Selecting and presenting content culled from multiple heterogeneous and physically distributed sources is a challenging task. The exponential growth of the web data in modern times has brought new requirements to such integration systems. Data is not any more produced by content providers alone, but also from regular users through the highly popular Web 2.0 social and semantic web applications. The plethora of the available web content, increased its demand by regular users who could not any more wait the development of advanced integration tools. They wanted to be able to build in a short time their own specialized integration applications. Aggregators came to the risk of these users. They allowed them not only to combine distributed content, but also to process it in ways that generate new services available for further consumption. To cope with the heterogeneous data, the Linked Data initiative aims at the creation and exploitation of correspondences across data values. In this work, although we share the Linked Data community vision, we advocate that for the modern web, linking at the data value level is not enough. Aggregators should base their integration tasks on the concept of an entity, i.e., identifying whether different pieces of information correspond to the same real world entity, such as an event or a person. We describe our theory, system, and experimental results that illustrate the approach’s effectiveness
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