1. 20110518 Yoon kyoung-a A Semantic Match Algorithm for Web Services Based on Improved Semantic Distance Gongzhen Wang, Donghong Xu, Yong Qi, Di Hou School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

2.  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 algorithm Basic 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

3.  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 work Service 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

4.  Four matching degrees  exact > plugIn > subsumes > fail  Matching degree of the advertisement against the request  degreeOfMatch(outR, outA), degreeOfMatch(inR, inA)  Problem If an advertisement claims to output a certain concept C, it will output each subclass of C However, in the real world, it will usually output some subclasses of C, not each subclass Analyze of current sematic match algorithms (1 / 3)

5.  Four matching degrees  exact > plugIn > subsumes > fail  Problem Does 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 algorithms (2 / 3)  considered Semantic distance  considered binary relation

6. Analyze of current sematic match algorithms (3 / 3)

7.  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 binary 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 algorithms C2 C1 C2 C1 C2 C1 C2

8. Improved algorithm – Semantic Distance

9.  Calculate semantic distances  linked just by generalizations or just by specializations Improved algorithm

10. Improved algorithm – Similar Relation

11.  Match function MF(d) must satisfy three conditions Improved algorithm – Binary Relation

12. Performance comparison Al 2 Current Al Proposed Al Specialization, Generalization  1 Binary relation  2 Specialization  1 Generalization  1 Binary relation  2

13. 1.precision of the matching degree 2.consideration of the binary relation 3.consideration of the similar relation 4.consideration of the direction 5.False positives Differences of these three algorithms

14. 1. precision of the matching degree Al 2 Current Al Proposed Al

15. 2.consideration of the binary relation Al 2 Current Al Proposed Al

16. 3.consideration of the similar relation Al 2 Current Al Proposed Al

17. 4.consideration of the direction Al 2 Current Al Proposed Al

18. 5. False positives Al 2 Current Al Proposed Al

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

20.  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 distance It removes the false positives It considers the direction  Improved algorithm improves the recall ration and the precision ration of service discovery Conclusion