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- W4383046714 abstract "Despite their impressive scale, knowledge bases (KBs), such as Wikidata, still contain significant gaps. Language models (LMs) have been proposed as a source for filling these gaps. However, prior works have focused on prominent entities with rich coverage by LMs, neglecting the crucial case of long-tail entities. In this paper, we present a novel method for LM-based-KB completion that is specifically geared for facts about long-tail entities. The method leverages two different LMs in two stages: for candidate retrieval and for candidate verification and disambiguation. To evaluate our method and various baselines, we introduce a novel dataset, called MALT, rooted in Wikidata. Our method outperforms all baselines in F1, with major gains especially in recall." @default.
- W4383046714 created "2023-07-04" @default.
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- W4383046714 date "2023-06-30" @default.
- W4383046714 modified "2023-09-26" @default.
- W4383046714 title "Knowledge Base Completion for Long-Tail Entities" @default.
- W4383046714 doi "https://doi.org/10.48550/arxiv.2306.17472" @default.
- W4383046714 hasPublicationYear "2023" @default.
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