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            <title>Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Knowledge Base Completion for Long-Tail Entities
%A Chen, Lihu
%A Razniewski, Simon
%A Weikum, Gerhard
%Y Hruschka, Estevam
%Y Mitchell, Tom
%Y Rahman, Sajjadur
%Y Mladenić, Dunja
%Y Grobelnik, Marko
%S Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, ON, Canada
%F chen-etal-2023-knowledge
%X 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.
%R 10.18653/v1/2023.matching-1.8
%U https://aclanthology.org/2023.matching-1.8
%U https://doi.org/10.18653/v1/2023.matching-1.8
%P 99-108

Markdown (Informal)

[Knowledge Base Completion for Long-Tail Entities](https://aclanthology.org/2023.matching-1.8) (Chen et al., MATCHING 2023)

• Knowledge Base Completion for Long-Tail Entities (Chen et al., MATCHING 2023)

ACL

• Lihu Chen, Simon Razniewski, and Gerhard Weikum. 2023. Knowledge Base Completion for Long-Tail Entities. In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023), pages 99–108, Toronto, ON, Canada. Association for Computational Linguistics.