Semantically enabled data mashups using ontology learning method for Web APIs
Yong-Ju Lee, Jeong-Hong Kim · 2012
Data mashups enable users to create new applications by combining Web APIs from several data sources. However, the existing data mashup framework requires some programming knowledge, hence it is not suitable for use by non-expert users. In this paper, we present an ontology learning method that builds semantic ontologies automatically, and propose an interactive composition approach based on a similarity search method that supports the dynamic composition of APIs. These techniques allow mashup developers to automate the discovery and composition of Web APIs eliminating the need for programmer involvement.