LLM-Integrated Knowledge Graph Generation

Knowledge Graphs (KGs) are crucial for enhancing semantic web technologies, improving information retrieval, and bolstering data-driven AI systems. Despite their importance, constructing KGs from text corpora remains a significant challenge. Traditional methods, which rely on manually crafted rules and machine learning techniques, often struggle with domain-specific texts and cross-domain transferability. Recent advances in generative AI, particularly Large Language Models (LLMs) like GPT-4o, LLAMA-3.2, and the newer Qwen, promise to continue to revolutionize traditional text mining paradigms, including KG construction [1, 2, 3], due to their advanced capabilities in understanding and generating human-like text. Integrating LLMs into the KG construction pipeline can enable richer, more accurate extraction and inference of knowledge from unstructured text sources and provide solutions that are readily transferable across domains. Our proposed workshop, unlike others related, stands out for the breadth and uniqueness of its focus on generating KGs from diverse text domains—including research papers, legal documents, newswires, and social media. It emphasizes discussions on scalable strategies, maintaining consistency, and mitigating errors. Applications range from news analytics to scientific research.

Evolving from its previous four iterations, the TEXT2KG initiative, proposed as LLM-TEXT2KG in its 5th iteration, aims to explore the novel intersection of LLMs and KG generation, focusing on innovative approaches, best practices, and challenges. It will serve as a platform to discuss how LLMs can be utilized for improved knowledge extraction, context-aware entity disambiguation, named entity recognition, relation extraction, knowledge representation and seamless ontology alignment. The workshop solicits a broad range of papers including full research papers, negative results, position papers, and system demos examining the wide range of issues and processes related to knowledge graphs generation from text corpora. Papers on resources (methods, tools, benchmarks, libraries, datasets) are also welcomed.

The proceedings of the previous four editions of the workshops are in:
(2022) https://ceur-ws.org/Vol-3184, (2023) https://ceur-ws.org/Vol-3447, (2024) https://ceur-ws.org/Vol-3747, and (2025) https://ceur-ws.org/Vol-4020.

Themes & Topics

We are interested in (including but not limited to) the following themes and topics that study the generation of Knowledge Graphs from text with LLMs,
based on quantitative, qualitative, and mixed research methods.

Important Dates

TBD

Submission Instructions

We invite full research papers, negative results, position papers, dataset and system demo papers.
The page limit for the full research papers, negative results and dataset papers is 16 pages excluding references and for the short papers and demos it is 7 pages excluding references.
Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this workshop. Submissions will be evaluated by the program committee based on the quality of the work and its fit to the workshop themes. All submissions are double-blind and a high-resolution PDF of the paper should be uploaded to the EasyChair submission site before the paper submission deadline.
The accepted papers will be presented at the Text2KG workshop integrated with the conference, and they will be published as CEUR proceedings.
All must be submitted and formatted in the style of the CEUR proceedings format.
For details on CEUR style, see CEUR's Author Instruction.
Also see Overleaf Template.

Workshop Schedule

TBD

Organizing Committee