Matchmaking of Semantic Web Services Using Semantic-Distance Information Mehmet Şenvar, Ayşe Bener Boğaziçi University Department of Computer Engineering

2 OUTLINE Introduction  Matchmaking Related Work Concepts  Ontologies, UDDI,... Matching Details  Algorithms Simulation & Results Conclusion and Future Work

3 Introduction Use of Web Services Semantic Web Matchmaking Properties of matchmaking process  Extendable, efficient,general..

4 State of the Art Discovery Provides non-semantic search Keyword and attribute-based match Search retrieves lot of services (irrelevant results included) UDDI Business Registry Which service to select ? How to select? Search Results Selection

5 Discovery Arhitectures Web Service Discovery Architectures  Matchmaking  Brokerage  Peer-to-Peer (P-2-P) Matchmaking is the process of finding an appropriate provider for a requestor through a middle agent

6 Related Work LARKS  ITL  syntactic and semantic matching  Representation Input-output Ian Horrocks and Lei Lui’s architecture  based on DAML-S ontology  Description Logic reasoner

7 Background Ontologies  Concepts  Formalizations  Shared Vocabulary  Relations

8 Background UDDI  An open framework  Web Services Registry  Keyword search  API usage  Local usage available

9 Background OWL-S  OWL  Web Service descriptions  Semantics  Properties presents describedBy supportedBy

10 Problems in Current Semantic Discovery Solutions Set-based returned result to service requestor mostly Ontological information is not fully used User preferences and ordering choices of cannot be defined in search Threshold appliance rather than result size filtering Elimination of any mismatch case

11 PROPOSED FRAMEWORK Motivation and Goal  To provide a semantic web service discovery framework based on currently accepted technologies in a simple and effective manner  Return discovered services in an ordered and rated set  Allowing users to define their view-of-world concepts and search preferences  Use this information, named as semantic-distance, in matchmaking process Question : I am interested in tehchnology books and more on computer books than electronic books.How to define?

12 Proposed Hybrid Architecture Hybrid Architecture UDDI SuperPeers UDDI Consumer Producer ConsumerProducer

13 Service Category Based Distribution High level service ontology is defined and services are distributed to UDDI registries according to this classification Finacial Services Banking Services Retirement Services UDDI Payment Services EFT Services UDDI

14 Matching Algorithm Layered structure Extendable with plug-ins Based on subsumption relation and Semantic Distance information mainly Partial Result Set concept

15 Definition of Semantic Distance How user/agent view relation of concepts Reflect perspective of agent on ontologic concepts Weight assignment to subClassOf relation of concepts Semantic Weight /Distance = (parent-class, sub-class, similarity-weight)

16 Setting Semantic Weights Case I  Assignment is done by local users/agents on local/global ontologies Case II  Assignment is done on the global ontology by the Ontology Designer

17 Matchmaking Matching inputs – outputs Match levels exact > plug-in > subsume > fail

18 Matchmaking Assigning values for matching types  Exact =1  Plug-in = 0.8  Subsume = 0.5  Fail =0. Level of match  Minimum of the set of matches for inputs and outputs

19 General Concepts of Similarity Subsumption is determination of subconcept and superconcept relationships between concepts of a given ontology More generel concept called subsumer and more specific concept the subsumee Vehicle Car Sedan Vehicle Car Sedan Case I Case II S :Searched For S S Vehicle Car Sedan Case III S

20 General Concepts of Similarity II Axiom I : Most strongest match is where advertised concept match with the requested concept exactly. Axiom II : For the search result concepts under the target concept, the one that is upper in the ontologic representation is preferred. Axiom III : For the concepts over the target concept, the one that is closer to the searched concept which is in the lower part of the ontologic representation is chosen. Vehicle Car Sedan Vehicle Car Sedan Vehicle Car Sedan

21 Semantic Distance Weight Assignment the rate of coverage of sub-concepts for each concept in relation to subClassOf. done by sub-ontology managers Representation:  a tuple relation : SD = (parent_concept, subclass_concept, similarity)

22 MatchmakingAlgorithm Service Requestor Serv. Req (owl-s) Sem. Dist. File(*.sd) + Serv. Adv. (owl-s) Input Filtering Output Filtering Pre/Post Con Filtering Service Cat. Filtering MS-MatchMaker Maximum Result Size Plug-in Filters Service Provider

23 Algorithms Concept/Domain matching Input/Output matching Pre/Post condition matching Add-Value matching Level of Filtering Applied Maximum Result Size

24 Sample Weight Assignments on Ontologies For sample scenarios and test cases following ontology and semantic distance assigments are used Press Book TechnologyBooksHistoryBooks ComputerElectronics Pre Middle Close 1 1/2 1/3

25 Simulation Scenario 1  Search for : input : Price output : ComputerBooks Computer engineering student, mostly interested in computer books.It is not a strict rule given and open to other types of books offer and I have some preferences on these kind of books

26 Simulation Scenario 2  Given Price, return list of Electronics and Pre(Histroy) books

27 Scenario web services registered in the matchmaker 10 of them related with the context of BuyBookService, others not related maximum result set size to 5  No other constraints given  Strict matching  Assume 8 services still match -> top 5 returned

28 Comparasion with other Matchmakers FrameworkLARKSOWL-S Matchmaker Lei Lui ’ s Framework MS-Matchmaker LanguageITLOWL-SDAML-SOWL-S RepositoryLocal KBUDDI Service Category Filter xxx Input Filter xxxx Output Filter xxxx Pre/post Condition Filter xxxx Plug-in Filter xx Semantic Dist. Usage partial x Ranked List xx Type Based List xxxx

29 Conclusion A novel web semantic web service discovery framework is proposed with sematic distance information usage Ranking of services is realized using ontological parent-child relations Layered, extandable, simple matching algorithm A new Partial Result Set concept introduced

30 Future Work Quality of services can be integrated Similarity concept can be widened to properties, constraints etc. Mediation can be analized an integrated in a detail manner Complex ontologies, services, scenarios are required to validate the evaluation of semantic distance information usage Performance and security can be integrated to the framework