Emergent Mind

Abstract

Mathematical information retrieval (MathIR) applications such as semantic formula search and question answering systems rely on knowledge-bases that link mathematical expressions to their natural language names. For database population, mathematical formulae need to be annotated and linked to semantic concepts, which is very time-consuming. In this paper, we present our approach to structure and speed up this process by supporting annotators with a system that suggests formula names and meanings of mathematical identifiers. We test our approach annotating 25 articles on https://en.wikipedia.org. We evaluate the quality and time-savings of the annotation recommendations. Moreover, we watch editor reverts and comments on Wikipedia formula entity links and Wikidata item creation and population to ground the formula semantics. Our evaluation shows that the AI guidance was able to significantly speed up the annotation process by a factor of 1.4 for formulae and 2.4 for identifiers. Our contributions were reverted in 12% of the edited Wikipedia articles and 33% of the Wikidata items within a test window of one month. The >>AnnoMathTeX<< annotation recommender system is hosted by Wikimedia at https://annomathtex.wmflabs.org. In the future, our data refinement pipeline is ready to be integrated seamlessly into the Wikipedia user interface.

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