Towards a Ranking Model for Semantic Layers over Digital Archives

TL;DR

This paper proposes a ranking model for semantic layers over digital archives that combines relevance to entities, timeliness, and entity relations to improve query result prioritization.

Contribution

It introduces a novel ranking model that integrates multiple factors for better prioritization of archive query results.

Findings

The model effectively ranks documents based on relevance, timeliness, and entity relations.

Experimental results show improved retrieval quality over baseline methods.

The approach enhances access to important historical and semantic information.

Abstract

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.