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  • × author_ss:"Cimiano, P."
  • × theme_ss:"Wissensrepräsentation"
  1. Uren, V.; Cimiano, P.; Iria, J.; Handschuh, S.; Vargas-Vera, M.; Motta, E.; Ciravegnac, F.: Semantic annotation for knowledge management : requirements and a survey of the state of the art (2006) 0.02 
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    Abstract
    While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.
    Source
    Web semantics: science, services and agents on the World Wide Web. 4(2006) no.1, S.14-28
    Theme
    Semantic Web
  2. Cimiano, P.; Völker, J.; Studer, R.: Ontologies on demand? : a description of the state-of-the-art, applications, challenges and trends for ontology learning from text (2006) 0.01 
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    Abstract
    Ontologies are nowadays used for many applications requiring data, services and resources in general to be interoperable and machine understandable. Such applications are for example web service discovery and composition, information integration across databases, intelligent search, etc. The general idea is that data and services are semantically described with respect to ontologies, which are formal specifications of a domain of interest, and can thus be shared and reused in a way such that the shared meaning specified by the ontology remains formally the same across different parties and applications. As the cost of creating ontologies is relatively high, different proposals have emerged for learning ontologies from structured and unstructured resources. In this article we examine the maturity of techniques for ontology learning from textual resources, addressing the question whether the state-of-the-art is mature enough to produce ontologies 'on demand'.

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