Abstract
The Semantic Web is an ambitious initiative aiming to transform the Web to a well-organized source of information. In particular, apart from the unstructured information of today’s Web, the Semantic Web will contain machine-processable metadata organized in ontologies. This will enhance the way we search the Web and can even allow for automatic reasoning on Web data with the use of software agents. Semantic Web adaptation brings traditional Web adaptation techniques into the new era of the Semantic Web. The idea is to enable the Semantic Web to be constantly aligned to the users’ preferences. In order to achieve this, Web usage mining and text mining methodologies are employed for the semi-automatic construction and evolution of Web ontologies. This usage-driven evolution of Web ontologies, in parallel with Web topologies evolution, can bring the Semantic Web closer to the users’ expectations.
TopIntroduction
The rate of growth in the amount of information available in the World Wide Web has not been followed by similar advances in the way this information is organized and exploited. Web adaptation seeks to address this issue by transforming the topology of a Web site to help users in their browsing tasks. In this sense, Web usage mining techniques have been employed for years to study how the Web is used in order to make Web sites more user-friendly.
The Semantic Web is an ambitious initiative aiming to transform the Web to a well-organized source of information. In particular, apart from the unstructured information of today’s Web, the Semantic Web will contain machine-processable metadata organized in ontologies. This will enhance the way we search the Web and can even allow for automatic reasoning on Web data with the use of software agents. Semantic Web adaptation brings traditional Web adaptation techniques into the new era of the Semantic Web. The idea is to enable the Semantic Web to be constantly aligned to the users’ preferences. In order to achieve this, Web usage mining and text mining methodologies are employed for the semi-automatic construction and evolution of Web ontologies. This usage-driven evolution of Web ontologies, in parallel with Web topologies evolution, can bring the Semantic Web closer to the users’ expectations.
Key Terms in this Chapter
Web Mining: Mining data related to the Word Wide Web, such as the content of Web pages, intrapage structure, which includes the HTML or XML code of a page, interpage structure that is the linkage structure between Web pages, usage data that describe how Web pages are accessed, and user profiles, including demographic, registration information, or information found in cookies.
Web Adaptation: The process of transforming the topology of the Web in order to align it with the preferences of the users, thus facilitating their browsing.
Web Access Log: A listing of page reference data. Web access logs are created by Web servers in order to keep track of the requests that occur on Web sites by Web users.
Semantic Web Adaptation: The process of transforming the topology and ontology of the Web in order to improve its usability.
Web Site Ontology: An ontology whose concepts are the thematic categories covered by the pages of a Web site. Each Web page, depending on its content, is an instance of one or more concepts of the ontology. The concepts are related to each other through a number of relationship types, representing the associations the concepts have according to the Webmaster’s perception.
Ontology: A representation of a certain domain, through the definition of concepts, relationships between concepts, and instances of concepts.
Web Usage Mining: An application of data mining methodologies to Web access logs in order to discover trends and regularities in navigation patterns of Web users.