AGENT ONTOLOGY INTEROPERABILITY APPROACH

FOR MAS NEGOTIATIONS IN VIRTUAL ENTERPRISES

X. H. Wang, T. N. Wong and G. Wang

Department of Industrial and Manufaturing Systems Engineering, The University of Hong Kong

Pokfulam Road, Hong Kong

Keywords: Negotiation, Ontology Matching, Multi-agent System, Virtual Enterprise.

Abstract: In supply chain management, a Virtual Enterprise (VE) is a dynamic alliance of partner companies. Multi-

agent systems (MAS) have been introduced to facilitate negotiations among VE members. From the

perspective of knowledge management, heterogeneous VE members utilize different knowledge structures

and terminologies in their representative agents. To encourage their collaborative coordination and realize

mutual understanding, agent ontology interoperability should be reached. In this paper, an approach for

semantic ontology matching is proposed to generate correspondences among heterogeneous ontologies

embedded in MAS; additionally, an ontology correspondence generation and negotiation protocol is

developed to realize agent ontology interoperability in MAS negotiations.

1 INTRODUCTION

A Virtual Enterprise (VE) is a dynamic alliance of

partner companies, aiming at seeking more market

opportunities to provide high-quality with low-cost

services and products as fast as possible

(Camarinha-Matos et al, 1999). Generally, a VE is

composed of a VE initiator and several distributed

and heterogeneous VE partners. To respond to a

market opportunity, the VE initiator relies on its

partners to operate, that is, the functioning of a VE

relies on the collaboration and interaction of its

partners. Therefore, it is important for the VE

initiator to select appropriate partners to create the

initial VE.

In the VE formation phase, the initiator

negotiates with its potential partners to reach

agreement on the cooperation issues in order to form

the initial VE. In the subsequent VE functioning

phase, the initiator negotiates with its partners to

contract with each other. With the advance of agent

technology, multi-agent systems (MASs) have been

introduced to provide an effective and efficient

environment for VE formation and operation

(Norman et al, 2004). Within the MAS, agents are

established to be responsible for various functions of

different VE members. However, different VE

members may use different structures and

terminologies to organize and represent their

knowledge; it is important but difficult for agents to

reach mutual understanding through heterogeneous

knowledge representations. In order to achieve

semantic interoperability among agents, many

researchers have proposed to adopt the ontology-

based approach to support MAS negotiations in the

VE (Chen et al, 2008; Lo et al, 2008; Trappey et al,

2009; Garcia-Sanchez et al, 2009). Some are

concentrating on developing ontology matching

mechanisms to reach semantic interoperability

(Chen et al, 2008; Lo et al, 2008); while some are

focusing on realizing MAS automated negotiations

(Trappey et al, 2009; Garcia-Sanchez et al, 2009). In

most of the current applications, there are only a few

publications focusing on an entire framework to

illustrate how agent ontology interoperability is

reached, how semantic matching is conducted, and

how ontology based MAS negotiations is realized.

The objective of this paper is to establish an

ontology-based framework to achieve agent

ontology interoperability and to realize automated

MAS negotiations in the VE.

The remainder of this paper is organized as

follows: Section 2 presents some related work.

Section 3 outlines the MAS framework and semantic

matching approach. Section 4presents system

implementation and experiment evaluation. Section

5 presents conclusions and future work.

149

H. Wang X., N. Wong T. and Wang G. (2010).

AGENT ONTOLOGY INTEROPERABILITY APPROACH FOR MAS NEGOTIATIONS IN VIRTUAL ENTERPRISES.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Agents, pages 149-154

DOI: 10.5220/0002704301490154

 SciTePress

2 RELATED WORK

2.1 Ontology-based MAS for VE

Many researchers have engaged in providing

solutions in this field to reach mutual understanding

and automated MAS negotiations in VE.

In Lo et al (2008)’s application, the VE initiator

builds up domain ontology with an ontology

vocabulary inside its database, which can provide

flexible matching with different enterprises. Trappey

et al (2009) developed a JADE-based workflow

management system, where the workflow ontology

is constructed to represent relationships between

workflows, resources and actors. Garcia-Sanchez et

al (2009) designed and implemented a JADE-based

platform for the provision of semantic web services,

where ontologies is used to describe application

domain knowledge, agent local knowledge,

negotiation knowledge and semantic web services

knowledge.

The above applications are focused on

developing MAS platforms. Detail information

about utilizing heterogeneous ontologies is not

available. Therefore, a series of semantic ontology

matching approaches are proposed to fill the gap.

2.2 Ontology Matching

Ontology is increasingly considered as an essential

factor for reaching interoperability across

heterogeneous systems. Meanwhile, ontology

matching has been introduced to combine distributed

and heterogeneous ontologies.

Choi et al (2006) conducted a survey on ontology

matching, aiming at providing a comprehensive

understanding of ontology matching. Laera et al

(2007) proposed an argumentation based ontology

matching for MAS. Correspondence matching

repository is constructed, where candidate matchings,

ontology mismatches, matching preference and

candidate threshold are stored in. Bollegala et al

(2007) engaged in conducting query-based semantic

matching studies, where various types of query

ontologies were constructed to enable agents’

learning ontology models.

As revealed in the above publications, previous

research efforts have been mainly spent on the basic

ontology matching approaches. As presented in this

paper, a series of ontology-based semantic matching

approaches are proposed to reach agent ontology

interoperability in VE negotiations.

3 APPROACH

3.1 Ontology-based MAS Framework

An ontology-based MAS framework has been

developed to enable distributed and heterogeneous

VE members to communicate and negotiate. Figure

1 depicts the architecture of the system framework,

which comprises the following elements:

Potential Partners

KMA

CAi

BAi

CAi

SAi

Market

Opportunity

Knowledge

Repository

Partner KB

TEA

VE Partners

Performance

TDA

1. Individual ontology models

2. Correspondence library

3. Correspondence candidate library

1. Individual ontology models

2. Correspondence library

Figure 1: Ontology based MAS system architecture.

 Buyer Agent (BA): In this study, BA is on behalf

of the VE initiator, which initiates the

preliminary requirements to select its partners.

 Seller Agent (SA): SA is on behalf of VE

partners, which responds to BA’s requirements

and negotiate with it to reach agreements.

 Task Decomposer Agent (TDA): Receive

preliminary requirements from BA and

decompose them into small sub-tasks.

 Coordinator Agent (CA): Receive sub-tasks

from TDA, and sends them to BA and

initializes the negotiation.

 Task Evaluator Agent (TEA): Evaluate the

partners’ performances, and record it into VE

initiator’s knowledge repository.

 VE Initiator’s Knowledge Repository: Store

individual ontology models, correspondence

libraries, and correspondence candidate

libraries.

 VE Partner’s Knowledge Base: Store individual

ontologies, correspondence libraries with

different partners.

 Correspondence library: Record historical

correspondences used in past negotiations

between BA and other agents.

 Correspondence candidate library: Record

correspondence candidates obtained from

semantic matching process, using domain

individual ontologies.

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3.2 Correspondence Generation

and Negotiation Protocol

Figure 2 depicts ontology based correspondence

generation and negotiation protocol, which aims at

realizing mutual understanding and automated

negotiation among VE members. Specification for

this protocol is provided in Table 1.

Figure 2: Ontology correspondence generation and

negotiation protocol.

Here, BT is a term using buyer’s terminology,

is a term using seller’s terminology in form of

(BT, ST

) in buyer’s correspondence library; Sagent

is an agent participating in negotiations using (BT,

) as a correspondence; TT is a term translated

according to buyer-seller correspondence candidate

library; translated indicates that (BT, TT) is a

correspondence candidate pair, not a correspondence

pair; RT

is a term in form of (BT, RT

) in other

partner’s correspondence libraries.

Table 1: Interpretation of negotiation messages.

Message

Name

Functionality

INFORM Send a term to the receiver agent;

Ask it to check its correspondence

library using the received

information.

CONFIRM Confirm the correspondence with the

initiator.

NOT_UNDER

STAND

No correspondence is in the partner’s

library.

CALL FOR

BID

Initialize the negotiation with a

preliminary bid.

PROPOSE Respond to CALL FOR BID

message.

NEGOTIATE Negotiate with each other.

CONTRACT Reach agreement and contract.

FAILURE No agreement is made.

3.3 Knowledge Representation

Ontology is used to describe domain knowledge of

individual VE partners. In this study, two types of

individual negotiation ontologies are constructed, i.e.

Buyer ontology and Seller ontology, as shown in

Figure 3.

Figure 3: Partial ontologies for buyer and seller 1.

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3.4 Semantic Ontology Matching

The purpose of ontology matching is to find out the

correspondences between two separate ontologies.

By doing so, heterogeneous ontologies can reach

mutual understanding among each other.

Definition 1 (Ontology Matching System):

Suppose O and

are two ontologies, which are

defined as O = <C, R, I> and

>=<

''''

,, IRCO

. Here,

C stands for concepts, R stands for relations, while I

stands for instances. An ontology matching system

is defined as a triple (

MOO ,,

). M is the

correspondence between two ontologies, which is

defined as (

). Here,

and ee

are concept or

attribute in ontologies O and

, respectively;

similarity value between

and ee

The semantic of heterogeneous ontologies is

calculated by ontology matching. This study

proposes three types of ontology matching methods,

which are explained in the following sections.

3.4.1 Name-based Term Matching

In name-based matching process, firstly, names of

elements (concept and attribute) should be stemmed

and pre-processed into atomic terms. Secondly,

WordNet is introduced to find semantically similar

terms among heterogeneous ontologies (Cognitive

Science Laboratory, Princeton University, 2006).

Algorithm 1: Term similarity calculation

Input: Term T from ontology O, Term

from

ontology

Output: <

>, correspondence between T and

 Initialize similarity value

),(

= 0;

 Get all the senses and their hypernym of T

and

respectively, i.e., T (*) and

(*);

 Calculate the lengths of all paths between T

and

, and get the shortest path;

Suppose L is the length of the shortest path

between T and

, then similarity

),(

can be

calculated by (1):

eTT

−

=),(

(1)

 Calculate the depths of all terms in set of

(*)'(*) TT ∩

Suppose H is the biggest depth between T and

then similarity

),(

can be calculated by (2):

ββ

−

=),(

(2)

 Final term similarity can be calculated by (3):

),(),(),(

TTTTTT

σωσωσ

1 where

(3)

3.4.2 Structure and Constraint

based Attribute Matching

Ontology structure and constraint defined within

each element have significant effect on elements’

semantics. For concepts, a child concept will inherit

the semantics of its father concept; for attributes,

different attributes’ data types represent different

semantics. In this study, attributes are main

components which make up of the negotiation

messages, a structure and constraint based hybrid

matching algorithm for attribute is proposed.

Algorithm 2: Attribute similarity calculation

Input: O = <C, R, I> and

>=<

''''

,, IRCO

; Attribute

and its related concept

c of ontology

Output: <r,

r ,

max

>, the highest similarity between

all attributes in O and attribute

r from

 Find all attribute r in O of the same data type

with

r , and their related concept names c.

 Calculate similarities between qualified

attribute r and

r using formula (4):

),(),(

),(),(),(

044

013

012

011

rrrc

rcccrr

σασα

σασασ

++=

(4)

 Then the highest similarity can be obtained by

formula (5):

max

= max (

),(

) (5)

3.4.3 Distance between Data Patterns

based Instance Matching

The contents of instances reveal some correlations

among different ontologies. Therefore, an instance

matching is proposed based on auxiliary information,

such as data pattern, value distribution, average, etc.

Algorithm 3: Instance similarity calculation

Input: Instance strings SB

and SS

Output: Distance between input strings, d (SB, SS)

 Pre-process the attribute data according to the

following rules: Turn all numerals into symbol

“0”; Turn all alphabets into symbol “1”; Turn

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Table 2: Similarity results for correspondence candidate generation using three different methods (Buyer and Seller 1).

Correspondence pair Attribute method Instance method Combined method

Buyer Seller 1 Similarity Rank1 Distance Rank2 Average

(Rank1, Rank2)

Rank3

resolver resolver 0.4901 1 0 1 1 1

initiator proposer 0.3469 3 0 2 2.5 2

issue name item name 0.6073 2 1 1 1.5 1

issue value item value 0.5944 2 0 1 1.5 1

product product 0.4414 3 0 1 2 1

name name 0.5606 4 0 1 2.5 1

address address 0.5204 2 0 1 1.5 1

issue name item name 0.5947 1 1 1 1 1

status status 0.7506 1 0 1 1 1

payment pattern payment pattern 1 1 8 4 2.5 4

due date value time value 0.6503 2 0 1 1.5 1

warranty value service time 0.3994 4 0 3 3.5 3

quantity value order size 0.6025 3 1 2 2.5 2

price value price value 1 1 0 1 1 1

adjustment adjustment 0.6237 1 0 1 1 1

all “ ,” into symbol “X”; Turn all white space

into symbol “Y”; Turn all “http://” into

“*******”; Turn all “@-:/.” into symbol “#”.

Afterwards, the contents of different instances

are transformed into a series of similar strings.

 Introduce Edit Distance to calculate similarity

of strings (Navarro, G., 2001).

Definition 2:

 SB stands for a

string from buyer; |SB| stands

for the length of SB;

stands for the i

character of SB, for an integer i

∈{1…|SB|};

jiiji

SBSBSBSB ...

1... +

stands for a partial string

from

, where i > j;

 Strings for sellers are defined in the same way.

Definition 3: d(SB, SS) indicates the distance

between two strings SB

and SS, which is the minimal

cost of a sequence of operations that transform SB

into SS. Here, the operation refers to delete, insert or

substitute a character.

Definition 4: Define a matrix C

|SB|, |SS|

, where C

i, j

represents the minimum number of operations

needed to match

..1

. d(SB, SS) is computed

as follows:

 C

i, 0

= i; C

0, j

= j;

 C

i, j

= if (

) then C

i-1, j-1

else 1 + min (C

i-1, j

, C

i, j-1

, C

i-1, j-1

)

 d (SB, SS) = C

|SB|, |SS|

4 IMPLEMENTATION

AND EVALUATION

In this study, the MAS is implemented using JADE.

As a simple example in supply chain management, a

company has to purchase a product from two

potential suppliers. The case is therefore to establish

a VE with two of its suppliers. Accordingly, the

MAS comprises three VE members, here the VE

initiator acts as the buyer part.

Since different VE members are independent

companies, it is usual for them to adopt different

terminologies, even though they are describing the

same semantics. It is easy for human experts to

identify the representations, but difficult for agents

to recognize automatically. Therefore, semantic

ontology matching approaches are developed to

reach agent interoperability. As detailed in Section

3.4, the word method is a basic method to calculate

term similarities; the attribute and instance methods

are based on hybrid criterions and data patterns of

instances, respectively.

In this study, suppose that two sellers share a

same ontology structure. Two separate ontologies

are shown in Figure 3. Correspondences are

generated, and performances of different methods

are compared, which are as shown in Table 2.

To evaluate the performance of different

matching methods, four typical evaluators in

Information Retrieval (IR) are adopted (Islam, A.,

2008). In the following, TP stands for True Positive

(how many correspondences were selected with

right meanings); FP stands for False Positive (how

AGENT ONTOLOGY INTEROPERABILITY APPROACH FOR MAS NEGOTIATIONS IN VIRTUAL ENTERPRISES

153

many correspondences were not selected, which are

actually with right meanings); FN stands for False

Negative (how many correspondences were selected

with wrong meanings).

The evaluators are listed as below:

 Precision (P): P = TP / (TP + FP)

 Recall (R): R = TP / (TP + FN)

 F-Measure (F): F = 2PR / (P+R)

 Accuracy (A): A = (TP + FP) / (TP + FP + FN)

Figure 4 shows the experiment evaluation results.

Figure 4: Performance of attribute & instance

& combination methods (Rank 1 as threshold).

Figure 4 illustrates the comparison of the

attribute, instance and combination methods, where

rank No. 1 correspondence candidate is adopted as

the threshold. It indicates that with the threshold set,

performance of the attribute method is the worst and

that of the instance method is the best.

However, in reality, the instance method is less

restrained since contents of instances can be readily

modified manually, the performance cannot be very

stable. For this reason, the combination method is

adopted to balance the attribute method and the

instance method in a stable and well-performed way.

5 CONCLUSIONS

This paper presents an ontology-based approach to

achieve agent ontology interoperability in MAS

negotiations in the VE formation process. First of all,

an ontology-based correspondence generation and

negotiation protocol is proposed to provide a way

for agents to interact with each other. Secondly,

three semantic ontology matching methods are

proposed, where the combination method with Rank

No.1 correspondence candidate as threshold is

adopted as the most appropriate method to realize

agent ontology interoperability.

The research is still in progress. Future

enhancements will be developed from two aspects:

Firstly, new knowledge is created in every contract

round. It is required to consider how to update the

current knowledge libraries and to manage VE

knowledge evolution in an effective and efficient

way. Secondly, for different roles of agents, i.e.

buyer agents and seller agents, in order to ensure the

security of the negotiation platform, different

members should be assigned different levels of

authority to access the system. Therefore, a

knowledge access control mechanism is to be

developed in the future.

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