In recent years, formalised large theories in form of ontologies, system specifications or mathematical libraries with thousands of sentences have become more and more common. Automatically proving properties in such theories is an important task in research as well as in industry, for instance, as part of system specifications. However, automated theorem provers struggle finding proofs when there are too many axioms available. This paper presents and investigates two approaches to reduce the load on automated theorem provers by preselecting axioms: 1) a generalisation of the SInE selection heuristics to arbitrary first-order provers, and 2) frequent item set mining. These selection methods are implemented in the web application Ontohub. One of the implementations’ usefulness is validated while others’ is refuted by an evaluation on well-known benchmarks for automated theorem proving.