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  • × author_ss:"Völker, J."
  • × theme_ss:"Wissensrepräsentation"
  • × year_i:[2000 TO 2010}
  1. Bloehdorn, S.; Cimiano, P.; Duke, A.; Haase, P.; Heizmann, J.; Thurlow, I.; Völker, J.: Ontology-based question answering for digital libraries (2007) 0.00 
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    Abstract
    In this paper we present an approach to question answering over heterogeneous knowledge sources that makes use of different ontology management components within the scenario of a digital library application. We present a principled framework for integrating structured metadata and unstructured resource content in a seamless manner which can then be flexibly queried using structured queries expressed in natural language. The novelty of the approach lies in the combination of different semantic technologies providing a clear benefit for the application scenario considered. The resulting system is implemented as part of the digital library of British Telecommunications (BT). The original contribution of our paper lies in the architecture we present allowing for the non-straightforward integration of the different components we consider.
    Type
    a
  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) 0.00 
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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'.
    Type
    a

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