Abstract
The evolution of Web information is of utmost importance in the design of good Web Information Systems applications. New emerging paradigms, like the Semantic Web, use ontologies for describing metadata and are defined, in part, to aid in Web evolution. In this chapter, we survey techniques for ontology evolution. After identifying the different kinds of evolution with which the Web is confronted, we detail the various existing languages and techniques devoted to Web data evolution, with particular attention to Semantic Web concepts, and how these languages and techniques can be adapted to evolving data in order to improve the quality of Web Information Systems applications.
Key Terms in this Chapter
Hidden Web: The hidden Web (or invisible Web or deep Web) is the name given to pages on the World Wide Web that are not indexed by search engines. It consists of pages which are not linked to by other pages, such as dynamic Web pages based on responses to database queries. The deep Web also includes sites that require registration or otherwise limit access to their pages.
Web Resource: A Web resource is any one of the resources that are created during the development of a Web application, for example, Web projects, HTML pages, JSP files, servlets, custom tag libraries, and archive files.
Ontology: An ontology is an explicit and formal specification of a conceptualization. In general, an ontology describes formally a domain of discourse. Typically, an ontology consists of a finite list of terms and the relationships between these terms.
Web 2.0: Web 2.0 is a term often applied to a perceived ongoing transition of the World Wide Web from a collection of Web sites to a full-fledged computing platform serving Web applications to end users.
WIS: A Web information system (WIS) is an information system that uses the Web to present data to its users.
Sem antic Web: The Semantic Web is an evolving extension of the World Wide Web in which Web content can be expressed not only in natural language, but also in a form that can be understood, interpreted, and used by software agents, thus permitting them to find, share, and integrate information more easily.