Improving Open Data Web API Documentation through Interactivity and Natural Language Generation

Abstract

Widely adoption of Information Technologies has resulted in the continuous growing of open data available on the Web. However, the lack of suitable mechanisms to understand open data sources hampers its reusability. One way to overcome this limitation is by means of Web Application Programming Interfaces (APIs) with proper documentation, nowadays being the existing very rudimentary, hard to follow, and sometimes incomplete or even inaccurate in most cases. In order to improve the documentation of Web APIs that access open data, this paper proposes a novel approach to automatically generate interactive Web API documentation, both machine and user readable. This process starts by analysing the documentation of an API to obtain important information, automatically constructing Natural Language descriptions of the main Web API concepts by applying Natural Language Processing (NLP), and specifically, language generation techniques. Then, the documentation is made interactive by making it available as a Web interface, offering easy access to open data provided by Web APIs. Therefore, the use and comprehension of the Web APIs is facilitated, thus promoting the reusability of open data. The feasibility of our approach is presented through a case study and an experiment with users, both showing the benefits of our approach.