The Promise and Challenge of Large Language Models for Knowledge Engineering: Insights from a Hackathon

Author:

Walker Johanna1ORCID,Koutsiana Elisavet2ORCID,Nwachukwu Michelle1ORCID,Meroño Peñuela Albert1ORCID,Simperl Elena1ORCID

Affiliation:

1. Department of Informatics, King's College London, United Kingdom

2. Informatics, King's College London, United Kingdom

Publisher

ACM

Reference73 articles.

1. David Abián, F Guerra, J Martínez-Romanos, and Raquel Trillo-Lado. 2017. Wikidata and DBpedia: a comparative study. In Semanitic Keyword-based Search on Structured Data Sources. Springer, Springer, Cham, Poland, 142–154.

2. An Analysis of Content Gaps Versus User Needs in the Wikidata Knowledge Graph

3. Domain-specific knowledge graphs: A survey

4. Bradley P Allen Lise Stork and Paul Groth. 2023. Knowledge Engineering using Large Language Models. https://arxiv.org/abs/2310.00637 Accessed on January 2024.

5. Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, 2023. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Nusa Dua, Bali, 675–718.

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1. Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap;ACM Transactions on Autonomous and Adaptive Systems;2024-08-20

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