Semantic mashup composition from natural language expressions: preliminary results

Despite an abundance of data available on the web today, satisfying users' complex information needs intelligently by automatically integrating and processing data from various sources remains challenging. In recent years, a large stream of research into mashups as a paradigm of end user development has emerged. These mashups foster combination and reuse of data and services and thereby allow end users to create novel applications. Developing such mashups efficiently and effectively, however, is still difficult for users that lack technical expertise. To address this issue, we extend a mashup platform with automatic mashup composition mechanisms and an agent that assists users in mashup design. To this end, we leverage semantics to simplify the mashup composition process on multiple levels. We associate each widget (i.e., mashup component) with a semantic model of inputs and outputs. These semantic models are helpful for identifying appropriate widgets in a given context and allow us to validate the links between widgets in a mashup. These validations provide the foundation for an advanced composition algorithm that automatically creates meaningful mashups from a given set of widgets. Finally, we develop an agent that leverages the semantic annotations to allow users to automatically compose mashups by entering natural-language text.