  Entity relatedness has emerged as an important feature in a plethora of
applications such as information retrieval, entity recommendation and entity
linking. Given an entity, for instance a person or an organization, entity
relatedness measures can be exploited for generating a list of highly-related
entities. However, the relation of an entity to some other entity depends on
several factors, with time and context being two of the most important ones
(where, in our case, context is determined by a particular corpus). For
example, the entities related to the International Monetary Fund are different
now compared to some years ago, while these entities also may highly differ in
the context of a USA news portal compared to a Greek news portal. In this
paper, we propose a simple but flexible model for entity relatedness which
considers time and entity aware word embeddings by exploiting the underlying
corpus. The proposed model does not require external knowledge and is language
independent, which makes it widely useful in a variety of applications.
