Since SPARQL has been the standard language for querying RDF data, keyword
search based on keywords-to-SPARQL translation attracts more intention.
However, existing keyword search based on keywords-to-SPARQL translation
have limitations that the schema used for keyword-to-SPARQL translation is
incomplete so that wrong or incomplete answers are returned and advantages
of indexes are not fully taken. To address the issues, an inter-entity
relationship summary (ER-summary) is constructed by distilling all the
inter-entity relationships of RDF data graph. On ER-summary, we draw circles
around each vertex with a given radius r and in the circles we build the
shortest property path index (SP-index), the shortest distance index
(SD-index) and the r-neighborhoods index by using dynamic programming
algorithm. Rather than searching for top-k subgraphs connecting all the
keywords centered directly as most existing methods do, we use these indexes
to translate keyword queries into SPARQL queries to realize exchanging space
for time. Extensive experiments show that our approach is efficient and
effective.
Scalable Keyword Search on Large RDF Data