  Our goal is to answer elementary-level science questions using knowledge
extracted automatically from science textbooks, expressed in a subset of
first-order logic. Given the incomplete and noisy nature of these automatically
extracted rules, Markov Logic Networks (MLNs) seem a natural model to use, but
the exact way of leveraging MLNs is by no means obvious. We investigate three
ways of applying MLNs to our task. In the first, we simply use the extracted
science rules directly as MLN clauses. Unlike typical MLN applications, our
domain has long and complex rules, leading to an unmanageable number of
groundings. We exploit the structure present in hard constraints to improve
tractability, but the formulation remains ineffective. In the second approach,
we instead interpret science rules as describing prototypical entities, thus
mapping rules directly to grounded MLN assertions, whose constants are then
clustered using existing entity resolution methods. This drastically simplifies
the network, but still suffers from brittleness. Finally, our third approach,
called Praline, uses MLNs to align the lexical elements as well as define and
control how inference should be performed in this task. Our experiments,
demonstrating a 15\% accuracy boost and a 10x reduction in runtime, suggest
that the flexibility and different inference semantics of Praline are a better
fit for the natural language question answering task.
