  Answering science questions posed in natural language is an important AI
challenge. Answering such questions often requires non-trivial inference and
knowledge that goes beyond factoid retrieval. Yet, most systems for this task
are based on relatively shallow Information Retrieval (IR) and statistical
correlation techniques operating on large unstructured corpora. We propose a
structured inference system for this task, formulated as an Integer Linear
Program (ILP), that answers natural language questions using a semi-structured
knowledge base derived from text, including questions requiring multi-step
inference and a combination of multiple facts. On a dataset of real, unseen
science questions, our system significantly outperforms (+14%) the best
previous attempt at structured reasoning for this task, which used Markov Logic
Networks (MLNs). It also improves upon a previous ILP formulation by 17.7%.
When combined with unstructured inference methods, the ILP system significantly
boosts overall performance (+10%). Finally, we show our approach is
substantially more robust to a simple answer perturbation compared to
statistical correlation methods.
