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

ACL ID W14-4405
Title A language-independent method for the extraction of RDF verbalization templates
Venue International Conference on Natural Language Generation
Session Main Conference
Year 2014
Authors

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.