Cultural-heritage KGs such as the NFDI4Culture-KG contain millions of triples about artworks, music, inscriptions, historical events, and the people and places connected to them. For many users, however, discovering this knowledge can be difficult. While SPARQL can be learned, writing meaningful queries first requires an in-depth understanding of the graph's data model, an investment many domain researchers and practitioners are unwilling to make. Even with existing user interfaces, a starting point and some guidance are usually needed, because the data contained in the graph is highly specialized, heterogeneous, and constantly growing, making it challenging to know what it contains or which questions it can answer. In this paper, we present data stories as a way not only to lower this barrier, but also to turn exploration into data-quality assessment, and thus combine accessible querying with the discovery of issues that remain hidden in aggregate statistics. In this contribution, a data story is understood as a narrative document that integrates explanatory text and images with executable SPARQL queries and their visualized results. It is described how they are authored against the graph and how they serve several purposes: guiding users through an unfamiliar graph, creating reproducible narratives, and surfacing data-quality issues previously hidden in aggregate statistics. The authoring platform LODEON including its Sparnatural and AI-supported authoring assistants is introduced as a proof-of-concept. Within the authoring environment, every claim made about the data can be backed by an explicit query, making these narratives transparent and reproducible. This paper also reflects on lessons learned from hands-on seminars and workshops. Early experience suggests that such data stories make cultural-heritage knowledge graphs more accessible for both exploration and quality assessment.

The Central Federal Card Index (Bundeszentralkartei) of Germany is a key archival resource documenting compensation claims submitted by victims of National Socialist persecution and their relatives, within the German Wiedergutmachung process. To enable semantically enriched representation, integration, and reuse of this historically significant collection, we present the BZK Ontology (BZKO). We propose a two-layer ontology for historical archival data that separates ontologically grounded domain semantics from interoperability-oriented extension constructs. The approach combines BFO-based realism with archival standards (RiC-O, PROV-O, PiCo), enabling provenance-preserving semantic integration, while maintaining logical rigor, modularity, and reuse across digital humanities infrastructures. The proposed approach establishes a reusable semantic foundation for the integration of Wiedergutmachung archival materials into digital humanities infrastructures and lays the groundwork for future knowledge graph generation, ontology validation, and the incorporation of additional historical entities and uncertain temporal and spatial information. The ontology is available on https://github.com/ISE-FIZKarlsruhe/bzko.

Materials Science and Engineering (MSE) increasingly relies on data‐intensive, automated, and distributed workflows that span synthesis, manufacturing, characterization, design, and simulation. These settings require machine‐actionable representations of materials and processes that remain interoperable across laboratories, software stacks, and organizations. Therefore, Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a mid‐level ontology that provides a semantic framework for the processing–structure–properties paradigm in MSE. PMDco 3.0 adopts an architecture aligned with the Basic Formal Ontology that enables a logically consistent classification of fundamental MSE concepts and the explicit representation of intrinsic material properties, contextual roles and functions, and related information artifacts. The work outlines the technical curation approach that supports sustainable ontology evolution through reproducible builds, automated release generation, and systematic validation workflows. Representative semantic patterns are presented as reusable building blocks for consistent modeling and data mapping, including material object duality, intensive versus extensive qualities, role and function assignment, immaterial entities for spatial context, process modeling across production, assay, and computation, and the separation of requirements from observations via set points and measurements. PMDco 3.0 is intended to serve as a community‐driven anchor for interoperable domain and application ontologies and scalable semantic interoperability in MSE.

The representation of workflows and processes is essential in materials science engineering, where experimental and computational reproducibility depend on structured and semantically coherent process models. Although numerous ontologies have been developed for process modeling, they are often complex and challenging to reuse. Ontology Design Patterns (ODPs) offer modular and reusable modeling solutions to recurring problems; however, these patterns are frequently neither explicitly published nor documented in a manner accessible to domain experts. This study surveys ontologies relevant to scientific workflows and engineering process modeling and identifies implicit design patterns embedded within their structures. We evaluate the capacity of these ontologies to fulfill key requirements for process representation in materials science. Furthermore, we propose a baseline method for the automatic extraction of design patterns from existing ontologies and assess the approach against curated ground truth patterns. All resources associated with this work, including the extracted patterns and the extraction workflow, are made openly available in a public GitHub repository.

An essential component for evaluating and comparing physical and cognitive capabilities between populations is the testing of various factors related to human performance. As a core part of sports science research, testing motor performance enables the analysis of the physical health of different demographic groups and makes them comparable. The Motor Research (MO|RE) data repository, developed at the Karlsruhe Institute of Technology, is an infrastructure for publishing and archiving research data in sports science, particularly in the field of motor performance research. In this paper, we present our vision for creating a knowledge graph from MO|RE data. With an ontology rooted in the Basic Formal Ontology, our approach centers on formally representing the interrelation of plan specifications, specific processes, and related measurements. Our goal is to transform how motor performance data are modeled and shared across studies, making it standardized and machine-understandable. The idea presented here is developed within the Leibniz Science Campus “Digital Transformation of Research” (DiTraRe).

Chemistry is an example of a discipline where the advancements of technology have led to multi-level and often tangled and tricky processes ongoing in the lab. The repeatedly complex workflows are combined with information from chemical structures, which are essential to understand the scientific process. An important tool for many chemists is Chemotion, which consists of an electronic lab notebook and a repository. This paper introduces a semantic pipeline for constructing the BFO-compliant Chemotion Knowledge Graph, providing an integrated, ontology-driven representation of chemical research data. The Chemotion-KG has been developed to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) principles and to support AI-driven discovery and reasoning in chemistry. Experimental metadata were harvested from the Chemotion API in JSON-LD format, converted into RDF, and subsequently transformed into a Basic Formal Ontology-aligned graph through SPARQL CONSTRUCT queries. The source code and datasets are publicly available via GitHub. The Chemotion Knowledge Graph is hosted by FIZ Karlsruhe Information Service Engineering. Outcomes presented in this work were achieved within the Leibniz Science Campus “Digital Transformation of Research” (DiTraRe) and are part of an ongoing interdisciplinary collaboration.

Knowledge representation in the Materials Science and Engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant (consistent) and variant (context-specific) knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific. In this paper, an approach how to maintain a comprehensive MSE-centric terminology composing a mid-level ontology–the Platform MaterialDigital Core Ontology (PMDco)–via MSE community-based curation procedures is presented. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics. Additionally, it demonstrates how the PMDco lowers development and integration thresholds. Moreover, the research highlights how to fuel it with real-world data sources ranging from manually conducted experiments and simulations with continuously automated industrial applications.

The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecting digital artifacts and ensuring they adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. To this end, this poster introduces the NFDI4DS Ontology, which describes resources in DS and AI and models the structure of the NFDI4DS consortium. Built upon the NFDICore ontology and mapped to the Basic Formal Ontology (BFO), this ontology serves as the foundation for the NFDI4DS knowledge graph currently under development.

This study applies Semantic Web technologies to advance Materials Science and Engineering (MSE) through the integration of diverse datasets. Focusing on a 2000 series age-hardenable aluminum alloy, we correlate mechanical and microstructural properties derived from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities.

The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in-laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine-actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

Ontologies are widely used in materials science to describe experiments, processes, material properties, and experimental and computational workflows. Numerous online platforms are available for accessing and sharing ontologies in Materials Science and Engineering (MSE). Additionally, several surveys of these ontologies have been conducted. However, these studies often lack comprehensive analysis and quality control metrics. This paper provides an overview of ontologies used in Materials Science and Engineering to assist domain experts in selecting the most suitable ontology for a given purpose. Sixty selected ontologies are analyzed and compared based on the requirements outlined in this paper. Statistical data on ontology reuse and key metrics are also presented. The evaluation results provide valuable insights into the strengths and weaknesses of the investigated MSE ontologies. This enables domain experts to select suitable ontologies and to incorporate relevant terms from existing resources.

Historical archival records present many challenges for OCR systems to correctly encode their content, due to visual complexity, e.g. mixed printed text and handwritten annotations, paper degradation, and faded ink. This paper addresses the problem of automatic identification and separation of handwritten and printed text in historical archival documents, including the creation of an artificial pixel-level annotated dataset and the presentation of a new FCN-based model trained on historical data. Initial test results indicate 18% IoU performance improvement on recognition of printed pixels and 10% IoU performance improvement on recognition of handwritten pixels in synthesised data when compared to the state-of-the-art trained on modern documents. Furthermore, an extrinsic OCR-based evaluation on the printed layer extracted from real historical documents shows 26% performance increase.

Based on our experience within the NFDI4Culture and NFDI-MatWerk projects we propose generalized knowledge graph based research data management solutions, which are applicable to other consortia. Our solution covers the construction of a common NFDI core ontology adapted to specific domains via domain extensions as a basis for a knowledge graph (KG) providing information about a consortium and its related research data and software resources. This KG serves as a backend for the web portal that enables interactive access and management of this data. Already implemented for NFDI4Culture and to be adapted by NFDI-MatWerk, this solution might serve as an example solution also for other consortia. We are synchronizing our efforts with ongoing work to implement knowledge graph based research data management in NFDI4DataScience.

. NFDI4Culture is establishing a knowledge graph-based infrastructure for research data on material and immaterial cultural heritage in the context of the German National Research Data Infrastructure (NFDI) in compliance with the FAIR principles. The NFDI4Culture Knowledge Graph is developed and integrated with the Culture Information Portal to aggregate heterogeneous and isolated data from the culture research landscape and thereby increase their discov-erability, interoperability and reusability. This paper presents the research data management strategy in the long-term project NFDI4Culture, which combines a CMS and a knowledge graph-based infrastructure to enable an intuitive and meaningful interaction with research resources in the cultural heritage domain.

Based on our experience within the NFDI4Culture and NFDI-MatWerk projects we propose generalized knowledge graph based research data management solutions, which are applicable to other consortia. Our solution covers the construction of a common NFDI core ontology adapted to specific domains via domain extensions as a basis for a knowledge graph (KG) providing information about a consortium and its related research data and software resources. This KG serves as a backend for the web portal that enables interactive access and management of this data. Already implemented for NFDI4Culture and to be adapted by NFDI-MatWerk, this solution might serve as an example solution also for other consortia. We are synchronizing our efforts with ongoing work to implement knowledge graph based research data management in NFDI4DataScience.

For several decades researchers have studied legal documents for insights into the evolution of legal norms and strategies, in their social and cultural context. Analysing these documents and the associated legislative sessions, trials and court cases helps uncover hidden narratives and patterns, as well as showcase the lessons learnt. The field of knowledge engineering has contributed to the growing interest in the development and use of legal ontologies that aim at providing machine-readable foundations to model legal concepts, relations and processes. Legal ontologies have been used for legal knowledge management and as knowledge bases in legal knowledge systems. With a focus on the Wiedergutmachung project as a use case, this paper presents an overview of the existing legal ontologies, demonstrates the gap to align them with the essential conceptual framework required to model historical court proceedings with respect to provenance information, and presents the ongoing work towards developing the CourtDocs Ontology by utilising existing standards and ontologies on the intersection of the legal domain, history and archival sciences. The Wiedergutmachung project centres around constructing a knowledge graph as a backbone for information systems, based on historical archival records from the compensation procedure in post-World War II Germany.