LLM-Based Information Extraction to Support Scientific Literature Research and Publication Workflows

2025-10-06

AI Summary

With the exponential growth of scientific literature, automated extraction of key concepts remains challenging, particularly due to poor cross-disciplinary adaptability. Method: This paper proposes a lightweight LLM-based semantic extraction method supporting FAIR implementation in scholarly workflows. It introduces a context learning–driven zero-/few-shot domain adaptation mechanism that enables rapid, fine-tuning–free adaptation to new disciplines. We systematically benchmark multiple open-source and commercial LLMs on concept identification tasks and develop an interactive online prototype system. Contribution/Results: Empirical evaluation in computer science—complemented by user studies—demonstrates the method’s effectiveness in structured literature review, knowledge graph construction, and information retrieval. It significantly improves both accuracy and cross-domain generalization of concept extraction, offering a scalable technical pathway for intelligent, full-lifecycle scholarly knowledge services.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Natural Language Processing: Information Extraction
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The increasing volume of scholarly publications requires advanced tools for efficient knowledge discovery and management. This paper introduces ongoing work on a system using Large Language Models (LLMs) for the semantic extraction of key concepts from scientific documents. Our research, conducted within the German National Research Data Infrastructure for and with Computer Science (NFDIxCS) project, seeks to support FAIR (Findable, Accessible, Interoperable, and Reusable) principles in scientific publishing. We outline our explorative work, which uses in-context learning with various LLMs to extract concepts from papers, initially focusing on the Business Process Management (BPM) domain. A key advantage of this approach is its potential for rapid domain adaptation, often requiring few or even zero examples to define extraction targets for new scientific fields. We conducted technical evaluations to compare the performance of commercial and open-source LLMs and created an online demo application to collect feedback from an initial user-study. Additionally, we gathered insights from the computer science research community through user stories collected during a dedicated workshop, actively guiding the ongoing development of our future services. These services aim to support structured literature reviews, concept-based information retrieval, and integration of extracted knowledge into existing knowledge graphs.

Problem

• Extracting key concepts from scientific documents using LLMs
• Supporting FAIR principles in scientific publishing workflows
• Enabling rapid domain adaptation for scientific information extraction

Innovation

• Uses LLMs for semantic extraction of concepts
• Employs in-context learning with few examples
• Enables rapid domain adaptation across fields