Automatic Information Extraction from Scientific Publications Based on the Use Case of Additive Manufacturing
Source abstract
Abstract
A systematic literature review is fundamental to building a robust research foundation, informing experimental methodology, and ensuring the quality of future scientific output. However, manual extraction of targeted information from scientific publications is often laborious and prone to error, especially when researchers require rapid access to relevant findings without specialized hardware. This paper introduces an automated workflow for information extraction from scientific publications in the engineering domain. The proposed workflow consists of two primary stages: data preparation and information extraction. During data preparation, PDF files are converted to plain text and segmented into logical sections using a rule-based block detection and classification algorithm for keeping semantics. Information extraction is then performed by applying regular expressions both on keys and values in the same sentence to identify and extract relevant process and material data from the segmented text. The approach was evaluated on a dataset of 18 open-access scientific publications from various journals and conference proceedings in the AM domain. The results of the automated extraction were compared with manual extraction and with a modern large language model (LLM)-based approach. The findings demonstrate that the proposed workflow can accurately and efficiently extract relevant process and material data, achieving competitive performance relative to the LLM-based method. The workflow offers a significant reduction in time and potential errors associated with manual extraction, with automated processing averaging 15 s per document compared to one hour for manual extraction, and achieving a 76% match rate. This efficiency enables researchers to rapidly and effectively extract data. The methodology is readily transferable to other scientific fields where systematic literature reviews and structured data extraction are required.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Processing and 3D Reconstruction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Mechanical Science and Engineering, Institute of Mechatronic Engineering (IMD), TUD Dresden University of Technology, 01069 Dresden, Germany
Fraunhofer Institute for Material and Beam Technology (IWS), 01277 Dresden, Germany
Institute of Material Sciences (IfWW), TUD Dresden University of Technology, 01069 Dresden, Germany
Fraunhofer Institute for Machine Tools and Forming Technology (IWU), 01187 Dresden, Germany
Funding
Sächsische Aufbaubank (SAB) via funds of the European Regional Development Fund (ERDF) · German Federal Ministry of Economics and Climate Protection (BMWK) · VDI Technologiezentrum GmbH
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