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This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries.
This study aims to represent an approach for transferring the materials testing datasets to the digital schema that meets the prerequisites of the semantic web. As a use case, the tensile stress relaxation testing method was evaluated and the testing datasets for several copper alloys were prepared. The tensile stress relaxation testing ontology (TSRTO) was modeled following the test standard requirements and by utilizing the appropriate upper-level ontologies. Eventually, mapping the testing datasets into the knowledge graph and converting the data-mapped graphs to the machine-readable Resource Description Framework (RDF) schema led to the preparation of the digital version of testing data which can be efficiently queried on the web.
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.
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.
PMD Core Ontology (PMDco)
(2023)
The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. PMDco's class structure is both understandable and extensible, making it an efficient tool for organizing MSE knowledge. It serves as a semantic intermediate layer that unifies MSE knowledge representations, enabling data and metadata to be systematically integrated on key terms within the MSE domain. With PMDco, it is possible to seamlessly trace data generation. The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the generation, derivation, and attribution of resources. By building on this foundation, PMDco facilitates the integration of data from various sources and the creation of complex workflows. In summary, PMDco is a valuable tool for researchers and practitioners in the MSE domains. It provides a common language for representing and sharing knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless traceability of data generation. Overall, PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain. PMDco at GitHub: https://github.com/materialdigital/core-ontology
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.
Suitable material solutions are of key importance in designing and producing components for engineering systems – either for functional or structural applications. Materials data are generated, transferred, and introduced at each step along the complete life cycle of a component. A reliable materials data space is therefore crucial in the digital transformation of an industrial branch.
A great challenge in establishing a materials data space lies in the complexity and diversity of materials science and engineering. It must be able to handle data from different knowledge areas over several magnitudes of length scale.
The Platform MaterialDigital (PMD) is expected to network a large number of repositories of materials data, allowing the direct contact of different stakeholders as materials producers, testing labs, designers and end users. Following the FAIR principles, it will promote the semantic interoperability across the frontiers of materials classes. In the frame of a large joint initiative, PMD
works intensively together with currently near 20 research consortia in promoting this exchange (www.material-digital.de).
In this presentation we will describe the status of our Platform MaterialDigital. We will also present in more detail the activities of GlasDigital, one of the joint projects mentioned above dealing with the digitalization of glass design and manufacturing.
(https://www.bam.de/Content/EN/Projects/GlasDigital/glasdigital.html)
Mechanical testing ontology
(2023)
The materials mechanical testing ontology (MTO) was developed by collecting the mechanical testing vocabulary from ISO 23718 standard, as well as the standardized testing processes described for various mechanical testing of materials like tensile testing, Brinell hardness test, Vickers hardness test, stress relaxation test, and fatigue testing. Confirming the ISO/IEC 21838-2 standard, MTO utilizes the Basic Formal Ontology (BFO), Common Core Ontology (CCO), Industrial Ontologies Foundry (IOF), Quantities, Units, Dimensions, and data Types ontologies (QUDT), and Material Science and Engineering Ontology (MSEO) as the upper-level ontologies.
The materials mechanical testing ontology (MTO) was developed by collecting the mechanical testing vocabulary from ISO 23718 standard, as well as the standardized testing processes described for various mechanical testing of materials like tensile testing, Brinell hardness test, Vickers hardness test, stress relaxation test, and fatigue testing. Confirming the ISO/IEC 21838-2 standard, MTO utilizes the Basic Formal Ontology (BFO), Common Core Ontology (CCO), Industrial Ontologies Foundry (IOF), Quantities, Units, Dimensions, and data Types ontologies (QUDT), and Material Science and Engineering Ontology (MSEO) as the upper-level ontologies. Reusing these upper-level ontologies and materials testing standards not only makes MTO highly interoperable with other ontologies but also ensures its acceptance and applicability in the industry. MTO represents the mechanical testing entities in the 230 classes and four main parts: i) Mechanical testing experiments entities like tensile, hardness, creep, and fatigue tests as the subclasses of mseo:Experiment, ii) Mechanical testing quantity concepts such as toughness, elongation, and fatigue strength in the appropriate hierarchies of bfo:Disposition and bfo:Quality classes, iii) Mechanical testing artifacts like indenter as the subclasses of cco:Artifact, and iv) mechanical testing data like the stress-strain, S-N, or creep curves as the subclasses of cco:InformationContentEntity. MTO is publicly available via the KupferDigital GitLab repository.
The PMD Core Ontology (PMDco) is a comprehensive set of building blocks produced via consensus building. The ontological building blocks provide a framework representing knowledge about fundamental concepts used in Materials Science and Engineering (MSE) today. The PMDco is a mid-level ontology that establishes connections between narrower MSE application ontologies and domain neutral concepts used in already established broader (top-level) ontologies. The primary goal of the PMDco design is to enable interoperability between various other MSE-related ontologies and other common ontologies.
PMDco’s class structure is both comprehensive and extensible, rendering it an efficient tool to structure MSE knowledge. The PMDco serves as a semantic middle-layer unifying common MSE concepts via semantic mapping to other semantic representations using well-known key terms used in the MSE domain. The PMDco enables straight-forward documentation and tracking of science data generation and in consequence enables high-quality FAIR data that allows for precise reproducibility of scientific experiments.
The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the production, derivation, and attribution of resources. Via this foundation, the PMDco enables the integration of data from various data origins and the representation of complex workflows.
In summary, the PMDco is a valuable advancement for researchers and practitioners in MSE domains. It provides a common MSE vocabulary to represent and share knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless tracing of science data. Overall, the PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain in general.
This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries.
Tensile Test Ontology (TTO)
(2023)
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.
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.
Towards interoperability: Digital representation of a material specific characterization method
(2023)
Certain metallic materials gain better mechanical properties through controlled heat treatments. In age-hardenable aluminum alloys, the strengthening mechanism is based on the controlled formation of nanometer sized precipitates, which hinder dislocation movement. Analysis of the microstructure and especially the precipitates by transmission electron microscopy allows identification of precipitate types and orientations. Dark-field imaging is often used to image the precipitates and quantify their relevant dimensions.
The present work aims at the digital representation of this material-specific characterization method. Instead of a time-consuming, manual image analysis, a digital approach is demonstrated. The integration of an exemplary digital workflow for quantitative precipitation analysis into a data pipeline concept is presented. Here ontologies enable linking of contextual information to the resulting output data in a triplestore. Publishing digital workflow and ontologies ensures the reproducibility of the data. The semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability.
Das hochaktuelle Thema der Integration und Wiederverwendung von Wissen und Daten aus Herstellung, Bearbeitung und Charakterisierung von Materialien ('Digitalisierung von Materialien') wird in den Projekten Innovationsplattform MaterialDigital (PMD, materialdigital.de) und Materials-open-Lab (Mat-o-Lab, matolab.org) adressiert. In diesem Beitrag werden die Weiterentwicklungen in diesen Projekten hinsichtlich der Speicherung von Zugversuchsdaten gemäß einer normenkonformen (DIN EN ISO 6892-1:2019-11) ontologischen Repräsentation vorgestellt. Das umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verknüpfung von Ontologie und Daten. Letztendlich können die entsprechenden Daten in einem Triple Store abgelegt und abgefragt werden.
Efforts towards digitalization in the material science and technology community have enhanced in the last years. In 2019 the German digitalization initiative platform „MaterialDigital“1 (MD) has been started. Numerous projects concerning digitalization, including the copper related project „KupferDigital“ (copper digital) have been initiated under the umbrella of MD. The initiative strives to
address numerous issues concerning data access, exchange, security, provenance and sovereignty. Heterogeneous data origin, storage and evaluation often result in problems concerning comparability and reproducibility of scientific and technological results. In many cases material data are recorded, but the methods of testing are insufficiently described, or such information is not communicated along with the raw data. The material data can also have numerous different formats such as paper printouts, pdfs, excel sheets or csv-files. Hence, gathering and integrating material data from different sources is challenging for potential users like materials scientists and engineers, especially if there are contradictory data where the reasons for contradictions is not clear due their vague description. In order to address these problems, data should comply to the so called „FAIR“ principle which calls for data to be findable, accessible, interoperable, and reusable (FAIR)2 and hence be accessible via so-called decentralized but interconnected data spaces. By using knowledge representation withontologies, data can be enriched with meaning and the methods of the testing procedures can be accurately provided. In this presentation we want to introduce our approach to such knowledge representation based on a high-throughput alloy development process for Cu-based alloys³ along with characterization techniques such as hardness testing and microstructural characterization (e.g. EBSD – Electron Backscattered Diffraction).
A large amount of publicly available data is reproduced every day in the field of materials science, while these kind of material data can have different formats and types like paper-type publications, standards, datasheets or isolated datasets in repositories. However, gathering a specific library from such extensive and diverse material data is always challenging for the materials scientists and engineers, since the time-related limitations are not allowed to fully access the large publicly available databases; search across these disparate databases, manage the large volumes of heterogeneous datasets, and integrate data from multiple sources. To address these challenges and make data findable, accessible, interoperable, and reusable (FAIR), an efficient data management system is necessary to build comprehensive, documented, and connected data spaces in the future. A formal standardized knowledge representation through an ontology can address such problems and make data more available and interoperable between related domains. Ontology can also rich machine processable semantic descriptions that increases the performance of scientific searches. In this regard, the Platform MaterialDigital (PMD) is currently working on developing a high-level ontology for the materials and material related processes. For example, in one of the PMD projects of “KupferDigital”, we will try to develop a data ecosystem for digital materials research based on ontology-based digital representations of copper and copper alloys. As a case study, this paper describes the methodology for ontology development of Brinell hardness, based on PMD core ontology. The methodology we describe includes the following steps; gathering the required domain terminology from different resources like standards (DIN EN ISO 6506-1) and test reports, representing the performance of a standard-conformant hardness test and the treatment of the recorded values up to a “reportable” hardness value for a material, designing the process chain according to the semantic technologies, and developing a domain-level ontology of Brinell hardness based on PMD ontology semantic formalization. Apart the mentioned methodology, some interesting tools and methods were introduced and ontology design challenges and possible solutions for modelling materials and processes were discussed. Furthermore, a dataset from the Brinell hardness measurement of cast copper samples is prepared for testing the query process.
Towards Interoperability: Digital Representation of a Material Specific Characterization Method
(2022)
Certain metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminum alloys, the strengthening mechanism is based on the controlled formation of nanometer sized precipitates, which represent obstacles to dislocation movement. Precise tuning of the material structure is critical for optimal mechanical behavior in the application. Therefore, analysis of the microstructure and especially the precipitates is essential to determine the ideal parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in a first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and to quantify their relevant dimensions.
The present work aims at the digital representation of this material-specific characterization method. Instead of a time-consuming, manual image analysis, an automatable, digital approach is demonstrated. Based on DF-TEM images of different precipitation states of a wrought aluminum alloy, a modularizable digital workflow for quantitative precipitation analysis is presented. The integration of this workflow into a data pipeline concept will also be discussed. Thus, by using ontologies, the raw image data, their respective contextual information, and the resulting output data from the quantitative precipitation analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure the reproducibility of the data. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability.
The presented work is part of two digitization initiatives, the Platform MaterialDigital (PMD, materialdigital.de) and Materials-open-Laboratory (Mat-o-Lab).
Ontologies that represent a map of the concepts and relationships between them, are becoming an effective solution for data standardization and integration of different resources in the field of materials science, as efficient data storage and management is the building block of material digitization. However, building a domain ontology is not a simple task. It requires not only a collaborative effort between ontologists and domain experts, but also the modeling approaches and tools play a key role in the process. Among all approaches, graphical representation of domain ontologies based on standard conceptual modeling languages is widely used because of its intuitiveness and simplicity. Various tools have been developed to realize this approach in an intuitive way, such as Protégé plugins and web visualization tools.
The Materials-open-Lab (MatOLab) project, which aims to develop ontologies and workflows in accordance with testing standards for the materials science and engineering domains, adopted a UML (Unified Modeling Language) approach based on the diagrams.net. It is a powerful, popular, open-source graphical editor. In practical case studies, however, many users’ needs could not be met, such as reusing ontology, conversion, and data mapping. Users must switch between different tools to achieve a certain step, and thereby invariably increase learning cost. The lack of validation also leads to incorrect diagrams and results for users who are not familiar with the ontology rules. To address these issues, we designed Ontopanel, a diagrams.net-based plugin that includes a set of pipeline tools for semantic modeling: importing and displaying protégé-like ontologies, converting diagrams to OWL, validating diagrams by OWL rules, and mapping data. It uses diagrams.net as the front-end for method modeling and Django as the back-end for data processing. As a web-based tool, it is very easy to expand its functionality to meet changing practical needs.
Following the new paradigm of materials development, design and optimization, the digitalization of materials and processes is the main goal 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 (findability, accessibility, interoperability, reusability) principles are to be ensured. This includes storage, processing and querying of data in a preferably standardized form, also addressing the incorporation of standardization bodies. To meet the challenge to contextualize material data in a way that is consistent with all stakeholders, all necessary information on the condition of the material including production and application-related changes have to be made available via a uniform, machine-readable description. For this purpose, ontologies are to be used since they allow for machine-understandable knowledge representations and semantic conceptualizations that are needed for data management and the digitalization in the field of materials science.
With respect to this currently ever-growing topic of integration and reuse of data and knowledge from synthesis, production and characterization of materials, this presentation shows the efforts taken within the project Platform MaterialDigital (PMD, materialdigital.de) to store tensile test data in accordance with a standard-compliant ontological representation. The includes the path from developing an ontology in accordance with the respective standard, converting ordinary and arbitrarily selected data gained from standard tests into the interoperable RDF format, up to connecting the ontology and data, respectively. Finally, such data can be queried from a triple store.
In the field of material science and engineering (MSE), most mechanical test methods are standardized which serves as a valid basis for ontology development. Therefore, the well-known tensile test of metals at room temperature (DIN EN ISO standard 6892-1:2019-11) was selected to be considered as one of the first use cases in PMD. This consideration within the PMD features both, the ontological representation of such a tensile test in accordance with the standard as well as exemplary data generation. The semantic connection of the ontology and data leads to interoperability and an enhanced ability of querying.
Due to the diversity of materials and the processes associated with their production and use, the complexity of the lifecycles of materials and the multitude of academic and industrial researchers participating in generation of data for material design impose a huge challenge. The topical goal of digitalizing materials and processes can only be adequately addressed by consolidating the efforts of all stakeholders in this field. There are many scattered activities, but there is a demand for an elimination of redundancies as well as an advance in acceptance and a common basis in the digitalization of materials. Furthermore, data analysis methods play an important role in both, the experimental and simulation-based digital description of materials, but they have been poorly structured so far.
Therefore, the two joint projects Platform Material Digital (PMD, materialdigital.de) and Materials open Laboratory (Mat-o-Lab, matolab.de) aim to contribute to a standardized description of data processing methods in materials research. Besides stimulating the formation of a collaborative community in this respect, their main technical goals are the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications. In this regard, data management in accordance with the FAIR (findability, accessibility, interoperability, reuseability) principles is addressed. There is a common agreement in the scientific community following current discussions that data is supposed to be conform to these principles. This includes storage, processing and querying of data in a preferably standardized form.
To meet the challenge to contextualize material data in a way that is consistent with all stakeholders, all necessary information on the condition of the material including production and application-related changes have to be made available via a uniform, machine-readable description. For this purpose, ontologies are to be used since they allow for machine-understandable knowledge representations and conceptualizations that are needed for data management and the digitalization in the field of materials science.
As first efforts in PMD and Mat-o-Lab, application ontologies are created to explicitly describe processes and test methods. Thereby, the well-known tensile test of metals at room temperature was described ontologically in accordance with the respective ISO standard 6892-1:2019-11.
The efforts in creating this tensile test application ontology are shown in this presentation. Especially, the path of ontology development based on standards to be pursued is focused, which is in accordance with the generic recommendations for ontology development and which is supposed to be exemplary for the creation of other application ontologies.
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.
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.