Pedrinaci, Carlos; Liu, Dong; Lin, Chenghua and Domingue, John
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Supporting the efficient discovery and use of Web APIs is increasingly important as their use and popularity grows. Yet, a simple task like finding potentially interesting APIs and their related documentation turns out to be hard and time consuming even when using the best resources currently available on theWeb. In this paper we describe our research towards an automatedWeb API documentation crawler and search engine. This paper presents two main contributions. First, we have devised and exploited crowdsourcing techniques to generate a curated dataset of Web APIs documentation. Second, thanks to this dataset, we have devised an engine able to automatically detect documentation pages. Our preliminary experiments have shown that we obtain an accuracy of 80% and a precision increase of 15 points over a keyword-based heuristic we have used as baseline.
|Item Type:||Conference Item|
|Copyright Holders:||2012 Association for the Advancement of Artificial Intelligence (www.aaai.org)|
|Project Funding Details:||
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||17 Apr 2012 15:48|
|Last Modified:||05 Aug 2016 08:05|
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