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Archived collections of documents (like newspaper and web archives) serve asimportant information sources in a variety of disciplines, including DigitalHumanities, Historical Science, and Journalism. However, the absence ofefficient and meaningful exploration methods still remains a major hurdle inthe way of turning them into usable sources of information. A semantic layer isan RDF graph that describes metadata and semantic information about acollection of archived documents, which in turn can be queried through asemantic query language (SPARQL). This allows running advanced queries bycombining metadata of the documents (like publication date) and content-basedsemantic information (like entities mentioned in the documents). However, theresults returned by such structured queries can be numerous and moreover theyall equally match the query. In this paper, we deal with this problem andformalize the task of "ranking archived documents for structured queries onsemantic layers". Then, we propose two ranking models for the problem at handwhich jointly consider: i) the relativeness of documents to entities, ii) thetimeliness of documents, and iii) the temporal relations among the entities.The experimental results on a new evaluation dataset show the effectiveness ofthe proposed models and allow us to understand their limitations

Ranking Archived Documents for Structured Queries on Semantic Layers