Abstract
Semantic Web technologies in general and ontology-based approaches in particular are considered the foundation for the next generation of information services. While ontologies enable software agents to exchange knowledge and information in a standardized, intelligent manner, describing today\(\check{\rm S}\)s vast amount of information in terms of ontological knowledge remains a challenge.
In this paper we describe the research project AVALON - Acquisition and VALidation of ONtologies, which aims at reducing the knowledge acquisition bottleneck by using methods from ontology learning in the context of a cybernetic control system. We will present techniques allowing us to automatically extract knowledge from textual data and formulating hypothesis based upon the extracted knowledge. Based on real world indicators, like for example business numbers, hypotheses are validated and the result is fed back into the system, thereby closing the cybernetic control system’s feedback loop. While AVALON is currently under development, we will present intermediate results and the basic idea behind the system.
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Granitzer, M., Scharl, A., Weichselbraun, A., Neidhart, T., Juffinger, A., Wohlgenannt, G. (2007). Automated Ontology Learning and Validation Using Hypothesis Testing. In: Wegrzyn-Wolska, K.M., Szczepaniak, P.S. (eds) Advances in Intelligent Web Mastering. Advances in Soft Computing, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72575-6_21
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DOI: https://doi.org/10.1007/978-3-540-72575-6_21
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