Minimally Invasive Semantification of Light Weight Service Descriptions

Musyaffa, Fathoni A.; Halilaj, Lavdim; Siebes, Ronny; Orlandi, Fabrizio; Auer, Sören

doi:10.1109/icws.2016.93

Cited by 13 publications

(1 citation statement)

References 15 publications

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“…This composition is not trivial and involves the well-known challenges of any data integration scenario (e.g., entity matching) plus some others more APIspecific like finding the optimal paths (even based on non-functional properties), as sometimes similar information can be obtained from different overlapping sources. Existing works on API composition [6,10,3] can be used here.…”

Section: Discussionmentioning

confidence: 99%

A Model-based Chatbot Generation Approach to Converse with Open Data Sources

Ed-douibi¹,

Izquierdo²,

Daniel³

et al. 2020

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%