A Semantic Match Algorithm for Web Services Based on Improved Semantic Distance

20110518 Yoon kyoung-a

A Semantic Match Algorithm for Web Services Based on Improved Semantic

Distance

Gongzhen Wang, Donghong Xu, Yong Qi, Di HouSchool of Electronics and Information Engi-neering, Xi’an Jiaotong University, Xi’an 710049, China

UDDI Current web service discovery mechanism is mainly based on it Include WSDL Based on syntax Limit the precision ration and the recall ration of service discovery

Presented semantic Match algorithmBasic semantic Match algorithm Semantic Match algorithm based on semantic distance

also limit the precision ration and recall ration

Propose a semantic match algorithm based on improved semantic distance To eliminate defects Improve the recall ration and the precision

ration of service discovery

Introduction

OWL-S Describe the properties and capabilities of their web services Three essential type of knowledge about a services

Service Profile: What the services does Service Model: How the services workService Grounding: details of how to access a service

Service Profile Describe the function and interface of web services

Important role in semantic match Services are described in terms of IOPE(Input, Output, Preconditions

and Effects) Current semantic match algorithms mainly based on Input and

Output Advertisements and search queries:

are expressed in terms of OWL-S The process of service match :

extract Inputs and Outputs from the advertisement match them with Inputs and Outs of search queries

Ex) Input: date , region , Output: weather

Related Work

Four matching degrees exact > plugIn > subsumes > fail

Matching degree of the advertisement against the re-quest degreeOfMatch(outR, outA) , degreeOfMatch(inR, inA)

ProblemIf an advertisement claims to output a certain concept C, it will output

each subclass of CHowever, in the real world, it will usually output some subclasses of C,

not each subclass

Analyze of current sematic match algo-rithms (1 / 3)

Four matching degrees exact > plugIn > subsumes > fail

ProblemDoes not cover the binary relation

Advertisement: Ballpen, Ballpen has a property BallenLead Request: “BallenLead”

Does not cover the similar relation Advertisement: HireHonda Request: “HireBMW”

About matching degrees(only four matching degree) (Car , BMW) , (Vehicle , BMW)

Analyze of current sematic match algo-rithms (2 / 3)

considered Semantic dis-tance

considered binary rela-tion

Semantic match algorithms based on semantic distance Represents the similarity degree of two concepts A B & B A : equivalent

ProblemThere is no direction

Ex) Concept A is a subclass of concept B

- A B B A There are some false positives

C and E are not catchable at all

Analyze of current sematic match algo-rithms (3 / 3)

Specialization If concept C1 is a subclass of concept C2, C1 is a specialization of C2. If

C1 is an immediate subclass of C2, in weighted ontology map, there is a direction edge representing the specialization from C2 to C1.

Generalization If concept C1 is a superclass of concept C2, C1 is a generalization of C2.

If C1 is an immediate superclass of C2, in weighted ontology map, there is a direction edge representing the generalization from C2 to C1.

The binary relation If concept C2 is a part of concept C1, the relation from C1 to C2 is a bi-

nary relation. If C2 is a immediate part of C1, in weight ontology map, there is a direction edge representing a binary relation from C1 to C2.

The similar relation If concept C1 and concept C2 have a same superclass, there is a similar

relation from C1 to C2.

Four kinds of relations in Improved algo-rithms

C2

C1

C1

C2

C1

C2

C1

C2

Improved algorithm – Semantic Distance

Calculate semantic distances linked just by generalizations or just by specializations

Improved algorithm

Improved algorithm – Similar Relation

Match function MF(d) must satisfy three conditions

Improved algorithm – Binary Relation

Performance comparison

Al 2

Current Al

Proposed Al

Specialization, Generalization 1Binary relation 2

Specialization 1Generalization 1Binary relation 2

1. precision of the matching degree2. consideration of the binary relation3. consideration of the similar relation4. consideration of the direction 5. False positives

Differences of these three algorithms

1. precision of the matching degree

Al 2

Current Al

Proposed Al

2.consideration of the binary relation

Al 2

Current Al

Proposed Al

3.consideration of the similar relation

Al 2

Current Al

Proposed Al

4.consideration of the direction

Al 2

Current Al

Proposed Al

5. False positives

Al 2

Current Al

Proposed Al

Two request R1(Input: Novel, Output: Price) , R2 (Input: Monograph, Output:

Price)

Proposed a semantic match algorithm based on improved semantic distance Compared to the algorithm 2

It considers the binary relation and similar relation Compared to current semantic match algorithm based on sematic

distanceIt removes the false positives It considers the direction

Improved algorithm improves the recall ration and the precision ration of service discovery

Conclusion