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The zbMATH database contains more than 4 million bibliographic entries. Weaim to provide easy access to these entries. Therefore, we maintain differentindex structures, including a formula index. To optimize the findability of theentries in our database, we continuously investigate new approaches to satisfythe information needs of our users. We believe that the findings from theARQMath evaluation will generate new insights into which index structures aremost suitable to satisfy mathematical information needs. Search engines,recommender systems, plagiarism checking software, and many other added-valueservices acting on databases such as the arXiv and zbMATH need to combinenatural and formula language. One initial approach to address this challenge isto enrich the mostly unstructured document data via Entity Linking. The ARQMathTask at CLEF 2020 aims to tackle the problem of linking newly posted questionsfrom Math Stack Exchange (MSE) to existing ones that were already answered bythe community. To deeply understand MSE information needs, answer-, and formulatypes, we performed manual runs for tasks 1 and 2. Furthermore, we exploredseveral formula retrieval methods: For task 2, such as fuzzy string search,k-nearest neighbors, and our recently introduced approach to retrieveMathematical Objects of Interest (MOI) with textual search queries. The taskresults show that neither our automated methods nor our manual runs archivedgood scores in the competition. However, the perceived quality of the hitsreturned by the MOI search particularly motivates us to conduct furtherresearch about MOI.

ARQMath Lab: An Incubator for Semantic Formula Search in zbMATH Open?