  Ontology-based approach to the Natural Language Understanding (NLU)
processing allows to improve questions answering quality in dialogue systems.
We describe our NLU engine architecture and evaluate its implementation. The
engine transforms user input into the SPARQL SELECT, ASK or INSERT query to the
knowledge graph provided by the ontology-based data virtualization platform.
The transformation is based on the lexical level of the knowledge graph built
according to the Ontolex ontology. The described approach can be applied for
graph data population tasks and to the question answering systems
implementation, including chat bots. We describe the dialogue engine for a chat
bot which can keep the conversation context and ask clarifying questions,
simulating some aspects of the human logical thinking. Our approach uses
graph-based algorithms to avoid gathering datasets, required in the neural
nets-based approaches, and provide better explainability of our models. Using
question answering engine in conjunction with data virtualization layer over
the corporate data sources allows extracting facts from the structured data to
be used in conversation.
