A Data Mashup Language for the Data Web

Mustafa Jarrar and Marios D. Dikaiakos: A Data Mashup Language for the Data Web. Proceedings of LDOW, at WWW'09. ACM. ISBN 9781605582559. (2009).
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Abstract.
This paper is motivated by the massively increasing structured data on the Web (Data Web), and the need for novel methods to exploit these data to their full potential. Building on the remarkable success of Web 2.0 mashups, this paper regards the internet as a database, where each web data source is seen as a table, and a mashup is seen as a query over these sources. We propose a data mashup language, which allows people to intuitively query and mash up structured and linked data on the web. Unlike existing query methods, the novelty of MashQL is that it allows people to navigate, query, and mash up a data source(s) without any prior knowledge about its schema, vocabulary, or technical details. We even do not assume even that a data source should an online or inline schema. Furthermore, MashQL supports query pipes as a built-in concept, rather than only a visualization of links between modules.

Keywords: Linked-Data, Semantic Web, Data Web, Linked Data, Web 3.0, Web 2.0, RDF, SPARQL, Languages, Query-by-Diagram, Mashups, Query Pipelines, Human Factors, Design, Management.