFL¿F FRQFHSWV DQG WKH UHODWLRQV
between them is critical for creating integrative
views of information and knowledge in e-business
processes. However, there is paucity in research
on distributed information and knowledge shar-
ing that provides a unifying process perspective
to share information and knowledge (Oh & Park,
2003) in a seamless manner.
The Semantic Web is a key component for
realizing the vision of semantic knowledge trans-
parency in e-business processes. The Semantic
Web provides the technical foundations to sup-
SRUWWKHWUDQVSDUHQWÀRZRIVHPDQWLFNQRZOHGJH
representation to automate, enhance, and coordi-
nate collaborative inter-organizational e-business
processes (Singh, Iyer, et al., 2005). The Semantic
Web vision comprises ontologies for common

semantics of representation and ways to interpret
ontology; knowledge representation for structured
collections of information and inference rules
for automated reasoning in a single system; and
intelligent agent to collect content from diverse
sources and exchange data enriched with seman-
tics (Berners-Lee, Hendler, & Lassila, 2001). This
vision provides the foundation for the semantic
framework proposed in this research.
This chapter is structured as follows. First,
we conduct a review and analysis of the relevant
literature in the areas of e-business, KM, and the
Semantic Web. Second, the conceptualization of
the e-business process universe of discourse and
its description logic are developed. Third, we use
an intelligent infomediary-based e-marketplace as
a scenario to illustrate how semantic knowledge
transparency can be used to achieve the coordi-
nation of activities and resources across inter-
organizational systems. Finally, future research
issues and conclusions are stated.
BACKGROUND
Based on existing research in e-business, KM
and the Semantic Web, an innovative approach
to achieve semantic knowledge transparency
is developed. We use a process perspective to
integrate knowledge of resources involved in a
process and process knowledge including process
PRGHOVDQGZRUNÀRZVXVHGLQSURFHVVDXWRPDWLRQ
In order to achieve semantic knowledge transpar-

ency, we develop theoretical conceptualizations
using ontological analysis that will be formalized
using DLs. The ontology will support a common
vocabulary for transparent knowledge exchange
among inter-organizational systems of business
p a r t n e r s o f a v a l u e c h a i n , s o t h a t s e m a n t i c i n t e r o p -
erability can be achieved. The foundations of the
proposed approach are conceptually represented
in Figure 1 and explanations follow.
E-Business
Electronic data interchange (EDI) is an informa-
tion technology that allows business partners to
send and receive commercial documents in an
electronic format (Hansen & Hill, 1989). Under
EDI proprietary value-added networks data dis-
closure and information transparency were not
a concern. Interestingly, EDI by itself does not
provide market transparency (Zhu, 2004).
Nowadays, businesses are moving from EDI
WR:HEEDVHGV\VWHPV,QIDFWPDQ\¿UPVKDYH
adopted e-business models to improve their
collaborative capabilities (Segars & Chatterjee,
2003). Regarding business processes, they are
typically modeled as deterministic, action-event
2435
Semantic Knowledge Transparency in E-Business Processes
V HTX H QF H VL Q ZR UN ÀR Z ED V H GL Q IRU P D W LR QV \ VW H P V 
DQG ZRUNÀRZ DXWRPDWLRQ V\VWHPV :RUNÀRZV
establish the logical order of execution between
individual task units that comprise intra-or-

ganizational and inter-organizational business
SURF HVVHV7 KH:RUN ÀRZ0D QDJH PHQW&RDO LW LRQ
(www.wfmc.org) describes business process as
³DVHTXHQFHRIDFWLYLWLHVZLWKGLVWLQFWLQSXWVDQG
outputs and serves a meaningful purpose within
an organization or between organizations” (Dust-
dar, 2004 p. 460).
$ SURFHVV GH¿QLWLRQ LV WKH UHSUHVHQWDWLRQ
of a business process in a form which supports
automated manipulation, such as modeling, or
HQDFWPHQW E\D ZRUNÀRZPDQDJHPHQW V\VWHP
:I06 7KH SURFHVV GH¿QLWLRQ FRQVLVWV RI D
network of activities and their relationships, cri-
teria to indicate the start and the termination of
the process, and information about the individual
activities, including participants and data (www.
wfmc.org). Business processes can thus be gener-
DO L]HG DVKDY LQJD³ EHJL Q´DQGDQ³HQG´ SRL QWD QG
a series of intermediate tasks that are performed
in sequence on some entity, object, or activity.
In its simplest case, an e-business process may
KDYH HDFKZRUNÀRZ DFWLYLW\ SHUIRUPHG ZLWKLQ
a single organization; while in the most general
and extensible case, each individual activity may
be performed by a different partner organization.
0RVWLQWHURUJDQL]DWLRQDOZRUNÀRZVZRXOGIDOO
somewhere in between these end points.
Singh, Iyer, et al. (2005) explain that e-business
processes require transparent information and
semantic knowledge transparency among business

partners. The consequent lack of transparency in
L Q IR U P DWL RQ ÀRZ D F UR V VW K HYD OX HFK DL Q F RQ W L QX H V
to hinder productive and collaborative partner-
VKLSDPRQJ¿UPVLQe-marketplaces. Moreover,
the lack of transparency in business-to-business
(B2B) e-marketplaces increases the uncertainty
and perceived risks and hampers trusted relation-
ships among business partners.
E-Marketplace
The main roles of e-marketplace are: (1) discovery
– of buyers and suppliers that meet each other’s
requirements; (2) facilitation – of transactions to
HQDEOHLQIRUPDWLRQÀRZVOHDGLQJWRWKHÀRZRI
good and services among buyers and suppliers;
and (3) support – of decision process leading to
the development of collaborative relationships
Figure 1. Conceptual representation of semantic knowledge transparency and integration.
Semantic Web
Ontology,
Knowledge
Representation,
Intelligent Agents
e-Business
e-Marketplace
,
Infomediary
Knowledge Management
Organizational Knowledge,
Interorganizational Processes
Coordination

Semantic
Knowledge
Transparency
Semantic Web
Ontology,
Knowledge
Representation,
Intelligent Agents
e-Business
e-Marketplace
,
Infomediary
Knowledge Management
Organizational Knowledge,
Interorganizational Processes
Coordination
Semantic
Knowledge
Transparency
2436
Semantic Knowledge Transparency in E-Business Processes
between e-marketplace participants (Bakos,
1998). The value added to the process by the e-
marketplace is in providing information to buyers
and suppliers about each others’ capabilities and
requirements. E-marketplace is a mechanism to
VWUHDPOLQHLQIRUPDWLRQÀRZLQVXSSO\FKDLQDQG
re-balance the information asymmetry (Zhu,
2002). E-marketplaces offer value-added ser-
YLFHVE\OHYHUDJLQJLQGXVWU\VSHFL¿FNQRZOHGJH

through deciphering complex information and
contribute to transaction cost reduction. How-
ever, the lack of integration of information and
knowledge across the e-value chain continues to
hinder productive and collaborative partnerships
DPRQJ¿UPVLQHPDUNHWSODFHV
Infomediary
In e-marketplaces a new kind of intermediaries
has emerged: Infomediary. Grover and Teng (2001)
GH¿QHLQIRPHGLDU\DV³HFRPPHUFHFRPSDQLHV
leveraging the [power of] the Internet to unite
EX\HUVDQGVXSSOLHUVLQDVLQJOHHI¿FLHQWYLUWXDO
marketspace to facilitate the consummation of a
transaction” (p. 79). In this chapter, we argue that
in the context of e-marketplaces, intermediaries
have evolved into infomediaries that add value
to their stakeholders by deciphering complex
product information and matching buyers’ needs
with sellers’ products and/or services. Grover and
Teng focus on the critical information-providing
role of the market and identify the roles played
by electronic intermediaries, or infomediaries.
An infomediary is an emergent business model
adopted by organizations in response to the
enormous increase in the volume of information
available and the critical role of information in
enabling processes in electronic markets. Info-
mediaries perform an indispensable function by
matching buyers’ needs with suppliers’ products
and services to facilitate transactions. There is a

wealth of market information exchanged through
the infomediaries as they perform these functions.
As a result, Infomediaries become vital resources
of knowledge about the nature of exchanges in
the e-marketplace.
A n a n a l y s i s o f t h e i n f o m e d i a r y b u s i n e s s m o d e l
shows that individual buyers and suppliers seek
distinct goal-oriented information capabilities
from the infomediary—they provide decision
parameters through their individual demand or
supply functions. This is essentially a discovery
activity with buyers and suppliers searching for a
match of their requirements through infomediar-
LHV7KLVGLVFRYHU\SURFHVVLVLQÀXHQFHGE\KLV-
torical information including the past experiences
of other buyers’ reliability and trustworthiness
of the supplier. The infomediary business model
can provide valuable information to this deci-
sion process through its role as the repository of
experiential knowledge of transactional histories
for both buyers and suppliers. This information
can be used to develop knowledge that informs
discovery of buyers and suppliers for subsequent
transactions. A realization of the need for greater
collaboration among trading partners is fueling
the growth of KM to help identify integrative and
interrelated elements to enable collaborations.
Knowledge Management
.0FDQEHGH¿QHGDV³DSURFHVVWKDWKHOSVRU-
JDQL]DWLRQV ¿QG VHOHFW RUJDQL]H GLVVHPLQDWH

and transfer important information and expertise
necessary for activities such as problem solving,
dynamic learning, strategic planning, and deci-
sion making” (Gupta, Iyer, & Aronson, 2000,
S.0LQFOXGLQJWKHFRGL¿FDWLRQVWRUDJH
retrieval, and sharing of knowledge, transpires in
WKHFRQWH[WRIDSURFHVVVFLHQWL¿FJRYHUQPHQWDO
or commercial. Explicit knowledge, declarative
enough to be represented using standards-based
knowledge representation (KR) languages allows
for knowledge to be interpreted by software and
shared using automated reasoning mechanisms
to reach useful inferences. While all knowledge
cannot be explicated and be effectively represented
and reasoned with using decidable and complete
2437
Semantic Knowledge Transparency in E-Business Processes
computational techniques; it is useful to focus on
explicit, declarative KR using computationally
feasible KR languages to build effective and useful
NQRZOHGJHEDVHGV\VWHPV+DPHOLGHQWL¿HV
that knowledge transparency is directly related to
HDVHRIWUDQVIHU,QOLQHZLWKWKHQRWLRQRI¿UPVDV
repositories of productive knowledge (Demsetz,
1998), where knowledge resources are primary
concern, managing cooperative relationships is
I U H TX H QW O\D SUR F HV V RI P D Q DJ L Q JN QR ZOH G JH ÀRZ V 
(Badaracco, 1991).
Furthermore, transparency is critical to busi-
ness partnerships, lowering transaction costs

EHWZHHQ¿UPVDQGHQDEOLQJFROODERUDWLYHFRP-
merce (Tapscott & Ticoll, 2003). We focus on two
VSHFL¿FW\SHVRINQRZOHGJHLQWKLVUHVHDUFK
1. Component knowledge: Component
knowledge includes descriptions of skills,
technologies, tangible and intangible
resources and is amenable to knowledge
exchange (Hamel, 1991; Tallman, Jenkins,
Henry, & Pinch, 2004).
2. Process knowledge: Process knowledge is
typically embedded in the process models
RIZRUNÀRZPDQDJHPHQWV\VWHPVRUH[LVWV
as coordination knowledge among human
agents to coordinate complex processes.
Component and process knowledge are central
to activities of human and software agents in
inter-organizational e-business processes; there-
fore, the standard representation of both type of
knowledge is fundamental to achieve semantic
knowledge transparency. Newell (1982) regards
NQRZOHGJHDV³ZKDWHYHUFDQEHDVFULEHGWRDQ
agent, such that its behavior can be computed
according to the principle of rationality” (p. 105).
7KLVGH¿QLWLRQIRUPVDEDVLVIRUIXQFWLRQDO.0
using agents, human, and software when using
explicit, declarative knowledge that is represented
using standards-based knowledge representation
languages that can be processed using reasoning
mechanisms to reach useful inferences.
Inter-Organizational Process

Coordination
Inter-organizational processes allow collaborating
organizations to provide complementary services
through networks of collaborating organizations
(Dyer, 2000; Sawhney & Parikh, 2001). Here,
WKHUHVRXUFHEDVHGYLHZRI¿UPVZLWK IRFXVHG
capabilities is replaced by a network of organi-
zations with a focal enterprise that coordinates
resources of collaborating organizations to
execute processes (Sawhney & Parikh, 2001).
Complexities of coordinating inter-organizational
processes require knowledge-driven coordination
structures to determine decision authority and
knowledge sources (Anand & Mendelson, 1997).
The knowledge-integrated system incorporates
the coordination mechanism and offers authorized
resource matching in processes. Processes are
decomposed into activities organized by gener-
alization-specialization hierarchies and require
coordination mechanisms to manage dependen-
cies (Malone & Crowston, 1994). Coordination
RIDFWLYLWLHVLVHPEHGGHGLQSURFHVVZRUNÀRZV
and WfMS since they essentially deal with issues
of task-task and task-resource dependencies and
their coordination (Kishore, Sharman, Zhang,
& Ramesh, 2004). Coordination constructs used
in this proposed research are based on Malone,
Crowston, and Herman (2003) and are similar to
those in Van der Aalst and Kumar (2003).
The complexity of coordinating e-business

processes and the increasing demand by customers
for complete solutions over single products re-
quires knowledge-driven coordination to provide
intelligent support to determine decision authority
and knowledge sources in a value network. Al-
liances are seldom forged to co-produce single
products; they increasingly entail developing com-
plex systems and solutions that require resources
of multiple partners (Doz & Hamel, 1998). This
requires integrative architecture with reasoning
ability using knowledge about business processes
within a value network. The integrated informa-
2438
Semantic Knowledge Transparency in E-Business Processes
tion system as an integ ral par t of t he coord ination
structure can offer enhanced matchmaking of
resources and coordination of activities to allow
the value network to respond to dynamic customer
GHPDQGHI¿FLHQWO\DQGHIIHFWLYHO\$VRUJDQL]D-
tions become increasingly global and distributed
in nature, their reliance on inter-organizational
LQIRUPDWLRQÀRZVZLWKSDUWQHURUJDQL]DWLRQVLV
integral to e-business processes.
Integrating knowledge resources across col-
laborating organizations requires knowledge
transparency for global, inter-organizational,
access to knowledge resources. Here, semantic
knowledge transparency refers to the dynamic
RQGHPDQG DQG VHDPOHVV ÀRZ RI UHOHYDQW DQG
unambiguous, machine-interpretable knowledge

resources within organizations and across in-
ter-organizational systems of business partners
engaged in collaborative processes. A process
view of knowledge integration incorporates man-
agement of component knowledge and process
knowledge for integrated inter-organizational
systems that exhibit knowledge transparency.
The effective standardizations and adaptability
afforded by integrative technologies that support
the transparent exchange of information and
knowledge make inter-organizational e-business
relationships viable.
Semantic Web
Another theoretical foundation of the semantic
knowledge transparency in e-business processes
is the concept of the Semantic Web. The Semantic
Web is an extension of the current Web in which
LQIRUPDWLRQLVJLYHQ³ZHOOGH¿QHGPHDQLQJ´WR
DOORZPDFKLQHVWR³SURFHVVDQGXQGHUVWDQG´WKH
information presented to them (Berners-Lee et
al., 2001, p. 35). According to Berners-Lee, the
Semantic Web comprises and requires knowledge
representation, ontologies, and agents in order to
function (Figure 2 shows the different layers of
the Semantic Web architecture):
• Knowledge representation:Structured col-
lections of information and sets of inference
rules that can be used to conduct automated
reasoning. Knowledge representations must
be linked into a single system.

• Ontologies: Systems must have a way to
discover common meanings for entity rep-
resentations. In philosophy, ontology is a
theory about the nature of existence; in sys-
tems, ontology is a document that formally
GHVFULEHVFODVVHVRIREMHFWVDQGGH¿QHVWKH
relationship among them. In addition, we
need ways to interpret ontology.
• Agents: Programs that collect content from
diverse sources and exchange the result with
RWKHUSURJUDPV$JHQWVH[FKDQJH³GDWDHQ-
riched with semantics.” Intelligent software
agents can reach a shared understanding
by exchanging ontologies that provide the
vocabulary needed for discussion. Agents
FDQHYHQ³ERRWVWUDS´ new reasoning capa-
Figure 2. Semantic Web representation layers (Berners-Lee et al., 2001)
2439
Semantic Knowledge Transparency in E-Business Processes
bilities when they discover new ontologies.
Semantics makes it easier to take advantage
of a service that only partially matches a
request. (Lee et al. 2001, p. 37)
Given the importance of the Semantic Web
components to achieve processes integration and
automation, we analyze in more detail relevant
work in the areas of ontologies, DLs, and intel-
ligent agents in the next two subsections.
Ontologies and Description Logics
Ontologies provide a shared and common un-

GHUVWDQGLQJ RI VSHFL¿F GRPDLQV WKDW FDQ EH
communicated between disparate application
systems, and therein provide a means to integrate
the knowledge used by online processes employed
by organizations (Klein, Fensel, Van Harmelen, &
Horrocks, 2001). Staab, Studer, Schnurr, and Sure
(2001) describe an approach for ontology-based
KM through the concept of knowledge metadata,
which contains two distinct forms of ontologies
that describe the structure of the data itself and
issues related to the content of data. Jasper and
Uschold (1999) identify that ontologies can be
used for: (1) knowledge reuse; (2) knowledge
VSHFL¿FDWLRQ  FRPPRQ DFFHVV RI KHWHURJH-
neous information; and (4) search mechanisms.
We refer the reader to Kishore et al. (2004) for
a more comprehensive discussion of ontologies
and information systems.
Ontology documents can be created using
Foundation of Intelligent Physical Agents (FIPA)-
compliant content languages like business process
execution language (BPEL), resource descrip-
tion framework (RDF), Web ontology language
(OWL), and DARPA agent markup language
(DAML) to generate standardized representations
of the process knowledge.
The structure of ontology documents will be
based on DLs. DLs are logical formalisms for
knowledge representation (Gomez-Perez, Fer-
nandez-Lopez, & Corcho, 2004; Li & Horrocks,

2004). DLs are divided into two parts: (1) TBox,
which contains intentional knowledge in the form
of a terminology and is built through declarations
that describe general properties of concepts; and
(2) ABox, which contains extensional knowledge,
ZKLFKLVVSHFL¿HGE\WKHLQGLYLGXDORIWKHGLV-
c o u r s e d o m a i n ( B a a d e r , C a l v a n e s e , M c G u i n n e s s ,
Nardi, & Patel-Schneider, 2003; Gomez-Perez et
al., 2004).
I n t h i s s t u d y, w e a d o p t t h e S H I Q D L s p r e s e n t e d
b y L i a n d H o r r o c k s ( 2 0 0 4 ) . T h e y a r g u e t h a t S H I Q’s
expressive power made it to be equivalent to
DAML+Ontology Inference Layer (OIL). In addi-
tion, OWL is based on the SH family of descrip-
tion logics which supports Boolean connectives,
including intersection, union, and complements,
restrictions on properties transitive relationships
and relationship hierarchies. Standardized by the
World Wide Web Consortium (W3C), OWL is
the leading approach to Semantic Web ontologies
using DL as its fundamental KR mechanism.
Ontological analysis results in ontology descrip-
tions that are presented formally through DL for
theoretical soundness; and in machine-readable
format using OWL and OWL-DL to provide
practicality for the model. In addition, software
reasoners, such as Racer, support concept con-
sistency checking, TBox reasoning, and ABox
reasoning on models developed using SHIQ-DL
translated into OWL-DL. These provide the basis

for semantic knowledge transparency to support
the e-business processes.
Intelligent Agents
$QLQWHOOLJHQWDJHQWLV³DFRPSXWHUV\VWHPVLWX-
ated in some environment and that is capable of
ÀH[LEOHDXWRQRPRXVDFWLRQLQWKLVHQYLURQPHQW
in order to meet its design objectives” (Jennings
& Wooldridge, 1998, p. 8). The agent paradigm
can support a range of decision-making activity
including information retrieval; generation of
alternatives; preference order ranking of options
and alternatives; and supporting analysis of the
2440
Semantic Knowledge Transparency in E-Business Processes
DOWHUQDWLYHJRDO UHODWLRQVKLSV 7KH VSHFL¿F DX-
tonomous behavior expected of intelligent agents
depends on the concrete application domain and
the expected role and impact of intelligent agents
on the potential solution for a particular problem
for which the agents are designed to provide cog-
nitive support. Criteria for application of agent
technology require that the application domain
should show natural distributivity with autono-
mous entities that are geographically distributed
and work with distributed data; require ÀH[LEOH
interaction without a priori assignment of tasks
to actors; and be embedded in a dynamic environ-
ment (Muller, 1997). Papazoglou (2001) provides
a complete discussion of the use of intelligent
agents to support e-business.

A fundamental implication is that knowledge
must be available in formats that allow for process-
ing by software agents. Intelligent agents can be
u s e d f o r K M t o s u p p o r t s e m a n t i c e - b u s i n e s s a c t i v i -
ties. The agent abstraction is created by extending
an object with additional features for encapsula-
tion and exchange of knowledge between agents
to allow agents to deliver knowledge to users and
support decision-making activity (Shoham, 1993).
Agents work on a distributed platform and enable
the transfer of knowledge by exposing their public
methods as Web services using Simple Object
Access Protocol (SOAP) and Extensible Markup
L a n g u a g e ( X M L). I n t h i s r e s p e c t , t h e i n t e r a c t i o n s
among the agents are modeled as collaborative
interactions, where the agents in the multi-agent
community work together to provide decision
support and knowledge-based explanations of the
decision problem domain to the user.
A recent extension of the Semantic Web is the
vision of semantic e-business. Singh, Iyer, et al.
GH¿QHVHPDQWLFHEXVLQHVV DV³DQDSSURDFK
to managing knowledge for coordination of e-busi-
ness processes through the systematic application
of Semantic Web Technologies” (p. 20). Semantic
e-business leverages Semantic Web technologies
DQGFRQFHSWVWRVXSSRUWWKHWUDQVSDUHQWÀRZRI
semantically enriched information and knowledge
and enables collaborative e-business processes
within and across organizational boundaries.

In addition, the Semantic Web aids intelligent
agents to organize, store, retrieve, search, and
match information and knowledge for effective
collaboration among semantic e-business par-
ticipants. It has been recognized that candidates
for applications of semantic e-business include
supply chain management and e-marketplaces
(Singh, Iyer, et al., 2005). In this study, we apply
the vision of semantic e-business in conjunction
with the other theoretical foundations to explain
how semantic knowledge transparency can be
achieved in the context of intelligent, infomedi-
ary-based e-marketplace.
CONCEPTUALIZATION OF THE
E-BUSINESS PROCESS UNIVERSE
OF DISCOURSE
2QWRORJ\ UHSUHVHQWV VWUXFWXUHG DQG FRGL¿HG
knowledge of the conceptualizations, including
concepts, relationships, and constraints, for a
domain of interest (Kishore et al., 2004). Fox,
Barbuceanu, Gruninger, and Lin (1998) explain
that organizations are a set of constraints on the
activities performed by organizational agents,
which can play one or more roles. At the same
time, each role is designed with a set of goals
and authorization levels that allow the agent to
DFKLHYH WKH SUHGH¿QHG JRDOV ,Q DQ HEXVLQHVV
process, a human or software agent represents a
business enterprise and performs activities on its
behalf. Agents perform the individual business

activities that comprise the e-business process.
Business activities require access to resources of
the organization in order to perform the e-busi-
ness process. Activities are operations performed
by agents on individual resources owned by a
business enterprise. Resources, owned by vari-
ous owner organizations or business enterprises,
coordinate activities that are performed on them.
In the e-business process universe of discourse,
2441
Semantic Knowledge Transparency in E-Business Processes
information and knowledge are central resources.
They are used by actors in business enterprises to
perform their assigned tasks (activities) in order
to accomplish their goals. In this chapter, we
XWLOL]HDSUDJPDWLFGH¿QLWLRQRINQRZOHGJHWKDWLV
explicit and declarative enough to be represented
by a standards-based knowledge representation
language or formalism. Additionally, we constrain
this declarative knowledge as amenable to being
processed through some reasoning mechanism
to reach useful inference.
The essential set of concepts fundamental
to model e-business processes are: business
enterprise, agent, business activity, resource,
coordination, information, and knowledge. These
concepts are similar to those proposed by Malone
and Crowston (1994). The conceptualization of the
e-business process universe of discourse for an
intelligent infomediary-based e-marketplace is:

In an e-business process, a business enterprise
is represented by an agent to perform activities
which are coordinated by resources.
Description Logic Model for
Knowledge Representation of
E-Business Processes
The elementary descriptions of the atomic
concepts in the intelligent, infomediary-based
e-Marketplace problem domain include:
i. Business enterprise (BE)
ii. Agent (Ag)
iii. Business activity (Ac)
iv. Resource (Rs)
Elementary descriptions of the atomic rela-
tionships in the intelligent, infomediary-based
e-marketplace problem domain include:
i. Represents (
{ IsRepresentedBy
-
)
ii. Performs (
{ IsPerformedBy
-
)
iii. Coordinates (
{ HasCoordination
-
)
Here, if R is a relationship between two concepts
in the problem domain, then R

-
denotes the inverse
of the relationship R. DL derives its descriptive
power from the ability to enhance the expres-
siveness of the atomic descriptions by building
complex descriptions of concepts using concept
constructors. These terminological axioms make
statements about how concepts or roles are related
to each other.
This develops a set of terminologies, comprised
RI GH¿QLWLRQV ZKLFK DUH VSHFL¿F D[LRPV WKDW
GH¿QHWKHLQFOXVLRQV) or the equivalence ({).
The increased expressive power of the language
is manifested in a range of additional construc-
tors, including:
 R.C (full existential value restriction)
¬C (atomic negation of arbitrary concept)
Figure 3. E-business process universe of discourse for an intelligent, infomediary-based, e-market-
place
Business
Enterprise
Agent
Activity Resource
Is Represented By
Performs
Represents Is Performed By
Coordinates
HasCoordination
Owns a
Business

Enterprise
Agent
Activity Resource
Is Represented By
Performs
Represents Is Performed By
Coordinates
HasCoordination
Owns a
2442
Semantic Knowledge Transparency in E-Business Processes
< n R (at-most cardinality restriction)
> n R (at-least cardinality restriction)
= n R (exact cardinality restriction)
< n R.C (qualified at-most cardinality
restriction)º
> n R.C TXDOL¿HG DWOHDVW FDUGLQDOLW\ UHVWULF-
tion)
= n R.C TXDOL¿HGH[DFWFDUGLQDOLW\UHVWULFWLRQ
<
n
R (concrete domain max restriction)
>
n
R (concrete domain min restriction)
=
n
R (concrete domain exact restriction)
Given the aforementioned concepts and rela-
tionships in the problem domain, we can begin to

GH¿QHWKHUHODWLRQVKLSVEHWZHHQWKHFRQFHSWVLQ
WKHGRPDLQ+HUHZHGH¿QHWKHWHUPLQRORJ\IRU
the intelligent, infomediary-based e-marketplace
problem domain using the following terminologi-
cal axioms. This forms the knowledge represen-
tation terminology, or TBOX, for the problem
domain and the basis for the machine-interpretable
representation of the ontology in OWL-DL.
A BEFRQFHSWLVGH¿QHGDVDThing, the top
concept in OWL-DL, which is represented by at
least one Ag in the problem domain.
BusinessEnterprise 
(>1 IsRepresentedBy  Agent) 
(= 1 HasID  StringData) 
(>1 HasAddress  Address) 
(>1 HasDescription  StringData) 
(>1 HasReputation  StringData) 
(>1 HasTransactionSatisfactionHistory 
StringData)

An AgFRQFHSWLVGH¿QHGDVD7KLQJWKDWUHS-
resents a BE and performs activities for the BE.
Agent 
(= 1 HasID  StringData) 
( = 1 Represents  BusinessEnterprise) 
( >1 Performs  BusinessActivity)
A Business ActivityGH¿QHGDVD7KLQJWKDW
is performed by an Ag, has a coordination rela-
tionship with Rs, and has a Begin Time and an
End Time.

Business Activity 
( = 1 hasLabel  StringData) 
( >1 isPerformedBy  Agent) 
( >1 hasCharacteristics  StringData) 
( >1 HasDescription  StringData) 
( >1 HasCoordination  Resource) 
( = 1 hasBeginTime  DateTimeData) 
( = 1 hasEndTime  DateTimeData)

Each RsLVGH¿QHGDVD7KLQJWKDWLVRZQHG
by exactly one BE and coordinates Business
Activities.
Resource 
( = 1 hasID  StringData) 
(>1 hasOwner Business Enterprise) 
(>1 Coordinates  BusinessActivity)
We utilize a novel and theoretically grounded,
activity-resource, coordination mechanism for
capturing the relationships between activities and
resources. This allows for the explicit modeling
of the coordination of individual business activi-
ties, and the e-business process itself, using the
information and knowledge resources in inter-
organizational e-business processes over virtually
integrated business enterprises. Business Activi-
ties depend on resources and require coordination
mechanisms to resolve these dependencies in
an e-business process. A resource is related to
an activity through a Coordinates relationship,
where the resource coordinates business activities

through various coordination mechanisms.
Resource  (Coordinates BusinessActivity)
BusinessActivity (HasCoordination  Re-
source)
2443
Semantic Knowledge Transparency in E-Business Processes
'HVFULSWLRQORJLFDOORZVIRUWKHVSHFL¿FDWLRQRI
generalization-specialization hierarchies of rela-
tionships. We use the notion of activity-resource
GHSHQGHQF\ZKHUHDFWLYLWLHVKDYHDVKDULQJÀRZ
RU ¿W GHSHQGHQF\ ZLWK D UHVRXUFH 0DORQH HW
al., 2003) to specify the relationships between
activities and resources. Here we assume that the
Coordinates relationship between resource and
activity is an abstract, general relationship, which
materializes in the form of the specialized relation-
ships where a resource may coordination activities
through a CoordinatesFlow, CoordinatesFit, or
CoordinatesSharing relationship.
Coordinates 
CoordinatesFlow
CoordinatesFit
CoordinatesSharing
CoordinatesFlow 
CoordinatesFlowProducedBy
CoordinatesFlowConsumedBy
In addition, the CoordinatesFlow is further
specialized to capture the activity-resource co-
ordination where the resource coordinates the
ÀRZRIDFWLYLW\E\HLWKHUEHLQJSURGXFHGE\RU

consumed by a business activity.
We utilize the previous inheritance hierarchy
of the Coordinates relationship to develop a
complex description of the relationship between
Rs and Business Activities, as expressed in the
following terminological axiom.
Resource 
(>0 CoordinatesFlowProducedBy  Busines-
sActivity)
(>0 CoordinatesFlowConsumedBy  Busines-
sActivity)
(>0 CoordinatesFit  BusinessActivity)
(>0 CoordinatesSharing BusinessActivity)
Information and knowledge are the primary
resources pertinent to the problem domain we
consider in this chapter. We utilize the concept
GH¿QLWLRQV
Resource 
Information
Knowledge
These complex descriptions of concepts, built
from atomic descriptions, describe classes of
objects in the problem domain and their inter-re-
lationships. The terminological axioms presented
previously make statements about how concepts
and relationships are related to each other. The
VHWRIWHUPLQRORJLFDO D[LRPV LQFOXGLQJ GH¿QL-
tions, provide the terminology, or the TBox, for a
SUREOHPGRPDLQ7KHDIRUHPHQWLRQHGGH¿QLWLRQV
comprise the terminology for the intelligent, in-

fomediary-based e-marketplace problem domain,
LQFOXGLQJ WKH GH¿QHG DQG SULPLWLYH FRQFHSWV
and the binary relationships between them. This
provides the meta-level ontology and knowledge
representation for the knowledge base in an intel-
ligent, infomediary-based e-marketplace.
The other component of the knowledge base, in
addition to the terminology or TBox, is the world
description, or ABox that includes descriptions
of individuals in the problem domain. Together,
the TBox and the ABox comprise the knowledge
representation system based on description logics.
The knowledge representation system provides
the knowledge base and facilities to reason about
the content.
Proposed Semantic Knowledge
Representation for Supplier
Selection for Infomediary-Based
E-Marketplace
An e-procurement, supplier selection, e-business
process in an infomediary-based e-marketplace
requires information of attributes that describe the
buyer’s requirements, such as price, quantity, and
the date by which the item is required. The selec-
tion of a supplier, from a set of suitable suppliers