Use Large Language Models for Named Entity Disambiguation in Academic Knowledge Graphs

DOI

10.2991/978-94-6463-264-4_79How to use a DOI?

Keywords

Large language models; Named entity disambiguation; Academic knowledge graphs; ChatGPT; Chain-of-thought

Abstract

This study investigates the application of large language models (LLMs) in disambiguating homonymous named entities in academic knowledge graphs. Current state-of-the-art methods rely on supervised learning techniques that often necessitate extensive annotated datasets, which may be scarce in specialized domains. For further exploration, we constructed an academic knowledge graph in the science and technology domain using publicly available data and extracted contrasting homonymous named entities from different projects to create a test dataset. We evaluated the performance of the ChatGPT model on this dataset using zero-shot, in-context, and chain-of-thought prompting strategies. The experimental results reveal that while LLMs achieve limited success in a zero-shot setting, chain-of-thought prompting can enhance their reasoning abilities. However, a performance gap persists when compared to supervised learning methods specifically trained on the dataset. These findings suggest that LLMs, such as ChatGPT, present a promising direction for assisting in knowledge graph construction for named entity disambiguation, particularly when labeled data is scarce. The utilization of LLMs could be especially beneficial for domains lacking extensive annotated datasets, offering a competitive alternative for disambiguating homonymous named entities.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shaojun Liu
AU  - Yanfeng Fang
PY  - 2023
DA  - 2023/09/28
TI  - Use Large Language Models for Named Entity Disambiguation in Academic Knowledge Graphs
BT  - Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)
PB  - Atlantis Press
SP  - 681
EP  - 691
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-264-4_79
DO  - 10.2991/978-94-6463-264-4_79
ID  - Liu2023
ER  -