Goal-Driven Query Answering for Existential Rules with Equality
This work addresses performance bottlenecks in query answering for knowledge bases with equality, offering practical benefits for database and AI applications, though it is incremental relative to existing magic sets techniques.
The paper tackles the problem of efficient query answering over existential rules with equality by introducing a goal-driven approach that prunes irrelevant consequences, resulting in significant performance improvements, such as reducing query times from infeasible to a few seconds.
Inspired by the magic sets for Datalog, we present a novel goal-driven approach for answering queries over terminating existential rules with equality (aka TGDs and EGDs). Our technique improves the performance of query answering by pruning the consequences that are not relevant for the query. This is challenging in our setting because equalities can potentially affect all predicates in a dataset. We address this problem by combining the existing singularization technique with two new ingredients: an algorithm for identifying the rules relevant to a query and a new magic sets algorithm. We show empirically that our technique can significantly improve the performance of query answering, and that it can mean the difference between answering a query in a few seconds or not being able to process the query at all.