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

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

2. Introduction • 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

3. Related Work • 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

4. Analyze of current sematic match algorithms (1 / 3) • 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

5. Analyze of current sematic match algorithms (2 / 3) • 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) •  considered binary relation •  considered Semantic distance

6. Analyze of current sematic match algorithms (3 / 3) • Semantic match algorithms based on semantic distance • Represents the similarity degree of two concepts • A  B & B  A : equivalent • Problem • There 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

7. Four kinds of relations in Improved algorithms • 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. C2 C1 C1 C2 C1 C2 C1 C2

8. Improved algorithm – Semantic Distance

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

10. Improved algorithm – Similar Relation

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

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

13. Differences of these three algorithms • precision of the matching degree • consideration of the binary relation • consideration of the similar relation • consideration of the direction • False positives

14. 1. precisionof 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. Conclusion • 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