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Tuffy
2011
Proceedings of the VLDB Endowment
Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their widespread adoption. We present Tuffy that achieves scalability via three novel contributions: (1) a bottom-up approach to grounding that allows us to
doi:10.14778/1978665.1978669
fatcat:4fj3ahejojdpthugkdvck4egfi