Keyword-Based Search over Semantic Data
نویسندگان
چکیده
Enabling non-experts 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 combines keyword search and web querying. KWQL scales with a users’ experience and the sophistication of its information need by a seamlessly 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 can evaluate basic queries at almost the speed of a conventional search engine. Results of a user study suggest validate that users can quickly and with very little training formulate KWQL and visKWQL queries, including structured queries. Klara Weiand, Andreas Hartl, Steffen Hausmann, François Bry Ludwig-Maximilians-Universität München, Oettingenstr. 67, 80538 München, Germany, e-mail: [email protected], [email protected], [email protected], [email protected] Tim Furche Department of Computer Science and Institute for the Future of Computing, Oxford University, Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom e-mail: [email protected]
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