Semantically enabled data mashups using ontology learning method for Web APIs

Yong-Ju Lee, Jeong-Hong Kim
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引用次数: 10

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.
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使用Web api的本体学习方法实现语义支持的数据混搭
数据混搭使用户能够通过组合来自多个数据源的Web api来创建新的应用程序。但是,现有的数据mashup框架需要一些编程知识,因此不适合非专业用户使用。本文提出了一种自动构建语义本体的本体学习方法,并提出了一种基于相似度搜索的交互式组合方法,支持api的动态组合。这些技术允许mashup开发人员自动发现和组合Web api,从而消除了程序员参与的需要。
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