  Goal-oriented dialogue systems typically communicate with a backend (e.g.
database, Web API) to complete certain tasks to reach a goal. The intents that
a dialogue system can recognize are mostly included to the system by the
developer statically. For an open dialogue system that can work on more than a
small set of well curated data and APIs, this manual intent creation will not
scalable. In this paper, we introduce a straightforward methodology for intent
creation based on semantic annotation of data and services on the web. With
this method, the Natural Language Understanding (NLU) module of a goal-oriented
dialogue system can adapt to newly introduced APIs without requiring heavy
developer involvement. We were able to extract intents and necessary slots to
be filled from schema.org annotations. We were also able to create a set of
initial training sentences for classifying user utterances into the generated
intents. We demonstrate our approach on the NLU module of a state-of-the art
dialogue system development framework.
