TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546729 DO - https://doi.org/10.1002/adem.202101176 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Schilling, Markus A1 - Birkholz, Henk A1 - Jung, Matthias A1 - Waitelonis, Jörg A1 - Mädler, Lutz A1 - Sack, Harald T1 - PMD Core Ontology: Achieving semantic interoperability in materials science N2 - 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. KW - Ontology KW - Materials science and engineering KW - Knowledge representation KW - Reproducibility KW - Semantic interoperability KW - Semantic data integration PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-592948 DO - https://doi.org/10.1016/j.matdes.2023.112603 SN - 0264-1275 VL - 237 SP - 1 EP - 12 PB - Elsevier CY - Amsterdam AN - OPUS4-59294 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Schilling, Markus A1 - Curran, Maurice A1 - Campbell, Carelyn E. A1 - Dima, Alden A. A1 - Birkholz, Henk A1 - Lau, June W. T1 - Natural Language Processing-Driven Microscopy Ontology Development N2 - AbstractThis manuscript describes the accelerated development of an ontology for microscopy in materials science and engineering, leveraging natural language processing (NLP) techniques. Drawing from a comprehensive corpus comprising over 14 k contributions to the Microscopy and Microanalysis conference series, we employed two neural network-based algorithms for NLP. The goal was to semiautomatically create the Microscopy Ontology (MO) that encapsulates and interconnects the terminology most frequently used by the community. The MO, characterized by its interlinked entities and relationships, is designed to enhance the quality of user query results within NexusLIMS. This enhancement is facilitated through the concurrent querying of related terms and the seamless integration of logical connections. KW - Microscopy Ontology KW - Knowledge Representation KW - Semantic Interoperability KW - Natural Language Processing KW - Ontology Development PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-616942 DO - https://doi.org/10.1007/s40192-024-00378-y SP - 1 EP - 12 PB - Springer Science and Business Media LLC AN - OPUS4-61694 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Schilling, Markus A1 - von Hartrott, Philipp A1 - Waitelonis, Jörg T1 - Semantic integration of diverse data in materials science: Assessing Orowan strengthening N2 - AbstractThis 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. KW - Semantic Interoperability KW - Knowledge Graph KW - Orowan Mechanism KW - Aluminum Alloy Aging KW - Ontology KW - Semantic Data Integration PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-599780 DO - https://doi.org/10.1038/s41597-024-03169-4 VL - 11 IS - 1 SP - 1 EP - 12 PB - Springer Science and Business Media LLC AN - OPUS4-59978 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Waitelonis, Jörg A1 - Birkholz, Henk A1 - Jung, Matthias A1 - Schilling, Markus A1 - v. Hartrott, Philipp A1 - Bruns, Marian A1 - Schaarschmidt, Jörg A1 - Beilke, Kristian A1 - Mutz, Marcel A1 - Nebel, Vincent A1 - Königer, Veit A1 - Beran, Lisa A1 - Kraus, Tobias A1 - Vyas, Akhilesh A1 - Vogt, Lars A1 - Blum, Moritz A1 - Ell, Basil A1 - Chen, Ya‐Fan A1 - Waurischk, Tina A1 - Thomas, Akhil A1 - Durmaz, Ali Riza A1 - Ben Hassine, Sahar A1 - Fresemann, Carina A1 - Dziwis, Gordian A1 - Beygi Nasrabadi, Hossein A1 - Hanke, Thomas A1 - Telong, Melissa A1 - Pirskawetz, Stephan A1 - Kamal, Mohamed A1 - Bjarsch, Thomas A1 - Pähler, Ursula A1 - Hofmann, Peter A1 - Leemhuis, Mena A1 - Özçep, Özgür L. A1 - Meyer, Lars‐Peter A1 - Skrotzki, Birgit A1 - Neugebauer, Jörg A1 - Wenzel, Wolfgang A1 - Sack, Harald A1 - Eberl, Chris A1 - Portella, Pedro Dolabella A1 - Hickel, Tilmann A1 - Mädler, Lutz A1 - Gumbsch, Peter T1 - Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science N2 - 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. KW - Semantic Interoperability KW - Data Spaces KW - Integration Workflows KW - MaterialDigital PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622952 DO - https://doi.org/10.1002/adem.202401092 SN - 1527-2648 SP - 1 EP - 25 PB - Wiley-VCH CY - Weinheim AN - OPUS4-62295 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chen, Yue A1 - Schilling, Markus A1 - von Hartrott, P. A1 - Beygi Nasrabadi, Hossein A1 - Skrotzki, Birgit A1 - Olbricht, Jürgen T1 - Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing N2 - In recent years, the design and development of materials are strongly interconnected with the development of digital technologies. In this respect, efficient data management is the building block of material digitization and, in the field of materials science and engineering (MSE), effective solutions for data standardization and sharing of different digital resources are needed. Therefore, ontologies are applied that represent a map of MSE concepts and relationships between them. Among different ontology development approaches, graphical editing based on standard conceptual modeling languages is increasingly used due to its intuitiveness and simplicity. This approach is also adopted by the Materials-open-Laboratory project (Mat-o-Lab), which aims to develop domain ontologies and method graphs in accordance with testing standards in the field of MSE. To suit the actual demands of domain experts in the project, Ontopanel was created as a plugin for the popular open-source graphical editor diagrams.net to enable graphical ontology editing. It includes a set of pipeline tools to foster ontology development in diagrams.net, comprising imports and reusage of ontologies, converting diagrams to Web Ontology Language (OWL), verifying diagrams using OWL rules, and mapping data. It reduces learning costs by eliminating the need for domain experts to switch between various tools. Brinell hardness testing is chosen in this study as a use case to demonstrate the utilization of Ontopanel. KW - Materials Testing KW - Ontology KW - Visual ontology development KW - Data mapping KW - FAIR PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-560339 DO - https://doi.org/10.1007/s40192-022-00279-y SP - 1 EP - 12 PB - Springer AN - OPUS4-56033 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Glauer, M. A1 - Schilling, Markus A1 - Stappel, M. T1 - A practical ontology development guide N2 - Knowledge representation is becoming increasingly important in view of the large amounts of data that are handled in the modern scientific landscape. Many of the domains that have most readily realised this problem and worked on potential solutions have been the domain of biochemistry. And although the developments sought here were not accompanied by philosophers, this process organically led to the development of formal and structured representations of certain domains. While the resulting structures were not the first formal ontologies, they are ones that are still in wide use to this day. These approaches have led to major advances in the organisation, structuring and communication of scientific results. Since then, a variety of other domains have tried to adapt a similar process and develop their own ontologies. However, the development of ontologies from the domain of biochemistry was the result of a years-long process that also involved a large number of errors and course corrections. One of the greatest challenges is also one of the greatest strengths of ontologies: Interoperability with other ontologies. To ensure this interoperability, ontologies must follow certain principles. In the field of biochemistry, the OBO Foundry has established itself, which offers functionalities for a rich network of ontologies from the domain, but at the same time also defines rules. The purpose of this document is to define a similar set of rules for open ontology development, but which addresses a broader domain and at the same time lowers the barrier of entry for new ontology developers. To this end, we will outline a workflow that can be used to build new ontologies more efficiently. This workflow is based not only on our own years of experience in ontology development, but also on the rules of external experts such as the OBO Foundry. KW - Ontology KW - Data integration KW - Ontologie KW - Data interoperability PY - 2024 UR - https://scientific-ontology-network.github.io/ UR - https://github.com/scientific-ontology-network/ontology-development-guide/releases/download/v0.1.0/ontology-guide.pdf SP - 1 EP - 27 PB - GitHub CY - San Francisco ET - Version 0.1 AN - OPUS4-61140 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Tensile Test: From Standard to PMD Application Ontology N2 - Data analysis methods play an important role in both the experimental and simulation-based digital description of materials but have so far been poorly structured. The platform Material Digital (PMD) should contribute to a standardized description of data processing methods in materials research. The goal is the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications. Therefore, application ontologies are created to explicitly describe processes and test methods. In this presentation, the first efforts in creating a tensile test application ontology in accordance with the ISO standard 6892-1:2019-11 are shown. Especially, the path of ontology development to be pursued based on standards was focused. T2 - Onboarding Workshop der Plattform Material Digital (PMD) CY - Online meeting DA - 13.04.2021 KW - Ontology KW - Platform MaterialDigital KW - PMD KW - Tensile Test KW - Normung KW - Standardization PY - 2021 AN - OPUS4-52425 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Tensile Test Ontology used in Platform Material Digital (PMD) N2 - Data analysis methods play an important role in both the experimental and simulation-based digital description of materials but have so far been poorly structured. The platform Material Digital (PMD) is supposed to contribute to a standardized description of data processing methods in materials research. The goal is the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications. Therefore, application ontologies are created to explicitly describe processes and test methods. In this presentation, the first efforts within the joint project PMD in creating a tensile test application ontology in accordance with the ISO standard 6892-1:2019-11 are shown. Especially, the path of ontology development to be pursued based on standards was focused. Furthermore, the presentation includes a live demonstration of queries possibly performed to query data that was uploaded in the PMD triple store. T2 - Online Workshop: An introduction to the semantic web and ontologies CY - Online meeting DA - 23.04.2021 KW - Ontology KW - Tensile Test KW - Platform Material Digital KW - PMD KW - Knowledge Graphs KW - Semantic Web PY - 2021 AN - OPUS4-52949 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus T1 - Tensile Test Ontology (TTO) N2 - This is the stable version 2.0.1 of the PMD ontology module of the tensile test (Tensile Test Ontology - TTO) as developed on the basis of the 2019 standard ISO 6892-1: Metallic materials - Tensile Testing - Part 1: Method of test at room temperature. The TTO was developed in the frame of the PMD project. The TTO provides conceptualizations valid for the description of tensile test and corresponding data in accordance with the respective standard. By using TTO for storing tensile test data, all data will be well structured and based on a common vocabulary agreed on by an expert group (generation of FAIR data) which will lead to enhanced data interoperability. This comprises several data categories such as primary data, secondary data and metadata. Data will be human and machine readable. The usage of TTO facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of tensile test data is enhanced and data querying in combination with other aspects and data within the broad field of material science and engineering (MSE) is facilitated. The TTO class structure forms a comprehensible and semantic layer for unified storage of data generated in a tensile test including the possibility to record data from analysis, re-evaluation and re-use. Furthermore, extensive metadata allows to assess data quality and reproduce experiments. Following the open world assumption, object properties are deliberately low restrictive and sparse. KW - Ontology KW - Tensile Test KW - Digitalization KW - Plattform MaterialDigital KW - Structured Data PY - 2023 UR - https://github.com/MarkusSchilling/application-ontologies/blob/479311832819af695a2c64fa8eb772f2da398061/tensile_test_ontology_TTO/pmd_tto.ttl UR - https://github.com/materialdigital/core-ontology/blob/59f5727b0437ceea5e3d9fcb8fcd0ac211e92cc3/pmd_tto.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-57935 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - PMD Workshop: Use and benefits of the PMDco N2 - This is an interactive workshop of the Plattform MaterialDigital (PMD) on Semantic Web technologies (SWT) where we delve into the world of ontology development and data structuring in the field of materials science and engineering (MSE). This workshop is designed to equip participants with essential skills in categorizing classes within ontologies, with a special emphasis on the PMD Core Ontology (PMDco). Discover the immense value of ontologies for MSE data reproducibility and reuse and harness the power of PMDco to foster your data management practices. In this workshop, you will explore the intricacies of class categorization when developing ontologies in the MSE domain. Through interactive discussions and hands-on exercises, you will learn effective techniques for structuring and organizing ontologies to enhance data retrieval and analysis. You will gain valuable insights into best practices and considerations for class categorization, aiming at optimal data organization and management within your MSE projects. One of the highlights of this workshop is the introduction to PMDco, a mid-level ontology in the field of MSE developed community-driven in the frame of the project PMD. Learn about PMDco, its underlying concepts, and its application in detail. Discover how PMDco provides a comprehensive framework for MSE data representation, enabling seamless integration and interoperability across different systems and platforms. In addition to exploring PMDco, this workshop will illustrate the benefits of data interoperability and SWT with respect to leveraging the full potential of MSE data. T2 - NFDI MatWerk Conference 2023 CY - Siegburg, Germany DA - 27.06.2023 KW - Workshop KW - Ontology KW - Plattform MaterialDigital KW - PMDco PY - 2023 AN - OPUS4-57802 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Plattform MaterialDigital in DIN NA 062-01-42 AA N2 - In dieser Präsentation werden die letzten Entwicklungen in der Plattform MaterialDigital (PMD) im DIN Normenausschuss 062-01-42 AA zur Zug- und Duktilitätsprüfung für Metalle vorgestellt. Dabei werden hauptsächlich die Entwicklungen und Neustrukturierung der PMD Core Ontology (PMDco) sowie die entsprechende Anpassung der Zugversuchsontologie (Tensile Test Ontology - TTO) inklusive des Datenmappings adressiert. Für das Datenmapping wurde ein speziell im Zusammenhang mit der PMD erstellter Zugversuchsdatensatz verwendet, welcher frei verfügbar in einem online Zenodo Repository veröffentlicht ist. Weiterhin wird die Weiterentwicklung der Norm in Verbindung mit ihrer folgenden Revision hinsichtlich der Integration von Aspekten zur Digitalisierung in der Materialwissenschaft und Werkstofftechnik sowie der Verwendung von semantischen Technologien, wie unter anderem ontologischen Repräsentationen von Materialien und Prozessen, diskutiert. T2 - DIN NA 062-01-42 AA Meeting CY - Berlin, Germany DA - 14.06.2023 KW - Ontologie KW - Semantic Web Technologies KW - Normung KW - DIN KW - Plattform MaterialDigital KW - Zugversuch KW - Tensile Test KW - Materialcharakterisierung PY - 2023 AN - OPUS4-57803 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus T1 - Full-Notch Creep Test Ontology (ontoFNCT) N2 - This is the stable version of the full-notch creep test ontology (OntoFNCT) that ontologically represents the full-notch creep test. OntoFNCT has been developed in accordance with the corresponding test standard ISO 16770:2019-09 Plastics - Determination of environmental stress cracking (ESC) of polyethylene - Full-notch creep test (FNCT). The OntoFNCT provides conceptualizations that are supposed to be valid for the description of full-notch creep tests and associated data in accordance with the corresponding test standard. By using OntoFNCT for storing full-notch creep test data, all data will be well structured and based on a common vocabulary agreed on by an expert group (generation of FAIR data) which is meant to lead to enhanced data interoperability. This comprises several data categories such as primary data, secondary data and metadata. Data will be human and machine readable. The usage of OntoFNCT facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of full-notch creep test data is enhanced and querying in combination with other aspects and data within the broad field of materials science and engineering (MSE) is facilitated. The class structure of OntoFNCT forms a comprehensible and semantic layer for unified storage of data generated in a full-notch creep test including the possibility to record data from analysis and re-evaluation. Furthermore, extensive metadata allows to assess data quality and reliability. Following the open world assumption, object properties are deliberately low restrictive and sparse. KW - Ontology KW - Full-Notch Creep Test KW - FNCT KW - Knowledge Representation KW - Semantic Web Technologies KW - Data Structures KW - Data Management PY - 2024 UR - https://github.com/MarkusSchilling/ontoFNCT/blob/4abce82852190a5e444d302da077aa7404f433f0/ontoFNCT.ttl UR - https://raw.githubusercontent.com/MarkusSchilling/ontoFNCT/main/ontoFNCT.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-59815 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - FAIR Data in Platform MaterialDigital (PMD) - Ontologies , Semantic Data Integration and Data Exchange N2 - Following the new paradigm of materials development, design, and optimization, digitalization is the main goal in materials sciences and engineering (MSE) which imposes a huge challenge. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web technologies (SWT) are used since they allow for machine-actionable and human-readable knowledge representations needed for data management, retrieval, and (re)use. The project ‘platform MaterialDigital’ (PMD, https://materialdigital.de) aims to bring together and support interested parties from both industrial and academic sectors in a sustainable manner in solving digitalization tasks and implementing digital solutions. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. Core points to be dealt with are the development of agreements on data structures and interfaces implemented in distinct software tools and to offer users specific support in their projects. Furthermore, the platform contributes to a standardized description of data processing methods in materials research. In this respect, selected MSE methods are semantically represented which are supposed to serve as best practice examples with respect to knowledge representation and the creation of knowledge graphs used for material data. Accordingly, this presentation shows the efforts taken within the PMD project towards the digitalization in MSE such as the development of the mid-level PMD core ontology (PMDco, https://github.com/materialdigital/core-ontology). Furthermore, selected results of a PMD partner project use case addressing data and knowledge management from synthesis, production, and characterization of materials are shown. T2 - 1st VMAP User Meeting 2024 CY - Sankt Augustin, Germany DA - 14.02.2024 KW - Ontology KW - Semantic Web Technologies KW - Plattform MaterialDigital KW - Data Interoperability KW - Data Exchange KW - Data Structures PY - 2024 AN - OPUS4-59567 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Hochwertige, verlässliche und FAIRe Daten erstellen: Von Ontologien und elektronischen Laborbüchern zu Datenmanagement und Wissenstransfer N2 - Das hochaktuelle Thema der Integration und Wiederverwendung von Wissen und Daten aus Herstellung, Bearbeitung und Charakterisierung von Materialien wird im Zuge der digitalen Transformation in der Materialwissenschaft und Werkstofftechnologie in verschiedenen Projekten adressiert. Dabei stehen die Interoperabilität von nach FAIR-Prinzipien erstellten und veröffentlichten Daten und Anwendungen im Vordergrund. Zur Umsetzung eines komplexen Datenmanagements sowie der Digitalisierung im Bereich der Materialwissenschaften etablieren sich Ontologien zunehmend als belastbares Werkzeug. Sie ermöglichen sowohl menschenlesbare als auch maschinenverständliche und -interpretierbare Wissensrepräsentationen durch semantische Konzeptualisierungen. Im Rahmen des Projektes Plattform MaterialDigital (PMD, materialdigital.de) werden Ontologien verschiedener Ebenen entwickelt (verbindende mid-level sowie Domänen-Ontologien). Die PMD-Kernontologie (PMD Core Ontology - PMDco) ist eine Ontologie der mittleren Ebene), die Verbindungen zwischen spezifischeren MSE-Anwendungsontologien und domänenneutralen Konzepten herstellt, die in bereits etablierten Ontologien höherer Ebenen (top-level Ontology) verwendet werden. Sie stellt somit einen umfassenden Satz von durch Konsensbildung in der Gemeinschaft (geteiltes Vokabular) entstanden Bausteinen grundlegender Konzepte aus der Materialwissenschaft und Werkstofftechnik (MSE) dar. Das primäre Ziel des PMDco-Designs ist es, die Interoperabilität zwischen verschiedenen MSE-bezogenen und anderen Ontologien zu ermöglichen. Die PMDco dient als umfassend ausgelegte und erweiterbare semantische Zwischenschicht, die gemeinsame MSE-Konzepte durch semantisches Mapping auf andere Darstellungen vereinheitlicht, was sie zu einem effizienten Werkzeug zur Strukturierung von MSE-Wissen macht. In dieser Präsentation wird die Version 2.0 der PMDco vorgestellt. Weiterhin wird eine auf die PMDco bezogene normenkonforme ontologische Repräsentation zur Speicherung und Weiterverarbeitung von Zugversuchsdaten präsentiert, die in enger Zusammenarbeit mit Standardisierungsgremien erstellt wurde. Dies umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verbindung von Ontologie und Daten. Letztendlich können die entsprechenden Daten in einem Triple Store abgelegt und abgefragt werden. Auf Basis dieser Zugversuchsontologie wurde im Folgenden unter Verwendung eines elektronischen Laborbuches (electonic lab notebook – ELN) zur Datenaufnahme eine Praktikumsreihe von Universitätsstudierenden digitalisiert. Dadurch wurde eine vollständig digital integrierte Versuchsführung ermöglicht, die auf andere Versuchsreihen und Experimente übertragbar ist und ebenfalls vorgestellt werden soll. Neben einer erleichterten Aufnahme, Analyse und (Wieder-)Verwendbarkeit von Daten wird damit eine Sensibilisierung von Studierenden für Datenstrukturierung sowie semantische Technologien im Sinne der Aus- und Weiterbildung erreicht. T2 - DVM-Tagung Werkstoffprüfung CY - Berlin, Germany DA - 23.11.2023 KW - Ontologie KW - Semantic Web Technologies KW - Plattform MaterialDigital KW - PMDco KW - Zugversuchsontologie KW - Zugversuch KW - Elektronisches Laborbuch (ELN) PY - 2023 AN - OPUS4-59030 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - PMDco: Achieving High-Quality & Reliable FAIR Data N2 - 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 and variant 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 presentation, an approach how to maintain a comprehensive and intuitive MSE-centric terminology composing a mid-level ontology–the PMD core ontology (PMDco)–via MSE community-based curation procedures is shown. The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications. T2 - Kupfer-Symposium CY - Jena, Germany DA - 29.11.2023 KW - Ontology KW - Semantic Web Technologies KW - Plattform MaterialDigital KW - PMDco PY - 2023 AN - OPUS4-59031 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - PMDco - Platform MaterialDigital Core Ontology: Achieving High-Quality & Reliable FAIR Data N2 - 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 and variant 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 poster presentation, an approach how to maintain a comprehensive and intuitive MSE-centric terminology composing a mid-level ontology–the PMD core ontology (PMDco)–via MSE community-based curation procedures is shown. The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications. T2 - DVM Arbeitskreis Betriebsfestigkeit - Potenziale der Betriebsfestigkeit in Zeiten des technologischen und gesellschaftlichen Wandels CY - Munich, Germany DA - 11.10.2023 KW - Digitalization KW - Semantic Web Technologies KW - FAIR KW - Data Interoperability KW - PMD Core Ontology PY - 2023 AN - OPUS4-58602 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Interoperabilität und Wiederverwendung von Materialdaten: Der Weg zu Daten- und Wissensrepräsentationen in der PMD N2 - Die Integration und Wiederverwendung von Wissen und Daten aus der Herstellung, Bearbeitung und Charakterisierung von Materialien wird im Zuge der digitalen Transformation in der Materialwissenschaft und Werkstofftechnologie in verschiedenen Projekten behandelt. Dabei liegt der Fokus auf der Interoperabilität von Daten und Anwendungen, die nach den FAIR-Prinzipien erstellt und veröffentlicht werden. Zur Umsetzung eines komplexen Datenmanagements und der Digitalisierung im Bereich der Materialwissenschaften gewinnen Ontologien zunehmend an Bedeutung. Sie ermöglichen sowohl menschenlesbare als auch maschinenverständliche und -interpretierbare Wissensrepräsentationen durch semantische Konzeptualisierungen. Im Rahmen des Projektes Plattform MaterialDigital (PMD, materialdigital.de), welches in dieser Präsentation vorgestellt wird, werden Ontologien verschiedener Ebenen entwickelt (verbindende mid-level sowie Domänen-Ontologien). Die PMD-Kernontologie (PMD Core Ontology - PMDco) ist eine Ontologie der mittleren Ebene, die Verbindungen zwischen spezifischeren MSE-Anwendungsontologien und domänenneutralen Konzepten herstellt, die in bereits etablierten Ontologien höherer Ebenen (top-level Ontology) verwendet werden. Sie stellt somit einen umfassenden Satz von durch Konsensbildung in der Gemeinschaft (geteiltes Vokabular) entstanden Bausteinen grundlegender Konzepte aus der Materialwissenschaft und Werkstofftechnik (MSE) dar. In dieser Präsentation wird die PMDco adressiert. Weiterhin wird eine auf die PMDco bezogene normenkonforme ontologische Repräsentation zur Speicherung und Weiterverarbeitung von Zugversuchsdaten – die Tensile Test Ontology (TTO) – präsentiert, die in enger Zusammenarbeit mit dem zugehörigen DIN-Standardisierungsgremium NA 062-01-42 AA erstellt wurde. Dies umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verbindung von Ontologie und Daten. Auf Grundlage der dabei entwickelten Struktur und Vorgehensweise können weitere Materialcharakterisierungsmethoden semantisch beschrieben werden, woraus sich in diesem Zusammenhang Kooperationsmöglichkeiten ergeben. T2 - 20. Sitzung des NA 062-01-43 AA CY - Online meeting DA - 28.05.2024 KW - Semantische Daten KW - Plattform Material Digital KW - Ontologie KW - Normung KW - Wissensrepräsentation PY - 2024 AN - OPUS4-60178 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Digital Transformation in Materials Science: Insights from Platform MaterialDigital (PMD), Tensile Test Ontology (TTO), Electronic Lab Notebooks (ELN) N2 - The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations. T2 - NIST Seminar Series CY - Gaithersburg, MD, USA DA - 11.06.2024 KW - Semantic Data KW - Plattform Material Digital KW - Digitalization KW - Data Interoperability KW - NIST KW - Tensile Test Ontology KW - Elctronic Lab Notebook PY - 2024 AN - OPUS4-60392 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Advancing Digital Workflows in Materials Science: The Role of PMDco in Data Integration and Semantic Representation N2 - The field of Materials Science and Engineering (MSE) is undergoing a transformative shift towards digitalization, emphasizing the need for structured and interoperable data management. The Platform MaterialDigital Core Ontology (PMDco), now in version 3.0, addresses these challenges by providing a robust mid-level semantic framework. PMDco bridges the gap between abstract high-level ontologies, such as the Basic Formal Ontology (BFO) standardized in ISO/IEC 21838-2, and highly specific domain terminologies to ensure consistency and interoperability across diverse MSE applications. Developed through MSE community-based curation, PMDco facilitates the integration of real-world data from experiments, simulations, and industrial processes. This presentation will explore PMDco's role in enabling advanced digital workflows and its integration into demonstrators within the Platform MaterialDigital (PMD) initiative. Highlighted use cases include the semantic representation of tensile test data in compliance with ISO 6892-1:2019-11, utilizing the corresponding tensile test ontology (TTO) built on PMDco. Through an electronic laboratory notebook (ELN), data from experiments performed by undergraduate students were transformed into machine-actionable knowledge graphs, demonstrating the potential for education and fully digitalized experimental procedures. Additionally, a possible extension of PMDco as a linking point for semantically representing simulation data will be presented, aligning with the focus of VMAP. This includes linking experimental, simulation, and computational datasets to create comprehensive, FAIR-compliant knowledge ecosystems. By showcasing best practices in data acquisition, semantic integration, and knowledge graph generation, this presentation underscores PMDco’s versatility and its critical role in advancing digital MSE workflows. T2 - VMAP User Forum 2025 CY - Sankt Augustin, Germany DA - 18.02.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2025 AN - OPUS4-62607 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -