| Abstract |
Performing rule-based reasoning on a large amount of data is a challenging task. In the Semantic Web, there are billions of statements on which reasoning can be applied to infer new and useful knowledge. Reasoning can be applied in several ways. Currently, forward inference is the most scalable approach but backward inference is more appealing because it is executed at query time only on the knowledge we are interested on.
In this talk, I will briefly present the state of the art on this field and discuss how we can merge these two types of reasoning under a high performance and scalability point of view.
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