Thesis
Natural language generation from graphs
Washington State University
Master of Science (MS), Washington State University
2013
Handle:
https://hdl.handle.net/2376/102522
Abstract
The Resource Description Framework (RDF) is the primary language to describe things 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 general 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 hand-crafted template information for concepts, relationships and attributes as the knowledge base. The system 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. However, its ability to generate cohesive text is still limited.
Metrics
124 File views/ downloads
6 Record Views
Details
- Title
- Natural language generation from graphs
- Creators
- Ngan Thi Dong
- Contributors
- Lawrence B. Holder (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525062101842
- Language
- English
- Resource Type
- Thesis