  This paper presents a novel framework for reconstructing multi-hop
explanations in science Question Answering (QA). While existing approaches for
multi-hop reasoning build explanations considering each question in isolation,
we propose a method to leverage explanatory patterns emerging in a corpus of
scientific explanations. Specifically, the framework ranks a set of atomic
facts by integrating lexical relevance with the notion of unification power,
estimated analysing explanations for similar questions in the corpus.
  An extensive evaluation is performed on the Worldtree corpus, integrating
k-NN clustering and Information Retrieval (IR) techniques. We present the
following conclusions: (1) The proposed method achieves results competitive
with Transformers, yet being orders of magnitude faster, a feature that makes
it scalable to large explanatory corpora (2) The unification-based mechanism
has a key role in reducing semantic drift, contributing to the reconstruction
of many hops explanations (6 or more facts) and the ranking of complex
inference facts (+12.0 Mean Average Precision) (3) Crucially, the constructed
explanations can support downstream QA models, improving the accuracy of BERT
by up to 10% overall.
