Semantic Understanding of Natural Language Stories for Near Human Question Answering

Machine understanding of natural language stories is complex, and automated question answering based on them requires careful knowledge engineering involving knowledge representation, deduction, context recognition and sentiment analysis. In this paper, we present an approach to near human question answering based on natural language stories. We show that translating stories into knowledge graphs in RDF, and then restating the natural language questions into SPARQL to answer queries can be successful if the RDF graph is augmented with an ontology and an inference engine. By leveraging existing knowledge processing engines such as FRED and NLQuery, we propose the contours of an open-ended and online flexible query answering system, called Omniscient, that is able to accept a natural language user story and respond to questions also framed in natural language. The novelty of Omniscient is in its ability to recognize context and respond deductively that most current knowledge processing systems are unable to do.