  Entity resolution, a longstanding problem of data cleaning and integration,
aims at identifying data records that represent the same real-world entity.
Existing approaches treat entity resolution as a universal task, assuming the
existence of a single interpretation of a real-world entity and focusing only
on finding matched records, separating corresponding from non-corresponding
ones, with respect to this single interpretation. However, in real-world
scenarios, where entity resolution is part of a more general data project,
downstream applications may have varying interpretations of real-world entities
relating, for example, to various user needs. In what follows, we introduce the
problem of multiple intents entity resolution (MIER), an extension to the
universal (single intent) entity resolution task. As a solution, we propose
FlexER, utilizing contemporary solutions to universal entity resolution tasks
to solve multiple intents entity resolution. FlexER addresses the problem as a
multi-label classification problem. It combines intent-based representations of
tuple pairs using a multiplex graph representation that serves as an input to a
graph neural network (GNN). FlexER learns intent representations and improves
the outcome to multiple resolution problems. A large-scale empirical evaluation
introduces a new benchmark and, using also two well-known benchmarks, shows
that FlexER effectively solves the MIER problem and outperforms the
state-of-the-art for a universal entity resolution.
