Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models

• URL: http://arxiv.org/abs/2609.17291v1
• Date: Tue, 15 Sep 2026 15:05:57 GMT
• Title: Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models
• Abstract summary: eolas is a pipeline that transforms scientific documents into knowledge graphs aligned with a specified ontology.<n>We demonstrate eolas effectiveness in extracting useful information for scientists studying materials designed to endure the extreme temperatures and radiation levels found in fusion reactors.
• Score: 2.2508510871478014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The quest for new materials increasingly relies on predictive models and comprehensive simulations that span scales from atomic to macroscopic levels. However, essential data necessary for these models and simulations are often embedded in scientific literature as unstructured text, limiting reusability and posing challenges for researchers seeking to leverage existing knowledge effectively. While extracting structured data from unstructured text using large language models is gaining popularity, traditional methods typically generate key-value pairs data with straightforward schemas. In contrast, we introduce eolas, a modular pipeline that uses large language models to automatically transform scientific documents into knowledge graphs aligned with a specified ontology. We demonstrate eolas effectiveness in extracting useful information for scientists studying materials designed to endure the extreme temperatures and radiation levels found in fusion reactors. While a human expert might spend between thirty to ninety minutes extracting relevant data from an article, eolas can generate high-quality knowledge graphs in just a few minutes. These are presented in a tabular format with faceted navigation for easy human validation. Additionally, we introduce the first benchmark dataset designed to assess large language models capabilities in constructing knowledge graphs within the domain of irradiated materials. The analysis of 168 experiments using our dataset, various large language models and prompting techniques provides key insights that we summarize into practical guidelines for effectively extracting knowledge graphs aligned with an input ontology.

Related papers

• Improving Large Vision-Language Models' Understanding for Field Data [62.917026891829025]
  We introduce FieldLVLM, a framework designed to improve large vision-language models' understanding of field data.<n>FieldLVLM consists of two main components: a field-aware language generation strategy and a data-compressed multimodal model tuning.<n> Experimental results on newly proposed benchmark datasets demonstrate that FieldLVLM significantly outperforms existing methods in tasks involving scientific field data.
  arXiv  Detail & Related papers  (2025-07-24T11:28:53Z)
• Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models [0.0]
  This manuscript showcases an automated workflow, which unravels the encoded information from scientific literature to a machine-readable database.<n>Ultimately, a Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) enables a fast and efficient question answering chat bot.
  arXiv  Detail & Related papers  (2025-02-18T16:24:46Z)
• SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents [49.54155332262579]
  We release a new entity and relation extraction dataset for entities related to datasets, methods, and tasks in scientific articles. Our dataset contains 106 manually annotated full-text scientific publications with over 24k entities and 12k relations.
  arXiv  Detail & Related papers  (2024-10-28T15:56:49Z)
• From Tokens to Materials: Leveraging Language Models for Scientific Discovery [12.211984932142537]
  This study investigates the application of language model embeddings to enhance material property prediction in materials science. We demonstrate that domain-specific models, particularly MatBERT, significantly outperform general-purpose models in extracting implicit knowledge from compound names and material properties.
  arXiv  Detail & Related papers  (2024-10-21T16:31:23Z)
• Synthetic continued pretraining [29.6872772403251]
  We propose synthetic continued pretraining on a small corpus of domain-specific documents. We instantiate this proposal with EntiGraph, a synthetic data augmentation algorithm. We show how synthetic data augmentation can "rearrange" knowledge to enable more data-efficient learning.
  arXiv  Detail & Related papers  (2024-09-11T17:21:59Z)
• Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research [139.69207791947738]
  Dolma is a three-trillion-token English corpus built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. We document Dolma, including its design principles, details about its construction, and a summary of its contents. We present analyses and experimental results on intermediate states of Dolma to share what we have learned about important data curation practices.
  arXiv  Detail & Related papers  (2024-01-31T20:29:50Z)
• Towards Controlled Table-to-Text Generation with Scientific Reasoning [46.87189607486007]
  We present a new task for generating fluent and logical descriptions that match user preferences over scientific data, aiming to automate scientific document analysis. We construct a new challenging dataset,SciTab, consisting of table-description pairs extracted from the scientific literature, with highlighted cells and corresponding domain-specific knowledge base. The results showed that large models struggle to produce accurate content that aligns with user preferences. As the first of its kind, our work should motivate further research in scientific domains.
  arXiv  Detail & Related papers  (2023-12-08T22:57:35Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Exploring Multilingual Text Data Distillation [0.0]
  We propose several data distillation techniques for multilingual text classification datasets using language-model-based learning methods. We conduct experiments to analyze their performance in terms of classification strength, and cross-architecture generalization. Our approach builds upon existing techniques, enhancing cross-architecture generalization in the text data distillation domain.
  arXiv  Detail & Related papers  (2023-08-09T14:31:57Z)
• Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT [9.33544942080883]
  This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR dataset with 91.8% F1-score and extended the dataset with data published since its release. We also designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs)
  arXiv  Detail & Related papers  (2023-04-05T04:01:52Z)
• ENT-DESC: Entity Description Generation by Exploring Knowledge Graph [53.03778194567752]
  In practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. We introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. We propose a multi-graph structure that is able to represent the original graph information more comprehensively.
  arXiv  Detail & Related papers  (2020-04-30T14:16:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.