Automatic Service Composition Based on Process Ontology

Gexin Li1, Shuiguang Deng2, Haijiang Xia1, Chuan Lin1 1 School of Computer Science and Engineering, Wenzhou University, Wenzhou 325035, China

2College of Computer Science, Zhejiang University, Hangzhou 310027, China jsj_lgx@wzu.edu.cn

Abstract

Web service composition is a very important way to offer novel value-added services on the fly, which entrusts with the existing services via reuse and integration. The composition processes are mostly static and hence suffer from lack of support for runtime redesign. We have implemented the semi-automatic service composition with workflow technology which combines Business Process Execution Language for Web Service with process ontology. In practice, we extract edge-edge relations and node-node composition rules from the process ontology tree. For a service request, system searches the target nodes in the process ontology tree, and completes validation of the interrelations and composition rules. Subsequently, a new dynamic sub-service-composition is assembled from those specific nodes. Web service composition is a very important way to offer novel value-added services on the fly. In this paper, we propose a method for semi-automatic composition of web services using process ontology. This method describes unfixed part of a process by process ontology. Then, it extracts edge-edge relations and node-node composition rules to guide automatically service composition. 1. Introduction

Nowadays, both industry and academia are increasingly interested in web service composition. Therefore, researchers from both industry and academia join forces to advance the art of web service composition. Industry endeavors to customize series of protocols and language standards such as SOAP (Simple Object Access Protocol), WSDL (Web Service Description), UDDI (Universal Description, Discovery and Integration) and BPEL4WS (Business Process Execution Language for Web Service). Meanwhile, researchers from academia undertake exciting studies in service discovery, description, composition and framework. Many successful service composition frameworks are proposed, such as E-Flow [1], METEOR-S [2, 3, 4], and so on. In these frameworks, it is typical to use mature workflow to implement service composition.

Currently，most of frameworks and systems which are used to compose web services based on workflow

techniques require the processes to be predefined, and services to be statically bounded in advance. This kind of composition is called static composition. Unfortunately, static composition naturally lacks of necessary flexibility and scalability, especially for variable applications where the unfixed factors may stem from domain rules, business policies, user preferences, and the dynamic Internet environment. For example, the desired results can not be gained from an unchanged process when users temporarily change their decisions. Automatically composing Web services for applications with such uncertain requests, which is called automatic Web service composition, is intriguing. To promote the degree of automation by entrusting with the existing web services, we need to reduce manual operation and improve the efficiency of composition generation and execution. In practice, the pivotal strategy resides in taking advantage of semantic and ontology.

Some exciting stories of automatic Web services composition have been reported. Deng [5, 6] proposed a service composition framework based on flexible workflow to enable part of a process to be created automatically, and encapsulate those uncertain, variable, dynamic activities into the black-boxes described by rules which were generated from domain knowledge, business policies and user requirements. In this paper, we use semantic and ontology to describe those rules, and propose a new semi-automatic service composition approach using mature workflow technology. Namely, in a workflow, the fixed part is pre-defined, and the corresponding services are bounded in advance. The rest will be temporarily produced just before execution. This kind of semi-automatic service composition has been applied successfully in the Dartflow project, a sub-project of DartGrid [7, 8, 9]. Based on the work presented in [5], we propose a method based on process ontology to automate service composition.

The rest of this paper is organized as follows: Section 2 gives the formal definition of process ontology and relation-rule among service nodes, section 3 discusses how to realize service composition using process ontology. The related work on service composition and the conclusion are introduced in section 4 and 5 respectively.

Third International Conference on Next Generation Web Services Practices

0-7695-3022-2/07 $25.00 © 2007 IEEEDOI 10.1109/NWESP.2007.17

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2. Process Ontology

Process ontology is advantageous to the service dynamic discovery, selection, composition and invocation. Each service may correspond to a node of process ontology tree. In order to realize service composition, we formally define process ontology, some relations and rules as follows:

Definition (Process Ontology). A process ontology is a 3-tuple: , ,PO N E R=< > , where

(1) N is a set of nodes, which consists of individual node in process ontology.

(2) E is a set of edges. (3) R is a set of rules. It is a union set of edge-edge

relations and node-node composition rules. Definition (Rule Set). R is a rule set, a union

consisting of SR set and CR set, i.e. R SR CR= ∪ . SR is the edge-edge relation set; CR is the node-node composition rule set. Each rule can be defined as:

{ , , }r t Ar e= where, (1) t is the rule type; (2) Ar is the set of abstract services referred by the

rule; (3) e is the expression of the rule.

In the defined process ontology, the edge-edge relation set SR includes four elements: , ,⊕ ⊗ and Θ . The node-node composition rule set CR includes two elements: ⇒ and . The definition of relation and rule is as follows.

Definition (Choice Relation). Given an abstract services set 1 2{ , , , }mA ψ ψ ψ= , a choice relation, denoted as 1 2 mψ ψ ψ⊕ ⊕ ⊕ , defines that at least one

abstract service (1 )i i nψ ≤ ≤ must be selected. Definition (Exclusive Relation). Given an abstract

services set 1 2{ , , , }mA ψ ψ ψ= , an exclusive relation, denoted as i jψ ψ⊗ , defines that only one abstract

service from A can be selected. Definition (Prior Relation). Given an abstract services

set 1 2{ , , , }mA ψ ψ ψ= , a prior relation, denoted as

i jψ ψ , defines that the service iψ must be finished before the service jψ is executed.

Definition (Unordered Relation). Given an abstract services set 1 2{ , , , }mA ψ ψ ψ= , an unordered relation, denoted as i jψ ψΘ ，defines that the services executing is

unordered. Definition (Sequence Composition Rule). Given two

abstract services 1ψ and 2ψ , denoted as 1 2 ψ ψ⇒ , defines that 1ψ and 2ψ are executed in turn.

Definition (Parallel Composition Rule). Given two abstract services 1ψ and 2ψ , denoted as 1 2 ψ ψ , defines that 1ψ and 2ψ may be executed at the same time.

For convenience, the node pair (b, c) in a process ontology tree shown in Figure 1 describes not only the relation between edge ba (from node b to its parent node a) and edge ca (from node c to its parent node a), but also the composition rule between the node b and the node c. The rest can be deduced analogically.

a

b c d

e f h i j

k l

{ , }Θ { , }⊕ ⇒

{ , }⊕

{ }⊗ { , }⊕ ⇒

{ , }⇒

{ }⊗g

Fig. 1. Process Ontology Tree

The edge-edge relations and node-node composition rules are shown in Table 1.

Table 1. Relations and rules

Node Pair Relation and Rule (b , c) { Θ , } (c , d) { ⊕ ,⇒ } (g , h) { ⊕ ,⇒ } (i , j) { ⊗ } (b , d) { ⊕ , } (e , f) { ⊗ } (k , l) { ,⇒ }

3. Automatic Service Composition

In order to realize automatic service composition, firstly, we should extract all relations and rules from a process ontology tree. Secondly, according to service request, system can search for the target nodes in the process ontology tree, and complete validation of the interrelations. Finally, new and sub service compositions are automatically completed. Due to the limited space, here we only introduce how to extract relations and rules from ontology tree.

In the given process ontology, we extract all relations and rules to form the set R. The edge-edge relations and node-node composition rules with same parent node will be sought. The goal of extracting is to check validity of the requested service for automatic services composition.

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Therefore, node depth and parent node are introduced as follow:

Definition (Node Depth). ( )ideep ψ defines the

distance, also called depth, from root node to node iψ in a process ontology tree. Its value is number of nodes from root node to node iψ (including iψ ). therefore, ( ) 0deep root = .

Definition (Parent Node). ( )iparent ψ is the parent

node of iψ . In the defined process ontology, the algorithm

_EXTRACT RULES for extracting relations and rules is as follows. The time complexity of this algorithm is 2( )O n . The executing time will increase by square factorial to the increase of node number. During the process of extracting relations and rules, Time of extraction is 2

nC .

: _:

, , , .:

.

:

ALGORITHM EXTRACT RULESINPUT

PO N E R n is the numbers of nodesOUTPUTSet R that consists of the relationsbetween edges and rules between nodes

METHODS

======================================

=< >

; ( 1, 1;( 1)&&( 1)&&

( ); , ){ ( ( ) ( )

&& ( ) ( ))

( , ) ; }

i j

i j

i j

ET RFOR i j i n j n

i j i jIF deep deep

parent parentInsert the the relations and rules

of into R

ψ ψψ ψ

ψ ψ

= ∅= = < + < +

≠ + + + +==

==

======================================

It is noted that a node pair which does not have the predefined relations or rules would also undergo extraction once. In addition, when the extracted relations and rules are inserted into set R, there may be element duplication in set R. To improve the efficiency of verifying the validity of services subsequently, those redundant relations and rules should be removed from set R. The _CUTTING R algorithm to eliminate redundancy is shown in right page. 4. Related Work

Web service is an emerging application pattern, which orchestrates application logic, business intelligence, network technology, workflow management, knowledge

1

: _: ;

: :

; ; ; 1 | | -1 {

1

i

ALGORITHM CUTTING RINPUT the Set ROUTPUT RMETHOD

Set RRMarking the element of R with rInsert r into RRFOR i to R

FOR j i

====================================

= ∅

== + | | {

{ int ; } }

} ;

i j

j

to RIF r r

Inset r o RR

R RR

≠

=====================================

representation, logic reasoning, information integration and other techniques. Service composition has become the focus of research and development in web service domain. Researchers from both industry and academia are now joining forces to bring major advances in the art of Web service composition, and we are witnessing a new form of service composition which unleashes a revolution of new possibilities.

Deng [5,6] developed a framework for semi-automatic service composition, which enabled a part of a process to be created by automatic service composition, and encapsulated the dynamic and changeable part into black-boxes. During the process executing phase, black-boxes were concretized by composing services into a sub-process according to the predefined rules automatically.

Abraham and his colleagues [10,11] proposed a new method of service discovery based on complex process ontology, Motivated by their work, the authors of this paper developed a novel semi-automatic service composition approach via process ontology [12] coupled with BPEL4WS [ 13].

CHUN [14] presented three different types of compositional knowledge (expressed as rules), that is syntactic knowledge, semantic knowledge and pragmatic knowledge. The composition knowledge and relative rules could be expressed by extending standards based on XML. These rules played an important role in service discovery and service composition. Chun’s work was applied in government affair management very successfully.

Ma and his group [15] presented a prototype framework based on a goal-driven ontology in which user’s goal was decomposed to sub-goals. Services were composed using ontology, service and proxy technology. The related algorithm was successfully applied to the OMWSC project.

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5. Conclusion

Web service composition has become an active field in Web service domain. It is the unique technique which is capable of offering value-added integrated services on the fly, and satisfying users’ ever-increasing service requests. This paper proposed a two steps approach to compose services into workflow. The first step is to predefine the fixed part of process in the composition service by utilizing BPEL4WS technology and to pre-bind the corresponding service. The second step is to dynamically and temporarily define the unfixed part which is determined by the diverse, uncertain and variable factors resulting in frequently service change. The proposed approach shares the simplicity and practicality of BPEL4WS, and the flexibility of OWL-S. We further demonstrated the scalability and availability of our approach when it was applied in a typical instance by sculpting out PE items with process ontology.

We are currently progressing on further extending our work. Enabling hospitals to provide PE package services is one of the research directions. We plan to adopt dynamic binding method for all-around workflow, and take advantage of domain knowledge, hospital policies, provision of renting taxi, user preferences, and so on. Moreover, how to utilize the rich information from Quality of Service is also on our agenda. It is believed that those services could enrich users’ experiences.

Acknowledgement

This work was done during the first author’s visit in

Zhejiang University. It is supported by Zhejiang Province Educational Office Project under Grant No. 20060380.

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