Semantically enabled data mashups using ontology learning method for web APIs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2012 Computing, Communications and Applications Conference, ComComAp 2012
Pages304-309
Number of pages6
DOIs
StatePublished - 2012
Event2012 Computing, Communications and Applications Conference, ComComAp 2012 - Hong Kong, China
Duration: 11 Jan 201213 Jan 2012

Publication series

Name2012 Computing, Communications and Applications Conference, ComComAp 2012

Conference

Conference2012 Computing, Communications and Applications Conference, ComComAp 2012
Country/TerritoryChina
CityHong Kong
Period11/01/1213/01/12

Keywords

  • data mashup
  • interactive composition
  • ontology learning
  • similarity searching
  • Web API

Fingerprint

Dive into the research topics of 'Semantically enabled data mashups using ontology learning method for web APIs'. Together they form a unique fingerprint.

Cite this