Backward chaining inference as a database stored procedure – the experiments on real-world knowledge bases

In this work, two approaches of backward chaining inference implementation were compared. The first approach uses a classical, goal-driven inference running on the client device – the algorithm implemented within the KBExpertLib library was used. Inference was performed on a rule base buffered in memory structures. The second approach involves implementing inference as a stored procedure, run in the environment of the database server – an original, previously not published algorithm was introduced. Experiments were conducted on real-world knowledge bases with a relatively large number of rules. Experiments were prepared so that one could evaluate the pessimistic complexity of the inference algorithm. This work also includes a detailed description of the classical backward inference algorithm – the outline of the algorithm is presented as a block diagram and in the form of pseudo-code. Moreover, a recursive version of backward chaining is discussed