We utilize the relational model from ProbKB [https://dsr.cise.ufl.edu/projects/probkb-web-scale-probabilistic-knowledge-base/] for mining conditional rules in batches. Our mining algorithm can be written in one SQL sentence per rule type:
However, directly executing the SQL sentence would be infeasible due to the large KB sizes and multiple self joins on the KBs download theme park world. Instead we implement the algorithm in Spark with a number of optimization techniques.
Figure 1 and Figure 2 compare the predictive ability of conditional rules and normal rules, respectively. We can see that conditional rules achieves higher precision that normal rules consistently.