  Many Entity Linking systems use collective graph-based methods to
disambiguate the entity mentions within a document. Most of them have focused
on graph construction and initial weighting of the candidate entities, less
attention has been devoted to compare the graph ranking algorithms. In this
work, we focus on the graph-based ranking algorithms, therefore we propose to
apply five centrality measures: Degree, HITS, PageRank, Betweenness and
Closeness. A disambiguation graph of candidate entities is constructed for each
document using the popularity method, then centrality measures are applied to
choose the most relevant candidate to boost the results of entity popularity
method. We investigate the effectiveness of each centrality measure on the
performance across different domains and datasets. Our experiments show that a
simple and fast centrality measure such as Degree centrality can outperform
other more time-consuming measures.
