  Archived collections of documents (like newspaper archives) serve as
important information sources for historians, journalists, sociologists and
other interested parties. Semantic Layers over such digital archives allow
describing and publishing metadata and semantic information about the archived
documents in a standard format (RDF), which in turn can be queried through a
structured query language (e.g., SPARQL). This enables to run advanced queries
by combining metadata of the documents (like publication date) and
content-based semantic information (like entities mentioned in the documents).
However, the results returned by structured queries can be numerous and also
they all equally match the query. Thus, there is the need to rank these results
in order to promote the most important ones. In this paper, we focus on this
problem and propose a ranking model that considers and combines: i) the
relativeness of documents to entities, ii) the timeliness of documents, and
iii) the relations among the entities.
