The Semantic Web and Web Services

The Web as we know it represents a large, fragmented reservoir of information composed of haphazardly interrelated repositories associated with particular domains and enterprises. However, the information as it is presently stored usually does not have a clear meaning attached to it that would facilitate its retrieval and manipulation, both automated and manual. The main idea of the Semantic Web is to address this deficiency. Moreover, repositories and software systems are programmatically accessed by business processes that are internal to enterprises or are executed between enterprises in order to perform inter-enterprise cooperation like in the supply chain management domain. The definition of interfaces and their access at runtime is not uniform at all, and it is at a syntactic level only. Every enterprise uses different technology for the programmatic access. Web Services are proposed as a uniform and universal technology for this problem. The concept of Semantic Web has excited researchers in areas ranging from distributed information systems to artificial intelligence. Developers of future distributed information systems are also taking a close interest as many believe that, in some form, the Semantic Web will be a central component of their software constructions. Web Services have entered the research agendas of many research communities and are being proposed as the means for remote interoperable access of components and software systems, within and between organizations. Recently, the combination of both, so-called ‘‘Semantic Web Services,’’ have started to attract many researchers as the combination of the best of the two worlds. This special issue is about the Semantic Web and Semantic Web Services. The articles span a wide spectrum of research topics and research results. Each contributes to a specific aspect of the Semantic Web and Semantic Web Services. In the current (syntactic) Web, as well as in the Semantic Web, information search is still one of the major concerns. When users search for information they would like to see only relevant results. They do not like to retrieve data that are unrelated to their search string. In the current Web the difficulty of finding good search results lies in the correct interpretation of text that is not semantically annotated. Algorithms that interpret the syntactic content in order to return good search results are necessary. These algorithms can decide whether or not to take user context and the search history of users into consideration. In the article ‘Topic-Specific Crawling on the Web with the Measurements of the Relevancy Context Graph’ by Hsu and Wu a new approach is introduced that determines the relevance of a Web page in its context. This approach makes the search results more relevant than previous approaches. In ‘Modeling user Interests by Conceptual Clustering’ by Godoy and Amandi, the search history of users is taken into consideration during the search in order to determine only relevant search results. Personal search agents have access to historical information that is properly clustered in order to be a basis for the ongoing search. Even if data is structurally well represented it does not guarantee good search or query results. This is especially the case when the information space is scattered over several sources that have different formal