Governments enhance their attempt to offer efficient, advanced, and modern services to their users (citizens and businesses) based on information and computer technologies and especially the Web. E-government “fashion” is radically expanding through the world and to several governmental sections (ministries, public authorities, departments, etc.). The remarkable acceptance of this powerful tool has changed the way of conducting various activities and offers citizens, businesses, and public authorities limitless options and op- portunities. However, the emerging problem is not the provision of users with access to the e-governments’ information and services, but the provision of users with the right informa- tion and service according to their specific needs and preferences. To this direction, Web personalisation and Web mining, especially Semantic Web mining, are used for supporting tailored Web experiences.
These techniques appear the most promising for the future, since they help to establish one- to-one relationships between users and governments, improve the performance of provided
Markellos, Markellou, Panayotak, & Tsakalds
information and services, increase users’ satisfaction, and promote their loyalty for the e- services provided. On the other hand, governments take advantage of them, as long as they save costs (e.g., transactions, communication, task management, etc.), improve response times, automate various processes, provide alternative channels of cooperation and com- munication, and upgrade and modernise their profile and image.
Many research and commercial approaches, initiatives, and tools are available, based on Web site structure and contents, user’s navigation, behaviour and transaction history, server log files, and so forth. In order for the e-government to succeed, all techniques must take into consideration the problems that may encounter. In the case of personalisation, it will certainly benefit both providers (governmental authorities) and users (citizens, businesses, and other governmental authorities) to take into consideration the fact that personalisation requires rich and qualitative data in order to provide successful output. This is not always feasible, since many users are often negative towards the idea of being stereotyped. Ad- ditionally, personalisation requires a flexible Web structure, which will easily handle the Web site content update as well as the Web structure update.
Unfortunately, there is an extraneous factor that affects personalisation goal, which is the user itself. Many users are reluctant to give away personal information, are hesitant to visit Web sites that use cookies, or avoid disclosing personal data in registration forms. Users commonly consider their privacy in jeopardy since their personal data and the activities taken are all recorded in a Web site. They are skeptical whether e-government actually provides a strict and safe privacy and security policy for their data. In case e-government does provide a certain policy, users are sceptical whether the policy is undoubtedly safe and secure of any threats, hackers’ attacks, and whether the information collected on them is not mishandled by certain unauthorised people (governmental employees or external users).
Summarising, governments should work hard in the direction of providing the legal framework to ensure the protection of users’ privacy and to eliminate the possibility of misuse of their personal information. Moreover, governments should focus on dealing with data security issues, to avoid unwanted leaks of personal information. In this way, any extraneous fac- tors will be eliminated and personalised public e-services will lead the future governmental model, suspending the existing bureaucratic model that “crucifies” not only citizens and businesses but also intergovernmental transactions.
References
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender sys- tems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge Management and Data Engineering, 17(6), 734-749.
Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Scientific American, 284(5), 34-43.
Borges, J., & Levene, M. (1999, August 15). Data mining of user navigation patterns. In M. Spyliopoulou, J. Borges, & M. Levene (Eds.), Web Usage Analysis and User
Semantc Web Mnng for Personalzed Publc E-Servces Profiling: International WEBKDD’99 Workshop, San Diego, CA (pp. 31-36). Berlin;
Heidelberg: Springer-Verlag.
Cooley, R. (2000). Web usage mining: Discovery and application of interesting patterns from Web data. PhD thesis, University of Minnesota, Department of Computer Science.
Dai, H., & Mobasher, B. (2005). Integrating semantic knowledge with Web usage mining for personalization. In A. Scime (Ed.), Web mining: Applications and techniques (pp.
276-306). Hershey, PA: Idea Group Publishing.
Davies, J., Fensel, D., & Van Harmelen, F. (2003). Towards the Semantic Web, ontology- driven knowledge management (pp. 1-9). Chichester; West Sussex, UK: John Wiley
& Sons.
Eirinaki, M., & Vazirgiannis, M. (2003). Web mining for Web personalization. ACM Trans- actions on Internet Technology (TOIT), 3(1), 1-27.
Etzioni, O. (1996). The World Wide Web: Quagmire or gold mine. Communications of ACM, 39(11), 65-68.
Kobsa, A. (2001, July 13-17). Tailoring privacy to user’s needs. Invited keynote. In M.
Bauer, P. J. Gmytrasiewicz, & J. Vassileva (Eds.), User Modeling 2001: 8th Interna- tional Conference, Sonthofen, Germany (LNCS/LNAI 2109, pp. 303-313). Berlin;
Heidelberg: Springer-Verlag.
Kosala, R., & Blockeel, H. (2000). Web mining research: A survey. SIGKDD Explorations:
Newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Min- ing, ACM, 2(1), 1-15.
Madria, S. K., Bhowmick, S. S., Ng, W. K., & Lim, E. P. (1999, August 30-September 1).
Research issues in Web data mining. In M. K. Mohania & A. M. Tjoa (Eds.), Data Warehousing and Knowledge Discovery: First International Conference, DaWaK ’99, Florence, Italy (LNCS 1676, pp. 303-312). Berlin; Heidelberg: Springer-Verlag.
Markellos, K., Markellou, P., Rigou, M., & Sirmakessis, S. (2004a). Web mining: Past, present, and future. In S. Sirmakessis (Ed.), Proceedings of the NEMIS Launch Con- ference 2003, International Workshop in Text Mining and Its Applications, Patras, Greece (Series: Studies in fuzziness and soft computing, Vol. 138). Berlin; Heidelberg:
Springer-Verlag.
Markellos, K., Markellou, P., Rigou, M., Sirmakessis, S., & Tsakalidis, A. (2004b, April 14-16). Web personalization and the privacy concern. In Proceedings of the 7th ETHICOMP International Conference on the Social and Ethical Impacts of Informa- tion and Communication Technologies, Challenges for the Citizen of the Information Society, Syros, Greece.
Markellou, P., Mousourouli, I, Sirmakessis, S., & Tsakalidis, A. (2005, October 18-20).
Personalized e-commerce recommendations. In Proceedings of the IEEE Interna- tional Conference on E-Business Engineering (ICEBE 2005), Beijing, China (pp.
245-252).
Markellou, P., Panayiotaki, A., & Tsakalidis, A. (2003, June 3-6). E-government and appli- cations levels: Technology at citizen service. In A. Palma dos Reis & P. Isaias (Eds.),
Markellos, Markellou, Panayotak, & Tsakalds
Proceedings of IADIS International Conference, E-Society 2003, Lisbon, Portugal (pp. 849-854). IADIS Press.
Markellou, P., Rigou, M., & Sirmakessis, S. (2005a). Mining for Web personalization. In A. Scime (Ed.), Web mining: Applications and techniques (pp. 27-48). Hershey, PA:
Idea Group Publishing.
Markellou, P., Rigou, M., & Sirmakessis, S. (2005b). Web personalization for e-market- ing intelligence. In S. Krishnamurthy (Ed.), Contemporary research in e-marketing:
Volume 1 (pp. 48-68). Hershey, PA: Idea Group Publishing.
Markellou, P., Rigou, M., Sirmakessis, S., & Tsakalidis, A. (2004). Personalization in the Semantic Web era: A glance ahead. In A. Zanasi, N. F. Ebecken, & C. A. Brebbia (Eds.), Proceedings of the 5th International Conference on Data Mining, Text Mining and their Business Applications, Data Mining 2004 (pp. 3-11), Wessex Institute of Technology (UK), Malaga, Spain, Southampton. Boston: PWIT Press.
Mobasher, B., Cooley, R., & Srivastava, J. (2000). Automatic personalization based on Web usage mining. Communications of the ACM, 43(8), 142-151.
Pal, S. K., Talwar, V., & Mitra, P. (2002). Web mining in soft computing framework: Rel- evance, state of the art and future directions. IEEE Transactions on Neural Networks, 13(5), 1163-1177.
Pierrakos, D., Paliouras, G., Papatheodorou, C., & Spyropoulos, C. (2003). Web usage mining as a tool for personalization: A survey. User Modelling and User-Adapted Interaction, 13, 311-372.
Shahabi, C., & Chen, Y. S. (2003, September 22-24). Web information personalization:
Challenges and approaches. In N. Bianchi-Berthouse (Ed.), Databases in Networked Information Systems: Third International Workshop, DNIS 2003, Aizu, Japan (LNCS 2822, pp. 5-15).
Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web usage mining: Discovery and applications of usage patterns from Web data. SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, ACM, 1(2), 12-23.
Tsoukalas, A., & Anthopoulos, L. G. (2004). Moving toward the e-city. Retrieved July 3, 2006, from http://www.govtech.net/magazine/channel_story.php?channel= 17&
id=92188
Volokh, E. (2000). Personalization and privacy. Communications of the ACM, 43(8), 84- 88.
Key.Terms
• Click-stream:.It is a record of a user’s activity on the Internet, including every Web site and every page of every Web site that the user visits, how long the user was on a page or site, in what order the pages were visited, any newsgroups that the user
Semantc Web Mnng for Personalzed Publc E-Servces participates in, and even the e-mail addresses of mail that the user sends and receives.
Both ISPs and individual Web sites are capable of tracking a user’s click-stream.
• Cookie: The data sent by a Web server to a Web client, stored locally by the client and sent back to the server on subsequent requests. In other words, a cookie is simply an HTTP header that consists of a text-only string, which is inserted into the memory of a browser. It is used to uniquely identify a user during Web interactions within a site and contains data parameters that allow the remote HTML server to keep a record of the user identity, and what actions the user takes at the remote Web site.
• Data.mining: The application of specific algorithms for extracting patterns (models) from data.
• Extended.common.log.format.(ECLF):.An extended common log format file is a variant of the common log format file simply adding two additional fields to the end of the line, the referrer (the URL the client was on before requesting Web server URL) and the user agent (the software the client claims to be using) fields.
• Government-to-business.(G2B): In the case of Government-to-Business, it refers to e-commerce in which government sells to businesses or provides them with services, as well as businesses selling products and services to government. The objective of G2B is to enable businesses to interact, transact, and communicate with government online, with greater speed and convenience.
• Government-to-citizens.(G2C): Government-to-Citizens, according to experts, in- cludes all the interactions between a government and its citizens that can take place electronically. The objective of G2C is to offer citizens faster, more responsive, more convenient, and less complicated means to public services.
• Government-to-employees. (G2E): Government-to-Employees includes activities and services between government units and their employees. As the term implies, the objective of G2E is to develop and cultivate IT capabilities among government employees to deliver efficient and cost-effective services.
• Government-to-government. (G2G): Interestingly, Government-to-Government seems to have dual significance. One, G2G is said to consist of activities between government and other ministries, departments, and agencies (MDAs) of the same government. The other meaning of G2G is a situation in which Governments have to deal with their other counterpart governments of different countries.
• Knowledge.discovery.in.databases.(KDD): The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.
• Ontology: It is a means for capturing the knowledge about a domain, in such a way that a shared understanding of it is created and can be used both by humans and computers. Ontology defines concepts that represent classes or sets of instances in the world, relationships, and other constraints among them. There are many ways of representing ontologies from lists of words, taxonomies, database schema, to frame languages and logics.
• Recommendations.systems.(RSs): They comprise the most popular forms of per- sonalisation and are becoming significant business tools. RSs take advantage of users’
and communities’ opinions in order to support individuals to identify the information
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or products most likely to be interesting to them or relevant to their needs and pref- erences. The recommendations may have various forms, for example, personalised offers/prices/products/services, inserting or removing paragraphs/sections/units, sort- ing/hiding/adding/removing/highlighting links, explanations or detailed information, and so forth.
• Semantic.Web.mining:.The idea of the Semantic Web mining is to improve the results of the Web mining by exploiting the new semantic structures of the Web, as well as to use Web mining to help build the Semantic Web. It is the combination of two complementary families of methods: Semantic Web methods and Web mining methods.
• Semantic.Web:.It is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in coopera- tion.
• Server.log: Web servers maintain log files listing every request made to the server.
With log file analysis tools, it is possible to get a good idea of where visitors come from, how often they return, and how they navigate through a site. Using cookies enables Web masters to log even more detailed information about how individual users are accessing a site.
• Web.mining:.The Web mining applies Data mining techniques on the Web. Three areas can be distinguished: Web usage mining analyses user behaviour, Web structure mining explores hyperlink structure, and Web content mining exploits the contents of the documents in the Web.
• Web.personalisation: It is the process of customising a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user’s navigational behaviour (usage data) in correlation with other information col- lected in the Web context, namely structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalisation has gained great momentum both in the research and the commercial area.
• Web.Usage.mining:.The application of data mining techniques to Web click-stream data in order to extract usage patterns.
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