The Semantic WebSemantic Web is the new-generation Web that tries to represent information so that it can be used by machines not just for display purposes, but for automation, integration, and reuse across applications (Boley et al. 2001). It has been one of the hottest R&D topics in recent years in the AI community, as well as in the Internet community—the Semantic Web is an important W3CW3C activity (SW Activity 2008).

A company’s competitiveness relies heavily on its business processes and accurate knowledge to execute its business processes with agility and efficiency. Business Process Management (BPM) initially promised to provide the business world with suitable tools and techniques for successful BPM without help from the IT world. However, the current practice of BPM has several fundamental problems, including difficulty with automatic discovery and the integration of business processes across organizations. Understanding that the main cause of these problems lies in the lack of semantics on business process, we first define a variety of business process ontologies in order to build a semantic business process space (SBPS) for the limited area of sales order. We then explain how the SBPS satisfies the requirements for successful implementation of semantic BPM (SBPM) and demonstrate with a scenario how SBPM can be realized in the environment of SBPS. Our novel approach will reduce the time and cost necessary for the development of a new business process in a fast-changing environment and provide practitioners with useful insights into the proper implementation of the SBPM. Although our paper defines semantic business process knowledge for only a limited domain, its insights can be readily extended to other areas of business.

E-government applications and services are built mainly on access to, retrieval of, integration of, and delivery of relevant information to citizens, businesses, and administrative users. In order to perform such information processing automatically through the Semantic Web,¹ machine-readable² enhancements of web resources are needed, based on the understanding of the content and context of the information in focus. While these enhancements are far from trivial to produce, administrations in their role of information and service providers so far find little guidance on how to migrate their web resources and enable a new quality of information processing; even research is still seeking best practices. Therefore, the underlying research question of this chapter is: what are the appropriate approaches which guide administrations in transforming their information processes toward the Semantic Web? In search for answers, this chapter analyzes the challenges and possible solutions from the perspective of administrations: (a) the reconstruction of the information processing in the e-government in terms of how semantic technologies must be employed to support information provision and consumption through the Semantic Web; (b) the required contribution to the transformation is compared to the capabilities and expectations of administrations; and (c) available experience with the steps of transformation are reviewed and discussed as to what extent they can be expected to successfully drive the e-government to the Semantic Web. This research builds on studying the case of Schleswig-Holstein, Germany, where semantic technologies have been used within the frame of the Access-eGov³ project in order to semantically enhance electronic service interfaces with the aim of providing a new way of accessing and combining e-government services.

Ten years have passed since the concept of the semantic web was proposed by Tim Berners-Lee. For these years, basic technologies for them such as RDF(S) and OWL were published. As a result, many systems using semantic technologies have been developed. Some of them are not prototype systems for researches but real systems for practical use. The authors analyzed semantic web applications published in the semantic web conferences (ISWC, ESWC, ASWC) and classified them based on ontological engineering. This paper is a review of application papers published in Semantic Web conferences. We discuss a trend and the future view of them using the results.

Background

A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research , defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature.

Results

We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine.

Conclusion

Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.

Current industry standards for describing Web Services focus on ensuring interoperability across diverse platforms, but do not provide a good foundation for automating the use of Web Services. Representational techniques being developed for the Semantic Web can be used to augment these standards. The resulting Web Service specifications enable the development of software programs that can interpret descriptions of unfamiliar Web Services and then employ those services to satisfy user goals. OWL-S (“OWL for Services”) is a set of notations for expressing such specifications, based on the Semantic Web ontology language OWL. It consists of three interrelated parts: a profile ontology, used to describe what the service does; a process ontology and corresponding presentation syntax, used to describe how the service is used; and a grounding ontology, used to describe how to interact with the service. OWL-S can be used to automate a variety of service-related activities involving service discovery, interoperation, and composition. A large body of research on OWL-S has led to the creation of many open-source tools for developing, reasoning about, and dynamically utilizing Web Services.

The lack of any methodology for modeling Semantic Web Services means that developers wishing to utilize technologies like the Web Service Modeling Ontology, the Web Service Modeling Language, and the Web Service Execution Environment are lost in a Semantic wilderness with no road signs to guide them on their way. This paper presents an initial guide for developers wishing to model Semantic Web Services, along with a description of the Web Service Modeling Toolkit that provides tool support for the activities that must be conducted by the developer in this process.

In this paper we provide a classification of adaptive systems with respect to the kind of semantic technology they exploit to accomplish or improve specific adaptation and user modeling tasks. This classification is based on a distinction between strong semantic techniques and weak semantic techniques. The former are techniques based on the Semantic Web, while the latter regard technologies that, in different ways, annotate resources, enriching their meaning. This second category includes, in particular, Web 2.0 social annotations and mixed approaches between social annotations and Semantic Web techniques. While the impact of the Semantic Web on adaptive systems has been discussed in several survey papers, the potential of weak semantic technologies has, so far, received little attention. The aim of this analysis is to fill this gap. Therefore, we will discuss contributions and limits of both approaches, but we will focus special attention on weak semantic adaptive systems.