Editorial: Revisiting the (Machine) Semantic Web: The Missing Layers for the Human Semantic Web

Abstract

THE Semantic Web has recently emerged as a new and highly promising context for knowledge and data engineering. Within an atmosphere of high expectations, many myths as well as many visions have exhibited a number of different approaches for the exploitation of the Semantic Web in both academia and industry. However, a struggling business reality requires a concrete strategy as well as the development of specific competencies from the knowledge and data engineering community in order to prove the value of the Semantic Web to society. For three years, we have undertaken a significant effort to cultivate the Semantic Web vision in the computer science, information systems, and Semantic Web communities through the Special Interest Group on Semantic Web and Information Systems of the Association for Information Systems (AIS, see http://www.aisnet.org [1], [8]). We have emphasized the benefits of the Semantic Web merits in different application contexts, including digital libraries, e-government, knowledge management, health care, and e-learning [3], [4], [5], [6]. One of the most fascinating aspects of our effort is the exchange of ideas with people that come from different disciplines, which has led us to arrive at the conclusion that the Semantic Web adoption requires the convergence of many different disciplines; this is illustrated in Fig. 1. It is more than obvious that, after an early stage of evolution, Semantic Web research has reached a first level of maturity. Most significantly, some “voices” of criticism or questioning for the pace of the change that Semantic Web brings to traditional knowledge and data engineering have initiated a new stream of innovations. The concept of semantics [2] and its capacity to support a new era of applications challenges the traditional perceptions for the never-ending journey of computing. Knowledge and data representation and retrieval require new conceptual models and the move to a human Semantic Web vision seems more timely than ever. After the initial enthusiasm and excitement following the launch of the Semantic Web vision, a time of significant problems, unexploited opportunities, and slow adoption followed. Knowledge and data engineering had to meet several diverse and high demanding requirements for the realization of the Semantic Web. In numerous international efforts as well as in various research and competence centers of the Semantic Web, there is now a continuous effort to reach the point for a real “take-off” of the Semantic Web. It looks like an entire research community is looking for the last step before the breaking of the “wall.” And, this wall is associated with all the inefficiencies of the traditional Web, and with a panacea for the solution of all the knowledgerelated performance gaps. For the past one and a half years, we have been working hard, with the support of a large number of reviewers, on the development of an excellent quality TKDE special issue on the Semantic Web. From the beginning, our motivation was based on a clear belief that the Semantic Web represents a key milestone for the knowledge and data engineering community. While the Semantic Web is often considered to be a machine-intensive/oriented theme, our key argumentation is that Semantic Web is a Human manifesto [7]. The fundamental social and political impact of the Semantic Web is derived from the fact that its underlying technology supports a shift of social interaction patterns from “knowledge push” to “knowledge pull” [7]. This includes the shift