Conceptual Knowledge Acquisition Using Automatically Generated Large-Scale Semantic Networks

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Executive Summary

The authors present a method for automatically creating large-scale semantic networks from natural language text, based on deep semantic analysis. They provide a robust and scalable implementation, and sketch various ways in which the representation may be deployed for conceptual knowledge acquisition. A translation to RDF establishes interoperability with a wide range of standardised tools, and bridges the gap to the field of semantic technologies. Graph-based models for representing conceptualisations have a long-standing history, ranging from expressive logical frameworks (as laid out in Peirce's work and further developed into conceptual graphs) to widely applied graph-based Semantic Web formalisms like the Resource Description Framework (RDF).

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