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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 theexistence of a single interpretation of a real-world entity and focusing onlyon finding matched records, separating corresponding from non-correspondingones, with respect to this single interpretation. However, in real-worldscenarios, where entity resolution is part of a more general data project,downstream applications may have varying interpretations of real-world entitiesrelating, for example, to various user needs. In what follows, we introduce theproblem of multiple intents entity resolution (MIER), an extension to theuniversal (single intent) entity resolution task. As a solution, we proposeFlexER, utilizing contemporary solutions to universal entity resolution tasksto solve multiple intents entity resolution. FlexER addresses the problem as amulti-label classification problem. It combines intent-based representations oftuple pairs using a multiplex graph representation that serves as an input to agraph neural network (GNN). FlexER learns intent representations and improvesthe outcome to multiple resolution problems. A large-scale empirical evaluationintroduces a new benchmark and, using also two well-known benchmarks, showsthat FlexER effectively solves the MIER problem and outperforms thestate-of-the-art for a universal entity resolution.

FlexER: Flexible Entity Resolution for Multiple Intents