Léger, Alain and J.B. Nixon, Léger and Shvaiko, Pavel and Charlet, Jean (2005) Semantic Web Applications: Fields and Business Cases. The Industry Challenges The Research. UNSPECIFIED. (In Press)

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Abstract

Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is conceptualized and formalized (e.g., by means of an ontology) in order to support diversified knowledge processing (e.g., reasoning) by machine. Moreover, through the subtle joining of (cognitive) human reasoning and (logical) machine reasoning, it is possible for humans and machines to share complementary tasks. Some examples of application areas where these tasks arise are: corporate portals and knowledge management, e-commerce, e-work, healthcare, e-government, natural language understanding and automated translation, information search, data and services integration, social networks and collaborative filtering, knowledge mining, and so on. From a social and economic perspective, this emerging technology should contribute to growth in economic wealth, but it must also show clear cut value for everyday activities through technological transparency and efficiency. The uptake of Semantic Web technology by industry is progressing slowly. One of the problems is that academia is not always aware of the concrete problems that arise in industry. In contrast, industry is not often well informed about the academic developments that can potentially meet its needs. In this paper we present ongoing work in the cross-fertilization between industry and academy. In particular, we present a collection of application fields and use cases from enterprises which are interested in the promises of Semantic Web technology. We explain our approach by analyzing the industry needs in different application areas. We summarize industrial requirements with respect to Semantic Web technology in the form of a typology of knowledge processing tasks. These results are intended to focus academia on the development of plausible knowledge-based solutions for concrete industrial problems, and therefore, facilitate the industrial uptake of Semantic Web technology.

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