The Complexity of Why-Provenance for Datalog Queries
This work addresses a gap in the literature for database and ontology-based applications, providing foundational insights into the tractability of explanation methods, though it is incremental as it builds on existing notions.
The paper tackled the computational complexity of why-provenance for Datalog queries, a method for explaining query results in databases, and found that it is intractable for recursive queries but highly tractable for non-recursive ones, with experimental validation using SAT solvers.
Explaining why a database query result is obtained is an essential task towards the goal of Explainable AI, especially nowadays where expressive database query languages such as Datalog play a critical role in the development of ontology-based applications. A standard way of explaining a query result is the so-called why-provenance, which essentially provides information about the witnesses to a query result in the form of subsets of the input database that are sufficient to derive that result. To our surprise, despite the fact that the notion of why-provenance for Datalog queries has been around for decades and intensively studied, its computational complexity remains unexplored. The goal of this work is to fill this apparent gap in the why-provenance literature. Towards this end, we pinpoint the data complexity of why-provenance for Datalog queries and key subclasses thereof. The takeaway of our work is that why-provenance for recursive queries, even if the recursion is limited to be linear, is an intractable problem, whereas for non-recursive queries is highly tractable. Having said that, we experimentally confirm, by exploiting SAT solvers, that making why-provenance for (recursive) Datalog queries work in practice is not an unrealistic goal.