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==Objectives==
The term Linked Data refers to a practice for publishing and interlinking structured data on the Web. Since the practice has been proposed in 2006, a grass-roots movement has started to publish and to interlink multiple open databases on the Web following the Linked Data principles. Due to conference workshops, tutorials, and general evangelism an increasing number of data publishers such as the BBC, Thomson Reuters, The New York Times, the Library of Congress, and the UK and US governments have adopted Linked Data principles. The ongoing effort resulted in bootstrapping the Web of Data which, today, comprises billions of RDF triples including millions of links between data sources. The published datasets include data about books, movies, music, radio and television programs, reviews, scientific publications, genes, proteins, medicine, and clinical trials, geographic locations, people, companies, statistical and census data, etc.

Access to Linked Data presents exciting opportunities for the next generation of Web-based applications: data from different providers can be aggregated and fragmentary information from multiple sources can be integrated to achieve a more comprehensive view. While a few applications, such as the BBC music guide have used Linked Data to significant benefit, the deployment methodology has been to harvest the data of interest from the Web to create a private, disconnected repository for each specific application. Such an approach can only be the beginning; new concepts to consume Linked Data are required in order to exploit the Web of Linked Data to its full potential. The concepts, patterns and tools necessary are very different from situations when resource identifiers are local or known a-priori, whole-repository queries are possible, access to the repository is reliable and relevant data sources are known to be trustworthy.

Several open issues that make the development of Linked Data based applications a challenging or still impossible task. These issues include the lack of approaches for seamless integration of Linked Data from multiple sources, for dynamic, on-the-fly discovery of available data, for information quality assessment, and for elaborate end user interfaces. These open issues can only be addressed appropriately when they are conceived as research problems that require the development and systematic investigation of novel approaches. The International Workshop on Consuming Linked Data (COLD) aims to provide a platform for the presentation and discussion of such approaches. Our main objective is to receive submissions that present scientific discussion (including systematic evaluation) of concepts and approaches, instead of exposition of features implemented in Linked Data based applications. For practical systems without formalization or evaluation we refer interested participants to other offerings at ISWC, such as the Semantic Web Challenge or the Demo Track. As such, we see our workshop as orthogonal to these events.