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A language-independent method for the extraction of RDF verbalization templates

Published: 2014 Juni

Buchtitel: INLG2014 - 8th International Natural Language Generation Conference
Verlag: The Association for Computer Linguistics

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With the rise of the Semantic Web more and more data become available encoded using the Semantic Web standard RDF. This representation is faced towards machines: designed to be easily processable by machines it is difficult to understand by non-experts. Transforming RDF data into human-comprehensible text would facilitate non-experts to assess this information. In this paper we present a language-independent method for extracting RDF verbalization templates from a parallel corpus of text and data. Our method is based on distant-supervised simultaneous multi relation learning and frequent maximal subgraph pattern mining. We demonstrate the feasibility of this method on a parallel corpus of Wikipedia articles and DBpedia data for English and German.

Download: Media:INLG2014 RDF.pdf






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