  Entity linking (EL) is the task of automatically identifying entity mentions
in text and resolving them to a corresponding entity in a reference knowledge
base like Wikipedia. Throughout the past decade, a plethora of EL systems and
pipelines have become available, where performance of individual systems varies
heavily across corpora, languages or domains. Linking performance varies even
between different mentions in the same text corpus, where, for instance, some
EL approaches are better able to deal with short surface forms while others may
perform better when more context information is available. To this end, we
argue that performance may be optimised by exploiting results from distinct EL
systems on the same corpus, thereby leveraging their individual strengths on a
per-mention basis. In this paper, we introduce a supervised approach which
exploits the output of multiple ready-made EL systems by predicting the correct
link on a per-mention basis. Experimental results obtained on existing ground
truth datasets and exploiting three state-of-the-art EL systems show the
effectiveness of our approach and its capacity to significantly outperform the
individual EL systems as well as a set of baseline methods.
