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Universal entity linking

delete2025-09-10
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OA
AI
A
Adam Aron Rynkiewicz *
P
Piotr Formanowicz
DOI：10.1016/j.engappai.2025.112185delete
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摘要

摘要

En 中文
实体链接是将文档中实体提及连接到知识库中对应条目的一个过程。用于实体链接的传统模型通常需要针对除其预训练所用知识库之外的其它知识库进行专门的微调，这限制了其灵活性和可扩展性。基于实体画像生成的概念，我们提出了一种新颖的方法，能够在不进行此类微调的情况下实现跨多种知识库的实体链接。我们的流程利用一个微调的大语言模型、一个通用嵌入模型和一个向量存储库，在TweekiGold和Reuters-128数据集上实现了高精度。此外，该方法在TweekiGold、Reuters-128和ISTEX-1000 Wikidata实体链接数据集上展示了强大的检索性能。我们还以Agrovoc知识库为例，说明了该方法在其它知识库中的适用性。该解决方案为实体链接提供了一种更具通用性和可扩展性的方法。
Keyword:
Entity linking
Large Language Model
Wikidata
Text embedding
AI总结

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Engineering Applications of Artificial Intelligence 封面图
Engineering Applications of Artificial Intelligence
IF:
8
论文数:
5.7K
被引数:
3.5W

机构

P
Poznan University of Technology
学者数:
4.4K
论文数: 4.1K
被引数: 3
P
Poznan Supercomputing and Networking Center
学者数:
16
论文数: 7
被引数: 38
引用论文

引用论文

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errProceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
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Scalable Zero-shot Entity Linking with Dense Entity Retrieval
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err2020-01-01
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errLedell Wu; Fabio Petroni; Martin Josifoski; Sebastian Riedel; Luke Zettlemoyer
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CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
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err2021-01-01
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errManoj Prabhakar Kannan Ravi; Kuldeep Singh; Isaiah Onando Mulang’; Saeedeh Shekarpour; Johannes Hoffart; Jens Lehmann
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ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
errProceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
IF0
err2022-01-01
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errTom Ayoola; Shubhi Tyagi; Joseph Fisher; Christos Christodoulopoulos; Andrea Pierleoni
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A Neural Approach to Entity Linking on Wikidata
errLecture Notes in Computer Science
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err2019-04-07
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Low Redox Decreases Potential Phosphorus Limitation on Soil Biogeochemical Cycling Along a Tropical Rainfall Gradient
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errLin, Yang; Gross, Avner; Silver, Whendee L.
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Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling
errProceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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err2021-01-01
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errAaron Mueller; Mark Dredze
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