The semantic web as a Linguistic resource: Opportunities for natural language generation

2006 ◽  
Vol 19 (5) ◽  
pp. 298-303 ◽  
Author(s):  
Chris Mellish ◽  
Xiantang Sun
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Natural Language Generation ◽  
Language Generation ◽  
Linguistic Resource

Natural Language Generation from Graphs

2014 ◽  
Vol 08 (03) ◽  
pp. 335-384 ◽  
Author(s):  
Ngan T. Dong ◽  
Lawrence B. Holder
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Web Search ◽  
Open Data ◽  
Natural Language Generation ◽  
Language Generation ◽  
Expert User ◽  
Computer Representation ◽  
Description Framework ◽  
Rdf 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.

Natural Language Generation in the context of the Semantic Web

Semantic Web ◽  
2014 ◽  
Vol 5 (6) ◽  
pp. 493-513 ◽  
Author(s):  
Nadjet Bouayad-Agha ◽  
Gerard Casamayor ◽  
Leo Wanner
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Natural Language Generation ◽  
Language Generation

scholarly journals Natural Language Generation and Semantic Technologies

2014 ◽  
Vol 14 (2) ◽  
pp. 3-23 ◽  
Author(s):  
Kamenka Staykova
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Natural Language Generation ◽  
Semantic Technologies ◽  
Text Generation ◽  
Language Generation

Abstract The paper presents a survey of the domain of Natural Language Generation (NLG) with its models, techniques, applications, and investigates how the semantic technologies are drawn into text generation. The idea and facilities of Semantic Web initiative are discussed in connection with the new opportunities offered to the Natural Language Generation.

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