Norman Radtke

The innovative strength of the steel industry is based on the mastery of microstructure–property relationships. Digital workflows can largely contribute to this aim by making the complexity of workflows reproducible and their execution user independent. In this work, the tools and workflows developed in the project StahlDigital as part of the German MaterialDigital initiative are presented and application examples are provided. A steel ontology builds the foundation for data collection and storage as well as for the semantic description of experimental and simulation data processing workflows. A dataspace powered by the Dataspace Management System (DSMS) digitally represents such workflows and manages data along them in a Findable, Accessible, Interoperable, Reusable manner aiming to derive new knowledge. Experimental data processed via DSMS can be further evaluated and used as input for simulation workflows. The simulations included in such workflows are run by the workflow system pyiron, which has been semantically extended in StahlDigital for this purpose.

Zusammenfassung: Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work.Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.Zusammenfassung. Wissensgraphen (englisch Knowledge Graphs, KGs), bieten uns eine strukturierte, flexible, transparente, systemübergreifende und kollaborative Möglichkeit, unser Wissen und unsere Daten über verschiedene Bereiche der Gesellschaft und der industriellen sowie wissenschaftlichen Disziplinen hinweg zu organisieren. KGs übertreffen jede andere Form der Repräsentation in Bezug auf die Effektivität. Die Entwicklung von Wissensgraphen (englisch Knowledge Graph Engineering, KGE) erfordert jedoch fundierte Erfahrungen mit Graphstrukturen, Webtechnologien, bestehenden Modellen und Vokabularen, Regelwerken, Logik sowie Best Practices. Es erfordert auch einen erheblichen Arbeitsaufwand.In Anbetracht der Fortschritte bei großen Sprachmodellen (englisch Large Language Modells, LLMs) und ihren Schnittstellen und Anwendungen in den letzten Jahren haben wir umfassende Experimente mit ChatGPT durchgeführt, um sein Potenzial zur Unterstützung von KGE zu untersuchen. In diesem Artikel stellen wir eine Auswahl dieser Experimente und ihre Ergebnisse vor, um zu zeigen, wie ChatGPT uns bei der Entwicklung und Verwaltung von KGs unterstützen kann.

Collaboration is one of the most important topics regarding the evolution of the World Wide Web and thus also for the Web of Data. In scenarios of distributed collaboration on datasets it is necessary to provide support for multiple different versions of datasets to exist simultaneously, while also providing support for merging diverged datasets. In this paper we present an approach that uses SPARQL 1.1 in combination with the version control system Git, that creates commits for all changes applied to an RDF dataset containing multiple named graphs. Further the operations provided by Git are used to distribute the commits among collaborators and merge diverged versions of the dataset. We show the advantages of (public) Git repositories for RDF datasets and how this represents a way to collaborate on RDF data and consume it. With SPARQL 1.1 and Git in combination, users are given several opportunities to participate in the evolution of RDF data.

Distributed actors working on a common RDF dataset regularly encounter the issue to compare the status of one graph with another or generally to synchronize copies of a dataset. A versioning system helps to synchronize the copies of a dataset, combined with a difference calculation system it is also possible to compare versions in a log and to determine, in which version a certain statement was introduced or removed. In this demo we present Quit Diff1, a tool to compare versions of a Git versioned quad store, while it is also applicable to simple unversioned RDF datasets. We are following an approach to abstract from differences on a syntactical level to differences on the level of the RDF data model, while we leave further semantic interpretation on the schema and instance level to specialized applications. Quit Diff can generate patches in various output formats and can be directly integrated in the distributed version control system Git which provides a foundation for a comprehensive co-evolution work flow on RDF datasets.