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An attempt is made to model the design of grain selection during single-crystal solidification of an Ni-based superalloy by the Bridgman method. Various geometrical designs of the starter block and spiral grain selector are chosen and their effects on crystal orientation of the single-crystal part are studied. The competitive grain selection is simulated utilizing the cellular automaton finite element module of the ProCAST software.

In this work, the kinetic and chemical conditions of the high propensity of the glass for the B2 phase formation are formulated, and the multi-technique approach can be applied to map phase transformations in other metallic-glass-forming systems.

OntoCommons aims at working towards interoperability by means of harmonization with respect to upper-level ontologies and facilitating agreement in domain ontology development. As part of the effort of work package 3, an objective of OntoCommons is to collect and formalize…

This paper focuses on the Wikidata and A 𝑟𝑡 G 𝑟𝑎𝑝ℎ KGs, which exhibit gaps in content that can be filled by enriching one with data from the other. Entity alignment can help to combine data from KGs by connecting entities that refer to the same real-world entities. However, entity alignment in art-domain knowledge graphs remains under-explored. In the pursuit of entity alignment between A 𝑟𝑡 G 𝑟𝑎𝑝ℎ and Wikidata, a hybrid approach is proposed.

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.

The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy.

Ontology and knowledge graph matching systems are evaluated annually by the Ontology Alignment Evaluation Initiative (OAEI). More and more systems use machine learning-based approaches, including large language models. The training and validation datasets are usually determined by the system developer and often a subset of the reference alignments are used. This sampling is against the OAEI rules and makes a fair comparison impossible. Furthermore, those models are trained offline (a trained and optimized model is packaged into the matcher) and therefore the systems are specifically trained for those tasks. In this paper, we introduce a dataset that contains training, validation, and test sets for most of the OAEI tracks. Thus, online model learning (the systems must adapt to the given input alignment without human intervention) is made possible to enable a fair comparison for ML-based systems. We showcase the usefulness of the dataset by fine-tuning the confidence thresholds of popular systems.

The rapidly evolving field of catalysis research generates a vast spectrum of data, necessitating innovative approaches to data management, interoperability, and utilization. This White Paper, “Ontology Mapping and Interoperability: Insights from Catalysis Research…

In Materials Science and Engineering (MSE), effective knowledge representation plays a crucial role in facilitating data interoperability, enabling collaboration, and supporting decision-making processes. Ontology design patterns (ODPs) provide a systematic and reusable solution…

In this paper, a novel question-answering (QA) approach named KGMistral is proposed, based on the Retrieval Augmented Generation (RAG) framework. Given the limitations of Large Language Models (LLMs) in generating accurate answers for domains not adequately covered by their training corpus, this work focuses on leveraging external domain-specific Knowledge Graphs (KGs) to enhance the performance of LLMs. Specifically, the study examines the benefits of using information from a KG to improve the QA performance of the Mistral model in the material science and engineering field. Experimental results indicate that KGMistral significantly enhances Mistral’s QA performance.

IUC12: Alignment of application- and higher-level ontologies

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.

Linked Open Data (LOD) offers significant advantages for the Materials Science and Engineering (MSE) domain, promoting interoperability, collaboration, and efficient knowledge discovery. Leveraging these benefits, the Materials Science and Engineering Knowledge Graph (MSE-KG)…

Ontologies have the potential to be widely re-used in the domain of materials science for the purpose of describing experiments, processes, properties of materials, and experimental and computational workflows [1, 2]. There are online repositories and portals that offer access…

This study tackles a significant challenge in ontology development for materials science: selecting the most appropriate upper‐level ontologies for creating application‐level ontologies and knowledge graphs. Focusing on the use case of Brinell hardness testing, the research…

The abstract The NFDICore Ontology And Related Modular Domain Ontologies For NFDI4Culture - NFDI-MatWerk - NFDI4DataScience - NFDI4Memory And Beyond was submitted to the Base4NFDI User Conference 2024 (https://events.gwdg.de/event/658/), reviewed, and accepted for…

This paper is a workshop proposal (abstract) for the DHd conference 2025.Ontologies and knowledge graphs (KGs) have become irreplaceable instruments in the toolkit of digital humanities (DH) research. They offer ways to represent and interconnect a myriad of heterogeneous…

The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort performed by ten teams of students, each guided by a senior researcher as their mentor, attending…

The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort performed by ten teams of students, each guided by a senior researcher as their mentor, attending…

The National Research Data Infrastructure (NFDI) is a German initiative aiming to develop a sustainable, standardized research data infrastructure across various disciplines [1]. As one of the specialized consortia within the NFDI framework, NFDI-MatWerk focuses on creating a…

In this paper, an approach for concept extraction from documents using pre-trained large language models (LLMs) is presented. Compared with conventional methods that extract keyphrases summarizing the important information discussed in a document, our approach tackles a more challenging task of extracting all present concepts related to the specific domain, not just the important ones. Through comprehensive evaluations of two widely used benchmark datasets, we demonstrate that our method improves the F1 score compared to state-of-the-art techniques. Additionally, we explore the potential of using prompts within these models for unsupervised concept extraction. The extracted concepts are intended to support domain coverage evaluation of ontologies and facilitate ontology learning, highlighting the effectiveness of LLMs in concept extraction tasks. Our source code and datasets are publicly available at https://github.com/ISE-FIZKarlsruhe/concept_extraction.

The National Research Data Infrastructure (NFDI) is a German initiative aimed at establishing a sustainable and standardized ecosystem for research data management across scientific disciplines. Within this context, the NFDI-MatWerk consortium focuses on developing a digital…

Digital transformation in Materials Science and Engineering (MSE) hinges on scalable, interoperable, and community-driven research data infrastructures. To achieve this, NFDI-MatWerk introduces Infrastructure Use Cases (IUCs). These IUCs stem from a requirement analysis of the…

The dataset contains use case examples from Knowledge Graph (KG) projects across the NFDI consortia, including the KG names, as well as the challenges the consortia faced in the process of employing a KG for the consortium. Lastly, the functions supported by the KGs are…

Ontologies play a central role in enabling sharing and reusing of knowledge on the Semantic Web. However, discovering suitable ontologies remains a major challenge, largely due to fragmented and insufficient metadata. Existing metadata schemas often suffer from limited scope,…

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…

NFDI-MatWerk (National Research Data Infrastructure for Materials Science and Engineering) is a German initiative focused on developing a digital infrastructure that integrates decentralized data, metadata, workflows, and a materials ontology to improve interoperability and…

The Materials Science and Engineering Knowledge Graph (MSE-KG) [1] serves as a central knowledge base for integrating and structuring research data within the NFDI-MatWerk [2]. It provides a semantic backbone that connects datasets, research outputs, institutions, and…

The NFDI MatWerk Ontology (MWO) and the accompanying Materials Science and Engineering Knowledge Graph (MSE-KG) are central pillars of the NFDI-MatWerk initiative, a national research data infrastructure project in Germany [1]. Their joint purpose is to establish a semantically…

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,…

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…

The ODP-Reuse dataset collection provides a curated suite of benchmark datasets for studying and evaluating the reuse of Ontology Design Patterns (ODPs) in ontologies. Each dataset in this collection corresponds to a distinct discovery source—(i) Paper & Repository…

■ Semantic RDM framework for MSE research data■ BFO-compliant ontology designed for MSE workflows■ Knowledge graph connects distributed materials datasets■ Community survey identifies key MSE data types■ SPARQL queries validate ontology-driven data retrieval■ FAIR principles…

The multiscale nature of materials and varied investigative methods in Materials Science and Engineering (MSE) lead to highly diverse data structures and formats. Metadata often lacks consistency across applications and is stored in unstructured formats, limiting…

The NFDI-MatWerk AHoD 2026 workshop on Knowledge Graphs, presented from an MSE-KG developers’ perspective, focused on practical experiences and challenges in building and maintaining the Materials Science and Engineering Knowledge Graph (MSE-KG). The session highlighted…

RDF dumps of the Materials Science and Engineering (MSE) Knowledge Graph (v2.1.1). Semantic integration of distributed materials science resources — enabling structured discovery, cross-resource linkage, and machine-actionable reuse across datasets, publications, software,…

The Materials Science and Engineering Knowledge Graph (MSE-KG) is a reproducible and modular pipeline for constructing domain-specific knowledge graphs within the context of the National Research Data Infrastructure for Materials Science and Engineering (NFDI-MatWerk). This work…