Knowledge Base Completion for Long-Tail Entities
Lihu Chen, Simon Razniewski, Gerhard Weikum · 2023
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.