  Many researchers have made use of the Wikipedia network for relatedness and
similarity tasks. However, most approaches use only the most recent information
and not historical changes in the network. We provide an analysis of entity
relatedness using temporal graph-based approaches over different versions of
the Wikipedia article link network and DBpedia, which is an open-source
knowledge base extracted from Wikipedia. We consider creating the Wikipedia
article link network as both a union and intersection of edges over multiple
time points and present a novel variation of the Jaccard index to weight edges
based on their transience. We evaluate our results against the KORE dataset,
which was created in 2010, and show that using the 2010 Wikipedia article link
network produces the strongest result, suggesting that semantic similarity is
time sensitive. We then show that integrating multiple time frames in our
methods can give a better overall similarity demonstrating that temporal
evolution can have an important effect on entity relatedness.
