Kassner, N; Petroni, F; Plekhanov, M; Riedel, S; Cancedda, N; (2022) EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022. (pp. pp. 8659-8673). ACL

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Abstract

Existing work on Entity Linking mostly assumes that the reference knowledge base is complete, and therefore all mentions can be linked. In practice this is hardly ever the case, as knowledge bases are incomplete and because novel concepts arise constantly. We introduce the temporally segmented Unknown Entity Discovery and Indexing (EDIN) -benchmark where unknown entities, that is entities not part of the knowledge base and without descriptions and labeled mentions, have to be integrated into an existing entity linking system. By contrasting EDIN with zero-shot entity linking, we provide insight on the additional challenges it poses. Building on dense-retrieval based entity linking, we introduce the end-to-end EDIN-pipeline that detects, clusters, and indexes mentions of unknown entities in context. Experiments show that indexing a single embedding per entity unifying the information of multiple mentions works better than indexing mentions independently.

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