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In this section we evaluate the performance of the DARQ query engine. The
prototype was implemented in Java as an extension to ARQ5. We used a subset
of DBpedia6. DBpedia contains RDF information extracted from Wikipedia.
The dataset is offered in different parts. +
{{{Description}}} +
We deploy 6 SPARQL endpoints (Sesame 2.4.0) on 5 remote
virtual machines. About 400,000 triples (generated by
BSBM) are distributed to these endpoints following Gaussian
distribution.
We follow the metrics presented in (23). For each query, we
calculate the number of queries executed per second (QPS)
and average results count. For the whole test, we record the
overall runtime, CPU usage, memory usage and network
overhead.
We perform 10 warm up runs and 50 testing runs for each
engine. Time out is set to 30 seconds. In each run, only
one instance of each engine is used for all queries, but cache
is cleared after finishing each query. Warm up runs are not
counted in query time related metrics, but included in system
and network overhead. +
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we investigated how the information from the VOID
descriptions effect the accuracy of the source selection. For each query, we look at
the number of sources selected and the resulting number of requests to the SPARQL
endpoints. We tested three different source selection approaches, based on 1) predicate
index only (no type information), 2) predicate and type index, and 3) predicate and type
index and grouping of sameAs patterns as described in Section 4.2. +
T
We followed these steps:
– A set of 10 predefined natural language queries has been prepared for evaluation
Table 4. Then, asking participants to try to answer these queries using their own
tools and services. The queries were chosen in increasing order of complexity.
– We implemented SPARQL queries corresponding to each of these queries to enable
non-expert participants, not familiar with SPARQL, to query the knowledge graph.
– We asked researchers to review the answers of the pre-defined queries that we formulated
based on the SemSur ontology. We asked them to tell us whether they
consider the provided answers and the way queries are formulated comprehensive
and reasonable.
– We finally asked the same researchers to fill in a satisfaction questionnaire with 18
questions14 +
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Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web +
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