  The increasing number of questions on Question Answering (QA) platforms like
Math Stack Exchange (MSE) signifies a growing information need to answer
math-related questions. However, there is currently very little research on
approaches for an open data QA system that retrieves mathematical formulae
using their concept names or querying formula identifier relationships from
knowledge graphs. In this paper, we aim to bridge the gap by presenting data
mining methods and benchmark results to employ Mathematical Entity Linking
(MathEL) and Unsupervised Formula Labeling (UFL) for semantic formula search
and mathematical question answering (MathQA) on the arXiv preprint repository,
Wikipedia, and Wikidata, which is part of the Wikimedia ecosystem of free
knowledge. Based on different types of information needs, we evaluate our
system in 15 information need modes, assessing over 7,000 query results.
Furthermore, we compare its performance to a commercial knowledge-base and
calculation-engine (Wolfram Alpha) and search-engine (Google). The open source
system is hosted by Wikimedia at https://mathqa.wmflabs.org. A demovideo is
available at purl.org/mathqa.
