Optimizing query performance using bif functions Example The following example describes how to optimize query performance using bif functions. Suppose there is the following query, which performs very well on Virtuoso db with default indexes:: SELECT DISTINCT ?r (bif:concat(bif:search_excerpt(bif:vector('lego'), ?v2))) as ?_n_f_t_m_ex_ WHERE { { ?r ?v1 ?v2 . ?v2 bif:contains 'lego' . } UNION { ?r ?v1 ?v3 . ?v3 ?v4 ?v2 . ?v4 rdfs:subPropertyOf rdfs:label . ?v2 bif:contains 'lego' . } . } LIMIT 10 Suppose also there should be hidden certain types from the user. For the purposes of this, to the end of the query is added nao:userVisible property: SELECT DISTINCT ?r (bif:concat(bif:search_excerpt(bif:vector('lego'), ?v2))) as ?_n_f_t_m_ex_ WHERE { { ?r ?v1 ?v2 . ?v2 bif:contains 'lego' . } UNION { ?r ?v1 ?v3 . ?v3 ?v4 ?v2 . ?v4 rdfs:subPropertyOf rdfs:label . ?v2 bif:contains 'lego' . } . ?r nao:userVisible "1"^^xsd:int . } LIMIT 10 The optimization: Replacing "?r nao:userVisible 1" with a bif:exists filter makes the query performance significant fast. Add Limit to each UNION, because in this case no one side of union will needlessly generate data that does not fit LIMIT 10. Some triples should reside in one graph, if this is applicable to the task in question. E.g. if both ?r ?v1 ?v2 . and ?r nao:userVisible "1"^^xsd:int . are supposed to be in same graph, then: ?r ?v1 ?v2 . ?v2 bif:contains 'lego' . -- can be replaced with: graph ?g { ?r ?v1 ?v2 . ?r nao:userVisible "1"^^xsd:int . ?v2 bif:contains 'lego' . } Finally, here is the optimized query: PREFIX nao: <http://www.semanticdesktop.org/ontologies/2007/08/15/nao#> SELECT DISTINCT ?r (bif:concat(bif:search_excerpt(bif:vector('lego'), ?v2))) as ?_n_f_t_m_ex_ WHERE { { { SELECT DISTINCT ?r ?v2 WHERE { ?r ?v1 ?v2 . ?v2 bif:contains 'lego' . ?r nao:userVisible "1"^^xsd:int . } LIMIT 10 } } UNION { { SELECT DISTINCT ?r ?v2 WHERE { ?r ?v1 ?v3 . ?v3 ?v4 ?v2 . ?v4 rdfs:subPropertyOf rdfs:label . ?v2 bif:contains 'lego' . ?r nao:userVisible "1"^^xsd:int . } LIMIT 10 } } } LIMIT 10 Related Virtuoso SPARQL Optimization Guide