Semantic filtering by inference on domain knowledge in spoken dialogue   systems
  General natural dialogue processing requires large amounts of domain knowledge as well as linguistic knowledge in order to ensure acceptable coverage and understanding. There are several ways of integrating lexical resources (e.g. dictionaries, thesauri) and knowledge bases or ontologies at different levels of dialogue processing. We concentrate in this paper on how to exploit domain knowledge for filtering interpretation hypotheses generated by a robust semantic parser. We use domain knowledge to semantically constrain the hypothesis space. Moreover, adding an inference mechanism allows us to complete the interpretation when information is not explicitly available. Further, we discuss briefly how this can be generalized towards a predictive natural interactive system. 
Afzal Ballim, Vincenzo Pallotta