iSSE an intelligent engine for user oriented web service selection

Acknowledgements First of all, I feel deeply indebted to my supervisors Associate Professor Pung Hung Keng and Dr Ng See-Kiong, without whom the completion of this thesis could not have been possible I would like to take this opportunity to express my deepest appreciation and sincere gratitude to them for their inspiring guidance, advice and kindly patience I am grateful to the team-workers in M-Comm group of NSS (Network Systems & Services) lab for their innovative thinking and works during the project I am also grateful to Haman Lee, Zhang Zhuo and all my colleagues in the Knowledge Discovery Department of the Institute for Infocomm Research (I2R) for their valuable instruction and generous assistance, which have been a great source of help I am grateful to He Jun, Zhou Lifeng, Peng Bin and Gu Tao in NSS lab who have been always encouraging, supporting and helping me during my postgraduate study I gratefully acknowledge the financial support from I2R, the Agency for Science, Technology and Research and National University of Singapore for the duration of this project Otherwise, I would not be able to undertake my further study on this project Finally, I want to show my deep appreciation to my boy friend for his constant caring and support throughout my life There are many others who have assisted me in various ways during this project I gratefully acknowledge their help I Table of Content ACKNOWLEDGEMENTS I TABLE OF CONTENT II LIST OF TABLES V LIST OF FIGURES .VI SUMMARY .VIII CHAPTER INTRODUCTION 1.1 RESEARCH PROBLEM 1.2 PROPOSED APPROACH 1.3 CONTRIBUTIONS 1.4 THESIS OVERVIEW CHAPTER BACKGROUND AND RELATED WORK 2.1 WEB SERVICE ARCHITECTURE AND STANDARDS 2.1.1 Web Service Architecture 2.1.2 Web Service Standards 11 2.2 WEB SERVICE SELECTION 15 2.2.1 UDDI for Service Selection 16 2.2.2 Service Selection in B2C 17 2.3 RELATED WORK 18 2.3.1 Semantic Annotations to Web Service 18 2.3.2 Semantic Annotations to UDDI 23 2.3.3 Semantic Annotations to WSDL 24 2.3.4 Semantic Annotations to Ontology 26 2.3.5 User-Oriented Web Service Selection 27 II 2.4 OUR APPROACH – A PREVIEW 28 CHAPTER SERVICE SELECTION IN MOBILE COMMERCE 30 3.1 3.1.1 3.2 THE M-COMM SYSTEM 30 M-Comm User Scenarios 33 SERVICE SELECTION IN M-COMM 34 3.2.1 Service Selection System Architecture 35 3.2.2 Discussions 38 CHAPTER INTELLIGENT WEB SERVICE SELECTION ENGINE (ISSE) 40 4.1 SERVICE DESCRIPTION BY TEXTUAL INFORMATION 44 4.1.1 Text-based Web Service Representation 45 4.1.2 Representing Web Services by Words 47 4.2 DOMAIN IDENTIFICATION USING SVM 49 4.2.1 Motivation 49 4.2.2 Domain Modeling 51 4.2.3 Domain Identification 59 4.3 SERVICE SELECTION 62 4.3.1 Ontology Concept Annotation 62 4.3.2 Target Service Template Construction 64 4.4 CONCLUSION 66 CHAPTER EVALUATION 68 5.1 SYSTEM IMPLEMENTATION 68 5.2 DATA 69 5.3 EVALUATION 73 5.3.1 Selection accuracy 73 5.3.2 Time latency 78 5.4 CONCLUSION 79 III CHAPTER CONCLUSIONS 80 APPENDIX A: UDDI DATA STRUCTURE 83 A.1 DATA STRUCTURES IN UDDI 83 A.2 AN EXAMPLE 90 APPENDIX B SAMPLE OWL FOR POSTAL CODE SERVICE 92 APPENDIX C THE M-COMM SYSTEM 97 C.1 SYSTEM ARCHITECTURE 97 C.2 SYSTEM DEVELOPMENT MODEL AND METHODS 99 APPENDIX D M-COMM SERVICE DISCOVERY SUB-SYSTEM DATA FLOW 103 REFERENCE 107 IV List of Tables Table4.1 Presidential Election Results 2004 – Service Presentation 47 Table4.2 Two Web Service Presented in iSSE 57 Table 4.3 Sample Features in Postal Code Service 58 Table 4.4 Mapping Question Words to Noun 60 Table 5.1 System Environment 68 Table5.2 Domain Categories and number of services in each domain 70 Table 5.3 OWL design for Postal Code Service 71 Table5.4 Precision and Recall in Service Selection Process of iSSE 74 V List of Figures Fig 2.1, Service – Oriented Web Services Architecture 10 Fig2.2 Web Service Technology Stack 12 Fig3 M-Comm System for Shopping - General Structure 32 Fig3 Components of M-Comm Service Discovery Engine 36 Fig4.1 Web Service Discovery in M-Comm System 42 Fig 4.2 iSSE System Process and Components 42 Fig 4.3 Distribution of # of Words and Occurrence 58 Fig5.1 Distribution of Words and Occurrences from the Testing Data 72 Fig 5.2 Precision Comparison between iSSE and SalCentral.com 75 Fig5.3 Recall Comparison between iSSE and SalCentral.com 75 Fig A.1 Five Data Structures defined in UDDI Data Structure Specification 2.03 84 Fig A.2 BusinessEntity data Structure 84 Fig.A.3 BusinessEntity Example 85 Fig A.4 BusinessService Data Structure Specification 85 FigA.5 BusinessService Example 86 Fig A.6 BindingTemplate structure specification 87 FigA.7 BindingTemplate Example 87 Fig A.8 tModel Data Structure Specification 88 FigA.9 tModel Example 89 Fig A.10 CategoryBag Data Structure Specification 90 FigA.11 CategoryBag Example 90 VI FigA.12 Example of UDDI data structure 91 Figure C.1 System architecture diagram 97 Figure C.2 Technology vs System architecture 101 Figure C.3 Deployment View of M-Comm System 102 Fig D.1 Level 0: DFD Diagram for Service Discovery Sub-system 103 Fig D.2 Level DFD Diagram for Service Invoking 104 Fig D.3 Level DFD Diagram for Query Process 105 VII Summary The World Wide Web, once just a simple repository of hyper-linked web pages, is now rapidly evolving into a provider of services Although web services were originally designed for B2B scenarios, as the popularity of the web increases, there is a growing need to adapt web services for B2C scenarios Current web service standards require the user to be conversant with the high technicalities associated with web services in order to select the relevant web services to apply However, B2C scenarios are inherently dynamic and heterogeneous—useroriented processes are necessary in order for web services to be useful In this thesis, we investigate the challenging problem of providing an intelligent engine for user-oriented web service selection in a B2C environment Our main contribution in this thesis is a novel light-weight semantic approach that utilizes machine learning and information retrieval techniques to identify web services that are relevant to a user’ query in free text Our approach treats web services as textual web pages from which their semantic content can be extracted using natural language text processing methods We employ a Support Vector Machine to identify the domain of the web services requested by a user’s query, and we then map the semantic content of the user’s free text query to the semantic content of the domain’s web services’ ontology concepts to select a list of relevant web services in the identified domain for the user We have implemented VIII our intelligent web service selection method as part of the service discovery engine in the M-Comm system Using actual web services from xMethods, we show that the iSSE (Intelligent Service Selection Engine) can accurately select relevant web services for different domains IX Chapter Introduction To begin, we provide the motivation of our thesis work in this introductory chapter We also give an overview of our proposed approach and our research contributions in this work 1.1 Research Problem The World Wide Web—once just a simple repository of hyperlinked web pages— is now evolving into a provider of services, or more specifically, “web services”.[1] Web services are automated resources that can be accessed via the Internet—they have been hailed as the next wave of internet based applications that will dramatically change the use of internet An increasing number of web services have now been published to several service registries Web users can use these published web services to perform many everyday information retrieval tasks and access the information providers’ technology platform directly For example, we can automate our access of catalog data at the online bookstore Amazon.com, create and populate an Amazon online shopping cart (a book sale platform), and even initiate the electronic checkout process by using the web services provided by Amazon (AWS, or Amazon.com Web Service) [4] The Figure C.3 Deployment View of M-Comm System 102 Appendix D M-Comm Service Discovery Sub- System Data Flow Figure D.1 shows the DFD diagram of the M-Comm Service Discovery Subsystem Fig D.1 Level 0: DFD Diagram for Service Discovery Sub-system Process in Figure D.1 is the process that receives service parameters from M-Comm, package as SOAP message and invoke the services on shop-side and return the result back to M-Comm (Figure D.2) 103 Fig D.2 Level DFD Diagram for Service Invoking • The Message Parser receives the parameters from M-Comm server and translates into XML-format parameters that the Service Invoker can handle At the same time, the Message Parser is in charge of translating the returned service results into XML formats and returns the XML formatted results to the M-Comm server • The Service Invoker gets the service parameters from message parser, invokes the web services at the shop-side, and gets the result back and transfer the result the Message Parser Process in Figure D.1 is the process to receive the query XML string from MComm, and return the candidate services back to M-Comm server (Fig D.3) 104 Fig D.3 Level DFD Diagram for Query Process • The query message is parsed by the Message Parser Process At the same time, the Message Parser is in charge of packaging and returning the candidate services to M-Comm • After parsing, the parsed query message is passed to the Query Preprocess to the traditional information retrieval process • The query string is then passed on to the Inference Process The Inference Process retrieves the rules and entities from knowledge base, and use them to retrieve candidate service information from the service storage database and then returns the candidate services to the Message Parser 105 Process in Figure D.1 is the process to receive the registry request and information from shop side and store the information into Service Registry Database 106 References [1] Sheila A McIlraith, Tran 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[66] Protégé website: http://protege.stanford.edu/index.html [67] S T Dumais, J Platt, D Heckerman, and M Sahami Inductive learning algorithms and representations for text categorization In CIKM-98: Proceedings of the Seventh International Conference on Information and Knowledge Management, 1998 116 [...]... selection engines, and then return (or even invoke) 15 a list of relevant candidate web services for the user Some efforts have been attempted in adapting the current B2B-focuses web services standards for useroriented service selection [58, 59, and 60] In the next section, we discuss the pros and cons of using the current web service protocol—UDDI for service selection tasks 2.2.1 UDDI for Service Selection. .. with web services, an efficient and precise service discovery and selection mechanism for web services is necessary Web service selection is a process to automatically select one web service or a list of services according to users’ requirement So far, research in the area of web services mainly focuses on B2B web services with well defined interfaces and terminologies defining a shared meaning for. .. syntactic meaning in web service, there are also some semantic descriptions about the web service that can be used for intelligent service selection For example, the names of the operations, the structures of the web services, and the relationships between the various parameters can be very informative for service selection There are approaches in semantic web services that employ WSDL as a means for applying... that semantic annotations can be introduced into UDDI to enable common semantic understanding between the service providers and services requesters 23 These approaches to semantically extend UDDI for enhanced service selection ability try to seek a balance between the UDDI compliance and semantic annotation On one hand, they share the same disadvantages from semantic web services On the other hand, they... key standards in Web service architecture They define the protocol of Web Services for messaging, service description and service registry & selection respectively 2.1.1 Web Service Architecture The web service architecture is a Service -Oriented Architecture (SOA for short), which is also the structure that Grid computing is now based on Three key roles – Service Registry, Service Provider and Service. .. provide services that facilitate the development, deployment, and usage of web services and web service network 3 1.2 Proposed Approach We present a novel automated web service selection method that utilizes machine learning and information retrieval techniques to identify relevant web services based on a users’ service query in free text format Our approach is to treat web services as textual web pages... keywords and the search engine searches the web pages stored in their database to find the web sites that contain some or all of the user query keywords The new user- oriented web service selection engines will have to parse users’ input requirements in free text for some specific task, and translate them into appropriate service requirement specifications that can be understood by the web service selection. .. uniform presentation of the services, as in the case of B2B scenarios Domain selection is the first step of B2C web service selections o Lacking common language between service providers and human users: In B2B scenario, a common language, such as taxonomies and specifications, is introduced to seek a common understanding between service providers and service requesters In a B2C scenario, human users... XML-based messaging exchanging protocol, and the underlying network protocol can be HTTP, SMTP, and so on 2.1.2 Web Service Standards To ensure interoperability, there exist published standards for performing the actions on web service architectures for operations involved in business interactions The standard web service stack is shown in Figure 2.2 The various web service standards are mainly categorized... Chapter 4 for details) 2.2.2 Service Selection in B2C Initially, web services are deployed as one of the most important standards in SOA (Service Oriented Architecture) to seamlessly integrate in B2B scenario Thus major research emphasis and board interest in standardlizing in web services are in applying web services in B2B scenario, whist human user interaction are omitted or taken for granted With ... been developed for semantic web services: OWL-S (formerly DAML-S) [24, 25, 26, 27, 28], and LARKS [29, 30, 31] OWL-S OWL-S (DAML-S) is a web service ontology language developed for describing... matched service from the storage In this thesis, we implement an intelligent web service selection engine for M-Comm’s service discovery subsystem We call our system iSSE for intelligent Service Selection. .. of relevant candidate web services for the user Some efforts have been attempted in adapting the current B2B-focuses web services standards for useroriented service selection [58, 59, and 60]