The Complexity of Why-Provenance for Datalog Queries

TL;DR

This paper investigates the computational complexity of explaining query results in Datalog, revealing that recursive queries are intractable while non-recursive ones are manageable, and demonstrates practical feasibility using SAT solvers.

Contribution

It provides the first complexity analysis of why-provenance for Datalog queries, distinguishing between recursive and non-recursive cases, and explores practical solutions with SAT solvers.

Findings

Recursive Datalog why-provenance is intractable.

Non-recursive Datalog why-provenance is tractable.

SAT solvers can effectively handle practical instances.

Abstract

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.