Keywords-To-SPARQL translation for RDF data search and exploration
Linked Data is the most common practice for publishing and sharing information in the Data Web. As new data become available, their exploration is a fundamental step towards integration and interoperability. However, typical search methods as SPARQL queries require knowing both the SPARQL syntax and the vocabulary used in the data. For this reason, keyword-based search has been proposed, allowing an intuitive way for searching an RDF dataset. In this paper, we present a novel approach for keyword search on graph-structured data, and in particular temporal RDF graph, i.e. RDF data that involve temporal properties. Our method, instead of providing answers directly from the RDF data graph, automatically generates a set of candidate SPARQL queries that try to capture users information need as expressed by the keywords used. To support temporal exploration, our method is enriched with temporal operators allowing the user to explore data within predefined time ranges. To evaluate our approach, we perform an effectiveness study using two real-world datasets.