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(ICWS 2020) Web API Search: Discover Web API and its Endpoint with Natural Language Queries

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Abstract: In recent years, Web Application Programming Interfaces (APIs) are becoming more and more popular with the development of the Internet industry and software engineering. Many companies provide public Web APIs for their services, and developers can greatly accelerate the development of new applications by relying on such APIs to execute complex tasks without implementing the corresponding functionalities themselves. The proliferation of web APIs, however, also introduces a challenge for developers to search and discover the desired API and its endpoint. This is a practical and crucial problem because according to ProgrammableWeb, there are more than 22,000 public Web APIs each of which may have tens or hundreds of endpoints. Therefore, it is difficult and time-consuming for developers to find the desired API and its endpoint to satisfy their development needs. In this paper, we present an intelligent system for Web API searches based on natural language queries by using a two-step transfer learning. To train the model, we collect a significant amount of sentences from crowdsourcing and utilize an ensemble deep learning model to predict the correct description sentences for an API and its endpoint. A training dataset is built by synthesizing the correct description sentences and then is used to train the two-step transfer learning model for Web API search. Extensive evaluation results show that the proposed methods and system can achieve high accuracy to search a Web API and its endpoint.

Authors: Lei Liu (Fujitsu Laboratories of America, Inc., USA); Mehdi Bahrami (Fujitsu Laboratory of America Inc., USA); Junhee Park (Fujitsu Laboratories of America, Inc., USA); Wei-Peng Chen (Fujitsu Laboratory of America Inc., USA)

Email: lliu@fujitsu.com, mbahrami@fujitsu.com, wchen@us.fujitsu.com

Who can view: Everyone
Webinar Price: Free
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Dr. Lei Liu is a researcher at Fujitsu Laboratories of America Inc. since 2015. Before joining Fujitsu, he was with UC Davis and KDDI R&D Laboratories. His main research interests include the future Internet architecture, optical networking and communications, network protocols and algorithms, machine learning, and natural language processing. He has published more than 100 peer-reviewed papers related to the previously mentioned research topics in prestigious international journals, conferences, and book chapters.
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