  With recent emerging technologies such as the Internet of Things (IoT),
information collection on our physical world and environment can be achieved at
a much higher granularity and such detailed knowledge will play a critical role
in improving the productivity, operational effectiveness, decision making, and
in identifying new business models for economic growth. Efficient discovery and
querying such knowledge remains a key challenge due to the limited capability
and high latency of connections to the interfaces of knowledge bases, e.g., the
SPARQL endpoints. In this article, we present a querying system on SPARQL
endpoints for knowledge bases that performs queries faster than the
state-of-the-art systems. Our system features a cache-based optimization scheme
to improve querying performance by prefetching and caching the results of
predicted potential queries. The evaluations on query sets from SPARQL
endpoints of DBpedia and Linked GeoData showcase the effectiveness of our
approach.
