A language-independent method for the extraction of RDF verbalization templates
Basil Ell, Andreas Harth · 2014
With the rise of the Semantic Web more and more data become available encoded using the Semantic Web standard RDF. RDF is faced towards machines: de-signed to be easily processable by ma-chines it is difficult to be understood by casual users. Transforming RDF data into human-comprehensible text would facil-itate non-experts to assess this informa-tion. 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 maxi-mal subgraph pattern mining. We demon-strate the feasibility of our method on a parallel corpus of Wikipedia articles and DBpedia data for English and German. 1