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. 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.