Web stream reasoning using probabilistic answer set programming

We propose a framework for reasoning about dynamic Web data, based on probabilistic Answer Set Programming (ASP). Our approach, which is prototypically implemented, allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities, and for learning of such weights from examples (parameter estimation). Knowledge as well as examples can be provided incrementally in the form of RDF data streams. Optionally, stream data can be configured to decay over time. With its hybrid combination of various contemporary AI techniques, our framework aims at prevalent challenges in relation to data streams and Linked Data, such as inconsistencies, noisy data, and probabilistic processing rules.