Xây dựng hệ thống so trùng dịch vụ web

Đạ i họ c quố c gia Tp HồChí Minh TRƯỜNG ĐẠI HỌC BÁCH KHOA - LUẬNVĂN THẠC SỸ XÂYDỰNG HỆTHỐNG SO TRÙNG DỊ CHVỤWEB Họcv i ê n:LêDuyNgạn Gi ovi ê nhướn gdẫ n : Gi os ưAngela Goh (NTU, Singapore) Ti ế nSỹCaoHo àngTr ụ( ĐHBK TP.HCM, Vi ệ tNa m) 12/7/2005 NANYANG TECHNOLOGICAL UNIVERSITY Building a Matchmaker for Web Services A first year report Submitted to the School of Computer Engineering of Nanyang Technological University for Ph.D conformation By Le Duy Ngan Supervised by Professor Angela Goh and Co-supervised by Dr Cao Hoang Tru July 12, 2005 Tóm tắ t Các dị ch vụweb (Web services), đặ c biệ t dị ch vụWeb ngữnghĩ a, kỹ thuậ t quan trọng hệthố ng thương mạ iđiệ n tử Những dị ch vụWeb ngữnghĩ a xây dựng ontology, đặ c tảgồm khái niệ m mối quan hệgiữa khái niệ m, từđócó thểsuy diễ n tri thức Sựphát triể n nhanh chóng sựtồn tạ imột sốlượng lớn dị ch vụWeb hiệ n tạ i đãdẫ n đế nmột nhu cầ u vềtìm kiế m dị ch vụWeb UDDI cơng cụđiề n hình loạ idị ch vụtìm kiế m khơng hỗtrợngữnghĩ a Trong đó, hỗtrợngữnghĩ alà rấ t quan trọngtrong hệthống tim kiế m Đểvượt qua khuyế t điể m này, nhà nghiên cứu đãphát triể n hệ thống so trùng (Matchmaker) Những hệthống so trùng có thểđá p ứn g tốt dị ch vụWeb cung cấ p dị ch vụWeb tìm kiế mđ ề u sửdụng chung ontology, chúng không hỗtrợkhi dị ch vụWeb sửdụng ontology khác Vì , mặ c dù nế u dị ch vụWeb cung cấ p có thểđá p ứng yêu cầ u dị ch vụWeb yêu cầ u việ c so trùng trảvềkế t quảlà sai Trong đó, thếgiớithực nhà cung cấ p dị ch vụvà nhà yêu cầ udị ch vụlà khác hoạ t động độc lậ p nên họthường dùng ontology khác đểmô tảdị ch vụWeb củamình Luậ nvă nnà ygiới thiệ u mộthệthống so trùng mà hỗtrợnhững dị ch vụWeb sử dụng ontology khác Luậ nvă ntrình bày giả i thuậ t so trùng hỗt rợnhữngdị ch vụWeb sửdụng ontologies khác kiế n trúc hệthống cho giả i thuậ t Những thành phầ n hệthống so trùng bao gồm: kỹthuậ t phân loạ i vă n bả n, kỹthuậ t xem xét hai khái niệ m có xuấ t phát từmột ontology hay khơng, kỹthuậ t tính độ giốngnha ucủa khái niệ m Một ứng dụng dây chuyề n cung cấ p (supply chain) giới thiệ u sửdụnghệthống so trùng mà đềnghị Việ c mởrộng hệthống so trùng bằ ng cách sửdụng kỹthuậ t kế t nốicác dị ch vụWeb đểđá p ứng nhu cầ u so trùng thực hiệ n ởhai nă m tiế p theo củachương trình tiế ns ỹ i Acknowledgements After one year‟ sha r dwor k , I have obtained preliminary results to contribute to the scientific community During this time, I have received a lot of help, guidance, and encouragement from many individuals The research can not be completely successful without their support This report is a good opportunity for me to express my thanks to them First and foremost, I would like to express my special gratitude towards my supervisor, Professor Angela Goh, for her invaluable advice and enthusiastic help Although very busy, she spends much time advising me and revising my errors in this report and papers During the time working with her, I not only learn how to academic research but also many good characteristics from her Second, I wish to express my thanks to people who shared their ideas, gave their comments, and spent their time in discussion with me They are Quan Thanh Tho, Do Tien Dung, and Zhou Chen in NTU; all members of IMSS pilot project in SIMTech especial Chong Minsk, Siew Poh, Ramasamy; and Michael C.Jaeger in Berlin University Next, I wish to thank Dr Cao Hoang Tru, my co-supervisor in Vietnam, for his help and advice I would like to thank Vietnamese teachers who recommend me to receive the scholarship which has changed my life I would like to express my gratitude to Dr Duong Tuan Anh and Dr Nguyen Van Hiep who wrote recommended letter to NTU I wish to thank NTU for the financial support through the scholarship Finally, I am grateful to my parents, my younger sister Le Thuy Ngoc, and my closest friend Nguyen Hong Khoat in Vietnam for their love and encouragement, which has helped me overcome many difficulties faced during the research Singapore, July 12th 2005 Le Duy Ngan ii Table of Contents Chapter 1: Introduction 1.1 Web Service and Semantic Web Service 1.1.1 Web Services 1.1.2 Semantic Web Service 1.2 Motivations and Objectives 1.2.1 Problem definition 1.2.2 Scope and objectives 1.3 Outline of the report Chapter 2: Related technologies 2.1 Semantic web architecture 2.2 Introduction to Ontology 2.3 Web Services 10 2.3.1 Web Services Standards 10 2.3.2 Web Services Model 13 2.4 Semantic Web Services 14 Chapter 3: Literature Review 18 3.1 A survey of web services discovery 18 3.1.1 Web services discovery systems 18 3.1.2 Discovery of web services using the same ontology 19 3.1.3 Discovery for web services using different ontologies 24 3.2 Web services discovery problems and approaches 25 3.2.1 Matching web services using different ontologies 25 3.2.2 Matching semantic web services against non-semantic web services 26 3.2.3 Matching semantic web services that use different description languages 28 iii Chapter 4: MOM Framework 29 4.1 MOM algorithm 29 4.1.1 Storing advertised web service information in MOM 29 4.1.2 Basic terminology 31 4.2 The four stages matching algorithm 33 4.2.1 User-defined Matching 34 4.2.2 Input matching 35 4.2.3 Output matching 38 4.2.4 Operation matching 39 4.2.5 The final matching result 40 4.3 The algorithm for web services using different ontologies 42 4.4 MOM Architecture 45 4.4.1 MOM components 45 Chapter 5: Main modules in the MOM 48 5.1 Relationship of two concepts from different ontologies 48 5.1.1 Ontology replications in the internet 48 5.1.2 Checking if two concepts from different ontologies are related 49 5.2 Computing the semantic similarity of two concepts 50 5.2.1 Previous work on computing semantic similarity 51 5.2.2 Our approach to compute the semantic similarity 54 5.3 Text clustering 56 5.3.1 Text Processing 56 5.3.2 Clustering methods 58 5.3.3 Choosing Text Clustering Approach 60 Chapter 6: A Scenario using MOM 63 6.1 A Supply Chain Application 63 6.2 An Example 65 6.2.1 Same ontologies used 65 6.2.2 The requester and providers use the same ontology 69 6.2.3 The requester and providers use different ontologies 70 iv Chapter 7: Conclusions and future work 73 7.1 Conclusions 73 7.2 Future work 74 7.2.1 An extension of MOM 74 7.2.1.1 Using web services composition for discovery 74 7.2.1.2 Clustering ontologies to improve MOM 78 7.2.2 Implementation and state of components 78 7.2.3 Future work schedule 80 Schedule of 2005 - 2006 82 Schedule of 2006 - 2007 83 Reference: 84 Publication 90 v List of Figures Figure 2.1: The Semantic Web Architecture [72] Figure 2.2: A WSDL Example 11 Figure 2.3: Web service roles, operations, and artifacts 13 Figure 2.4: Top level of the service ontology 16 Figure 3.1: Taxonomy of web service discovery systems 18 Figure 4.1: The relation of concepts 32 Figure 4.2: Four stages matching algorithm 41 Figure 4.3: Matching Algorithm 43 Figure 4.4: MOM Engine 47 Figure 5.1: The relationship of two ontologies 49 Figure 5.2: The different context of the Laptop concept 55 Figure 5.3: Comparison process by similarity score 58 Figure 5.4: A hierarchical clusters 59 Figure 6.1: A supply chain model using MOM 65 Figure 6.2: Ontology in eBusiness domain 66 Figure 6.3: Ontology for computer device domain 67 Figure 6.4: Ontology for price domain 68 Figure 6.5: Requested and advertised services 69 Figure 6.6: Ontology for computer hardware device domain 71 Figure 6.7: Toshiba request service 71 Figure 7.1: Proposed Extension to MOM 77 Figure 7.2: The status of MOM components 80 Figure 7.3: Research schedule for the second year 82 Figure 7.4: Research schedule for the third year 83 List of Tables Table 3.1: Advantages and disadvantages of three approaches to semantic web discovery based on the same ontology 23 Table 4.1: The information of web service providers is advertised in MOM 30 Table 4.2: Degree similarity for input matching 38 Table 4.3: Degree of similarity for output matching 39 vi Chapter 1: Introduction The World Wide Web is considered one of the greatest inventions of the twenty-first c e nt ur y I ti sc a l l e d“ ar e vol ut i onofi nf or ma t i on”s i nc ei tpr ovi de sne w wa y st os r e , search, and publicize information easier and more conveniently than ever before HyperText Mark-up Language (HTML) [71] is one of the most important reasons behind its success Most web documents formatted using HTML, link documents to other documents Currently, the World Wide Web has billions of web pages and continues to increase rapidly As a consequence, there has been an enormous demand to have mechanisms to retrieve information Search engines such as Google [25], Yahoo [77], Excite [20], etc have been developed to satisfy this demand However, the documents are formatted in HTML and support searching only by keywords There are three main disadvantages Firstly, searches produce a large number of results, many of which are inaccurate Additionally, users must select from a large number of links in the results manually Furthermore, without linguist matching, synonyms of keywords are ignored and hence, the results are inaccurate and incomplete To overcome these problems, Tim Berners-Lee, the inventor of the World Wide Web, has coined the term “ Semantic Web”[5] The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation." Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web, Scientific American, May 2001 The Semantic Web is the next generation of the web and is an extension of the current World Wide Web It attempts to overcome the drawbacks of the current Web by using metadata to describe the semantics of the data When metadata is used to mark-up data, the data can be not only human readable but also be understood by machines An ontology is a specification of classes or concepts and their relationships, with constraints CHAPTER 1: INTRODUCTION that can be used to represent and infer knowledge, and shared by the communities or programs These ontologies are used as metadata in the Semantic Web and provide many advantages such as knowledge representation and reasoning, and re-use and sharing of such knowledge between programs By using ontologies to describe knowledge, the web can be brought to its full potential This introduction presents technologies that are key to the project, namely web service and semantic web service This leads to the motivations and objectives of the project, followed by an outline of the report 1.1 Web Service and Semantic Web Service 1.1.1 Web Services Web services are an emerging technology that enables e-business and e-commerce to become a reality This is achieved through its support of discovery, composition, invocation, monitoring and so on Web services have become a competitive tool of companies in business Firstly, they allow companies to reduce cost by fast, effective, and reliable services to customers, suppliers, and partners over the internet Secondly, they enable business operations to function more efficiently via the web and enhance business opportunities to companies A web service is a software component representing a specific business function that can be described, published and invoked over the network (typically Internet) using open-standards It uses a remote invocation method based on XML [73] for its binding, and supports both asynchronous and synchronous interaction A web service has the following features: platform independence, internet scoped, loosely coupled, easy interaction The web service requesters and providers not need to run on the same platform Web services can run on different networks (Internet, I nt r a ne t…)a nd di f f e r e ntope r a t i ng s y s t e ms ( Wi ndows ,Li nux …) ,a nd c a n be i mpl e me nt e dus i ngdi f f e r e ntpr og r a mmi ngl a ng ua ge s( J a va ,C,C++…) A we bs e r vi c e has a global scope and can be invoked over the internet, including computers that are firewalled CHAPTER 7: CONCLUSIONS AND FUTURE WORK A technique to store, index, and retrieve potential advertised web services from the temporary database and checking the ability to compose web services from other clusters Perform web services composition using different ontologies To perform composition, we also need to integrate web services using different ontologies 76 CHAPTER 7: CONCLUSIONS AND FUTURE WORK Advertised Service Requested Service Text Clustering two web services use the same ontology ? False True Semantic Similarity Input Matching Expected input Degree Operation Matching Userdefine Matching Filter Expected output Degree Filter Expected operation Degree Filter And Web services Composition Compute Semantic Similarity Yes Ability to compose ? Temporary database U S E R S Output Matching False Yes Can satisfy part of requirement ? return Fail No Semantic Similarity > threshold ? True Sorted Result Figure 7.1: Proposed Extension to MOM 77 CHAPTER 7: CONCLUSIONS AND FUTURE WORK 7.2.1.2 Clustering ontologies to improve MOM Motivation: When using txtDescription and serviceName to classify web services, there are some issues to consider If we use a large number of clusters, mismatch may occur because the number of web services in the cluster is small and the requested service may belong to more then one cluster If we assign the requested web service in a certain cluster, mismatch may happen If we use a small number of clusters, the number of web services per a cluster may be large This can avoid the mismatch mentioned above However, it is time consuming to match the requester with all the advertised services in the cluster We can overcome this problem by clustering ontologies The two web services will only be matched if they are in the same text cluster as well as in the same cluster of ontologies Otherwise, the matching fails 7.2.2 Implementation and state of components The MOM framework uses some available open source code components The rest of the components will be developed based on the algorithms described in earlier chapter Figure 7.2 presents the status of the components in the MOM architecture ServiceProfile Parser (1): is used to parse both advertised and requested services information OWLSParser from TUB [47] can be used for this purpose Text Clustering (2): txtDescriptions and serviceNames which are used in clustering are taken from ServiceProfile Parser The text clustering algorithm was described in chapter which is combination of two algorithms: k-means and first variation The source code of k-means is available and will be extended using the first variation algorithm Load Ontologies (3), Reasoner (4) The “ Loa di ng Ont ol og i e s ”c ompone ntis responsible for loading ontologies and parsing them into classes, relationships, and constraints, etc Jena [33], a Java framework for building semantic web applications, can be used for this purpose Then this information will be used by the reasoner to compute the similarity of two concepts OWLJessKB [22] and RACE [27] are reasoners which can satisfy the requirements of component Since OWLJessKB 78 CHAPTER 7: CONCLUSIONS AND FUTURE WORK includes Jena [33] and Jess [40], the Java Expert System Shell, we intend to use OWLJessKB for components (3) and (4) Checking Ontologies (5), Computing Semantic Similarity (6), Matching and Sorting results (7) are components whose algorithms were described in chapters and will be implemented 79 CHAPTER 7: CONCLUSIONS AND FUTURE WORK Web service provider Web service requester MOM Architecture Advertised Information Requested Information txtdescription and serviceName (1) ServiceProfile Parser Advertised Information Ontology of requester Advertisement information database Text/Cluster (2) Text Clustering (3) Load Ontologies SVO-Facts (4) Reasoner (Read SVO-Fracts and perform inferring) SVO-Fract (1) SVO-Facts (2) (6) Computing Semantic Related ontologies? Similarity (5) checking Ontologies (Read SVO to checking if the two ontologies are related) Semantic similarity between concepts The same ontology Web service information Results to requestor Ontologies Semantic distance (7) Matching and Sorting results Source code available To be implemented Figure 7.2: The status of MOM components 7.2.3 Future work schedule The main work in the next two years includes the design of algorithms and implementing the extension of MOM, implementing the components which were discussed above, and writing the thesis In the next two years, we will focus on the extension described in 80 CHAPTER 7: CONCLUSIONS AND FUTURE WORK 7.2.1.1 The second extension will be considered if there is sufficient time In using web services composition technologies for discovery purposes, the expected outcomes are: Checking if an advertised web services can satisfy part of the requirement of the requested web service To this, we need to have a method that can check if an operation of the advertised web services is more general instead of more specific than the operation of the requested web services Fuzzy technologies and machine learning can be considered to solve this problem Even though there has been much work on indexing web services, the specific need of de t e r mi ni ngt he„ c ompos i bi l i t y ‟ofwe bs e r vi c e sr e qui r e snove lt e c hni que s to store, index, and retrieve web services To perform composition, we also need to integrate web services using different ontologies There has been much work on web services composition but none for web services using different ontologies Based on the above, the schedule is described below and in figures 7.3 and 7.4:  For the next seven months (August 2005 to February 2006), we mainly focus on the implementation of components of MOM As discussed above, we have open source code of components 1, 3, 4, and part of component For the remaining components 5, and 7, algorithms have already been formulated  The following nine months (March to December 2006) will focus on the extension to MOM as described in section 7.2.1  The next two months (January to February 2007) will be spent on integrating and testing the system  In the last five months (March to July 2007), the final thesis will be written 81 CHAPTER 7: CONCLUSIONS AND FUTURE WORK Schedule of 2005 - 2006 Aug Implement text clustering Sep 2005 Implement computing semantic similarity between two concepts Oct Nov Implement checking ontology Dec Jan Fab Implement four -stage matching Integration and testing system Mar 2006 Apr Algorithm for checking if a web service can partially satisfy requirement Implement algorithm May Jun Technique for storing and indexing web service for convenient retrieval Jul Figure 7.3: Research schedule for the second year 82 CHAPTER 7: CONCLUSIONS AND FUTURE WORK Schedule of 2006 - 2007 Aug Algorithm for checking the ability to compose web service Implement algorithm Sep 2006 Oct Algorithm for web service composition for different ontologies Implement algorithm Nov Dec Compute semantic similarity of composed web service and requested web services Jan Fab Integrate, test, and enhance the system 2007 Mar Apr May Writing of final thesis Jun Jul Figure 7.4: Research schedule for the third year 83 Reference: [1] Angell, R., and Freund, G (1983) Automatic spelling correction using a trigram similarity measure In Information Processing and Management, 19(4), 255-161 [2] Baader, F et al (2003) The Description Logic Handbook: Theory, Implementation and Applications Cambridge University Press: ISBN-10: 0521781760 | ISBN-13: 9780521781763, p 574 pages [3] Bayardo, R et al (1997) Semantic integration of information in open and dynamic environments In Proceedings of the 1997 ACM International Conference on the Management of Data (SIGMOD), Tucson, 195-206 [4] Berkhin, P (2002) Survey of Clustering Data Mining Techniques In Technical Report, San Jose, CA, Accrue Software [5] Berners-Lee, T., Hendler, J., and Lassila, O (2001) The Semantic Web In Scientific American, 284(5), 34-43 [6] Boley, D (1998) Pricipal Direction Divisive Partitioning In Data Mining and Knowledge Discovery, Kluwer Academic Publishers, 1384-5810, 2(4), 325-344 [7] Bratko, I (2000) PROLOG Programming for Artificial Intelligence third edition, Pearson Education Limited [8] Cardoso, J., and Sheth, A (2003) Semantic e-Workflow Composition In Journal of Intelligent Information Systems, Kluwer Academic Publishers, 0925-9902, 21(3), 191 - 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(supply chain) giới thiệ u sửdụngh? ?thống so trùng mà đềnghị Việ c mởrộng h? ?thống so trùng bằ ng cách sửdụng kỹthuậ t kế t nốicác dị ch v? ?Web đểđá p ứng nhu cầ u so trùng thực hiệ n ởhai nă m tiế