Improving the Efficiency of Inductive Logic Programming Through the Use
  of Query Packs
  Inductive logic programming, or relational learning, is a powerful paradigm
for machine learning or data mining. However, in order for ILP to become
practically useful, the efficiency of ILP systems must improve substantially.
To this end, the notion of a query pack is introduced: it structures sets of
similar queries. Furthermore, a mechanism is described for executing such query
packs. A complexity analysis shows that considerable efficiency improvements
can be achieved through the use of this query pack execution mechanism. This
claim is supported by empirical results obtained by incorporating support for
query pack execution in two existing learning systems.
