Harald Sack

General-purpose named entity recognition does not identify the title and responsible person needed to link historical German bibliographic catalog strings in the Deutsche Digitale Bibliothek (DDB) to GND¹ Work authorities, since the task’s label set and centuries-spanning historical register have no counterpart in standard training corpora. This paper uses ISBD punctuation as a tiered silver labeling signal and evaluates a deterministic rule-based baseline alongside zero-shot, few-shot, and fine-tuned GLiNER-multi and fine-tuned XLM-R-large on a stratified, human-annotated gold set. The rule-based baseline beats both fine-tuned systems on untrained data; on trained data, only GLiNER-multi’s fine-tuning gains a real, confirmed precision advantage, and XLM-R-large’s never surpasses the rule.

Existing scholarly information extraction (SIE) datasets focus on scientific papers and overlook implementation-level details in code repositories. README files describe datasets, source code, and other implementation-level artifacts, however, their free-form Markdown offers little semantic structure, making automatic information extraction difficult. To address this gap, NERdME is introduced: 200 manually annotated README files with over νm10000 labeled spans and 10 entity types. Baseline results using large language models and fine-tuned transformers show clear differences between paper-level and implementation-level entities, indicating the value of extending SIE benchmarks with entity types available in README files. A downstream entity-linking experiment was conducted to demonstrate that entities derived from READMEs can support artifact discovery and metadata integration.

The growing complexity and heterogeneity of research data in materials science and engineering (MSE) demand structured and interoperable solutions for effective data management and reuse. To address this challenge, this article introduces the National Research Data Infrastructure (NFDI)‐MatWerk Ontology (MWO), as a semantic foundation to standardize metadata, link distributed datasets, and support digital research data management (RDM) in MSE. MWO addresses the need for the structured, standardized, and semantically rich representation of key entities, processes, and resources involved in the generation, sharing, and reuse of MSE research data. Aligned with the Basic Formal Ontology (BFO) , MWO develops as a modular extension of the NFDIcore ontology, and reuses Platform MaterialDigital core ontology (PMDco) MWO offers broad semantic coverage, modeling elements such as researchers, organizations, projects, software, workflows, datasets, metadata schemas, instruments, events, and services. It supports modular, scalable development through ontology design patterns (ODPs) and is maintained via the Ontology Development Kit (ODK) following best practices from the Open Biomedical Ontologies (OBO) Foundry. MWO also serves as the foundation for the MSE Knowledge Graph (MS‐KG), which integrates semantically interlinked research data across the NFDI‐MatWerk consortium and wider MSE community.

As research in Artificial Intelligence and Data Science continues to grow in volume and complexity, it becomes increasingly difficult to ensure transparency, reproducibility, and discoverability. To address these challenges, as research artifacts should be understandable and usable by machines, the NFDI4DataScience consortium is developing and providing Research Knowledge Graphs (RKGs). Building upon earlier works, this paper presents recent progress in creating semantically rich RKGs using standardized ontologies, shared vocabularies, and automated Information Extraction techniques. Key achievements include the development of the NFDI4DS ontology, metadata standards, tools, and services designed to support the FAIR principles, as well as community-led projects and various implementations of RKGs. Together, these efforts aim to capture and connect the complex relationships between datasets, models, software, and scientific publications.

This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value.