What Is Approximate Reasoning?

Sebastian RudolphSebastian Rudolph, Tuvshintur TserendorjTuvshintur Tserendorj, Pascal HitzlerPascal Hitzler

Sebastian Rudolph, Tuvshintur Tserendorj, Pascal Hitzler
What Is Approximate Reasoning?
In Diego Calvanese and Georg Lausen, eds., Proceedings of the 2nd International Conference on Web Reasoning and Rule Systems (RR2008), volume 5341 of LNCS, 150-164, October 2008. Springer

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KurzfassungAbstract
Approximate reasoning for the Semantic Web is based on the idea of sacrificing soundness or completeness for a significant speed-up of reasoning. This is to be done in such a way that the number of introduced mistakes is at least outweighed by the obtained speed-up. When pursuing such approximate reasoning approaches, however, it is important to be critical not only about appropriate application domains, but also about the quality of the resulting approximate reasoning procedures. With different approximate reasoning algorithms discussed and developed in the literature, it needs to be clarified how these approaches can be compared, i.e. what it means that one approximate reasoning approach is better than some other. In this paper, we will formally define such a foundation for approximate reasoning research. We will clarify - by means of notions from statistics - how different approximate algorithms can be compared, and ground the most fundamental notions in the field formally. We will also exemplify what a corresponding statistical comparison of algorithms would look like.
Forschungsgruppe:Research Group: Computational LogicComputational Logic

The final publication is available at Springer.

@inproceedings{RTH2008,
  author    = {Sebastian Rudolph and Tuvshintur Tserendorj and Pascal Hitzler},
  title     = {What Is Approximate Reasoning?},
  editor    = {Diego Calvanese and Georg Lausen},
  booktitle = {Proceedings of the 2nd International Conference on Web Reasoning
               and Rule Systems (RR2008)},
  series    = {LNCS},
  volume    = {5341},
  publisher = {Springer},
  year      = {2008},
  month     = {October},
  pages     = {150-164}
}