mlsanityData to 13 Oct 2026

Knowledge Base Completion for Long-Tail Entities

arXiv 2306.17472 ↗First posted 30 Jun 2023cs.CLcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 30 Jun 2023.

Builds on

28 of 41 references.

References · 28

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16 more
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 1.

7 papers.

  1. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
  2. 2024AI and the Problem of Knowledge Collapsecited by 34
  3. 2024ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertaintycited by 10
  4. 2024Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluationcited by 2
  5. 2025Evaluation of LLMs on Long-tail Entity Linking in Historical Documentscited by 2
  6. 2023UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphscited by 0
  7. 2026Bridging the Long-Tail Gap: Robust Retrieval-Augmented Relation Completion via Multi-Stage Paraphrase Infusioncited by 0