  Motivated by applications in large-scale knowledge base construction, we
study the problem of scaling up a sophisticated statistical inference framework
called Markov Logic Networks (MLNs). Our approach, Felix, uses the idea of
Lagrangian relaxation from mathematical programming to decompose a program into
smaller tasks while preserving the joint-inference property of the original
MLN. The advantage is that we can use highly scalable specialized algorithms
for common tasks such as classification and coreference. We propose an
architecture to support Lagrangian relaxation in an RDBMS which we show enables
scalable joint inference for MLNs. We empirically validate that Felix is
significantly more scalable and efficient than prior approaches to MLN
inference by constructing a knowledge base from 1.8M documents as part of the
TAC challenge. We show that Felix scales and achieves state-of-the-art quality
numbers. In contrast, prior approaches do not scale even to a subset of the
corpus that is three orders of magnitude smaller.
