Refining instructional text generation after evaluation

Natural Language Generation from Graphs

The Resource Description Framework (RDF) is the primary language to describe information on the Semantic Web. The deployment of semantic web search from Google and Microsoft, the Linked Open Data Community project along with the announcement of schema.org by Yahoo, Bing and Google have significantly fostered the generation of data available in RDF format. Yet the RDF is a computer representation of data and thus is hard for the non-expert user to understand. We propose a Natural Language Generation (NLG) engine to generate English text from a small RDF graph. The Natural Language Generation from Graphs (NLGG) system uses an ontology skeleton, which contains hierarchies of concepts, relationships and attributes, along with handcrafted template information as the knowledge base. We performed two experiments to evaluate NLGG. First, NLGG is tested with RDF graphs extracted from four ontologies in different domains. A Simple Verbalizer is used to compare the results. NLGG consistently outperforms the Simple Verbalizer in all the test cases. In the second experiment, we compare the effort spent to make NLGG and NaturalOWL work with the M-PIRO ontology. Results show that NLGG generates acceptable text with much smaller effort.