A Model-based Chatbot Generation Approach to Converse with Open Data Sources
This addresses the gap between citizens and Open Data sources, making data more accessible, but it is incremental as it builds on existing chatbot and API technologies.
The paper tackles the problem of querying and integrating Open Data Web APIs, which is time-consuming and requires technical skills, by proposing an approach to automatically generate chatbots from these APIs, using a model-based intermediate representation to facilitate customization.
The Open Data movement promotes the free distribution of data. More and more companies and governmental organizations are making their data available online following the Open Data philosophy, resulting in a growing market of technologies and services to help publish and consume data. One of the emergent ways to publish such data is via Web APIs, which offer a powerful means to reuse this data and integrate it with other services. Socrata, CKAN or OData are examples of popular specifications for publishing data via Web APIs. Nevertheless, querying and integrating these Web APIs is time-consuming and requires technical skills that limit the benefits of Open Data movement for the regular citizen. In other contexts, chatbot applications are being increasingly adopted as a direct communication channel between companies and end-users. We believe the same could be true for Open Data as a way to bridge the gap between citizens and Open Data sources. This paper describes an approach to automatically derive full-fledged chatbots from API-based Open Data sources. Our process relies on a model-based intermediate representation (via UML class diagrams and profiles) to facilitate the customization of the chatbot to be generated.