Abstract
Enabling nonexperts to publish structured or semantic data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making this data easily accessible in turn has received only little attention. Querying in semantic wikis typically uses full-text search for the textual content and a web query language for the annotations. This has two shortcomings: combined queries over content and annotations are not possible, and users either are restricted to simple but vague keyword queries or have to learn a complex web query language. In this chapter, we present an overview of KWQL, a query language that combineskeyword search and web querying. KWQL scales with a users’ experience and the sophistication of its information need by a seamless transition from basic keyword queries to precise, sophisticated structured analysis queries. KWQL allows for rich combined queries of full text, metadata, document structure, and annotations. KWQL’s companion language visKWQL eases the authoring of such combined queries further through a set of visual building blocks. The underlying query engine provides the full expressive power of first-order queries, but, at the same time evaluates basic queries at almost the speed of a conventional search engine. Results of a user study validate that users can quickly, and with very little training, formulate KWQL and visKWQL queries, including structured queries.
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In addition, six queries were bracketed incorrectly, but since participants had to write down their answers by hand, this is likely due to clerical errors and was ignored.
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Weiand, K., Hartl, A., Hausmann, S., Bry, F., Furche, T. (2012). Keyword-Based Search over Semantic Data. In: De Virgilio, R., Guerra, F., Velegrakis, Y. (eds) Semantic Search over the Web. Data-Centric Systems and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25008-8_7
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