Visualization of Temporal Semantic Spaces

In order to realize the semantic web vision, the creation of semantic annotation, the linking of web pages to ontologies, and the creation, evolution and interrelation of ontologies must become automatic or semi-automatic processes. Natural Language Generation (NLG) takes structured data in a knowledge base as input and produces natural language text, tailored to the presentational context and the target reader. NLG techniques use and build models of the context and the user and use them to select appropriate presentation strategies.

In the context of Semantic Web or knowledge management, NLG can be applied to provide automated documentation of ontologies and knowledge bases. Unlike human-written texts, an automatic approach will constantly keep the text up-to-date which is vitally important in the Semantic Web context where knowledge is dynamic and is updated frequently. This chapter presents several Natural Language Generation (NLG) techniques that produce textual summaries from Semantic Web ontologies. The main contribution is in showing how existing NLG tools can be adapted to take Semantic Web ontologies as their input, in a way which minimizes the customization effort.

A major factor in the quality of the generated text is the content of the ontology itself. For instance, the use of string datatype properties with implicit semantics leads to the generation of text with missing semantic information. Three approaches to overcome this problem are presented and users can choose the one that suits their application best.