Generating A Semi-structured Data Frame by First Order Logic

There are many approaches toward semantic interpretation of natural language nowadays. Most follow the direction that is to extract key information from contexts in natural language and rearrange them into hierarchical structures. XML is one of such structures. The approach has been proved to be an effective and useful in many applications. XML-based semantic Web is striving to build up a Web that can make sense of what Web users want and how to provide meaningful responses for users. However, in term of the languages that interpret the structures, they are not quite expressive, e.g. typically RDF/RDFS and OWL. The consequence is the possibility of weak inference performance of those structures and a semantic Web needs much more than that. To address the issue, the method here is to generate wff sentences by rules of first order logic (FOL) from natural language text and translate the sentences into a semi-structured data frame which employs a structure similar to XML, in which way not only FOL's strictness of logic and its inference capability are preserved but also expressivity that is the same as that of XML is obtained.