Resource Description Framework Research Articles

BG-LoA: A Benchmarking Framework for BIM/GIS Data Integration Based on Meta-Modelling Theory

Abstract. There are various studies on the integration of building information modelling (BIM) and geographic information system (GIS) that convert BIM data into GIS data. These studies used different technologies, data standards, data models, and data formats such as RDF (Resource Description Framework), graph, CityGML, and Shapefile to address the data incompatibility problem between BIM and GIS. This makes it difficult to compare these studies for the purpose of assessing research innovation, and sometimes, improper comparisons were carried out between studies. To solve this problem, this study adopted the metamodeling theory used in model-driven software engineering (MDSE) and extended its 4-layer framework of level of abstraction. The new benchmarking framework, referred to as level of abstraction for BIM/GIS data integration (BG-LoA), has 5 levels, including meta metamodel (M3), metamodel (M2), model (M1), reality (data) (M0), and reality (real world) (M00). The common types of IFC (Industry Foundation Classes) conversion were analysed using the new framework, including IFC-to-CityGML, IFC-to-CityJSON, IFC-to-Shapefile, IFC-to-GML, IFC-to-3DTiles, IFC-to-RDF, IFC-to-IfcGraph, and IFC-to-UML. The result shows that the new framework can show the difference between those conversion types, which makes it easier for future studies to benchmark their work and assess level of contribution to the body of knowledge.

Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects

Clustering is a very common means of analysis of the data present in large datasets, with the aims of understanding and summarizing the data and discovering similarities, among other goals. However, despite the present success of the use of subsymbolic methods for data clustering, a description of the obtained clusters cannot rely on the intricacies of the subsymbolic processing. For clusters of data expressed in a Resource Description Framework (RDF), we extend and implement an optimized, previously proposed, logic-based methodology that computes an structure—called a Common Subsumer—describing the commonalities among all resources. We tested our implementation with two open, and very different, datasets: one devoted to public procurement, and the other devoted to drugs in pharmacology. For both datasets, we were able to provide reasonably concise and readable descriptions of clusters with up to 1800 resources. Our analysis shows the viability of our methodology and computation, and paves the way for general cluster explanations to be provided to lay users.

Persons in Context. A Model to Represent Observations and Reconstructions of Historical Persons in Linked Data

Reconstruction of historical persons and family ties is the bread and butter of many researchers and genealogists. With the increasing digital availability of historical person records, the scope and depth of person reconstructions speaks to the imagination of researchers and genealogists. Yet, the lack of standardisation in the description of historical person data has hurt the interoperability and sustainability of both small and large databases. Persons in Context, or PiCo, presents a data model for historical person records within the Resource Description Framework (RDF). RDF or Linked Data is specifically designed for clear, unambiguous information exchange between multiple parties over the internet. We show how reuse of existing ontologies and concentric description are the building blocks of a flexible, straightforward, and stringent data model that emphasises provenance.

Designing Semantic Tutoring Maps for Enhancing Peer Learning through Lesson Preparation

Advancements in Natural Language Processing technology present significant opportunities to enhance student learning through peer tutoring. While there is substantial research on the effectiveness of peer tutoring in learning science, few studies focus on the lesson preparation stage of peer tutoring. This study utilizes Semantic Web technologies to organize tutoring maps, aiming to develop an innovative learning artifact to address this research gap. Adopting a design-based research methodology, this study first provides a literature review regarding the application of semantic technology in education and peer tutoring preparation. Subsequently, a tutoring map application based on a semantic database is developed, encompassing the organization and storage of tutoring lesson plans using Resource Description Framework (RDF) technology, and the querying and feedback on lesson plan quality based on SPARQL Protocol and RDF Query Language (SPARQL) technology. This paper describes the theoretical basis and design rationale by which student tutors’ learning is improved through structured tutoring preparation. This includes enhancing cognitive and meta-cognitive abilities in designing learning activities, raising questions, anticipating answers, and providing guidance responses to incorrect answers. The paper concludes with recommendations for educational researchers and practitioners. Future research will focus on empirical studies of peer tutoring in scientific fields to verify the validity and adaptability of the design, further facilitating peer learning by tutoring and collective knowledge building.

Integrating Semantic Web, Artificial Intelligence, and Knowledge Management for Enhanced Accounting in SMEs

This study explores the integration of Semantic Web (SW), Artificial Intelligence (AI), and Knowledge Management (KM) to improve accounting practices in small and medium-sized enterprises (SMEs). Recognizing the challenges SMEs face in managing accounting knowledge due to insufficient expertise and fragmented data sources, this research proposes a conceptual model that utilizes SW technologies, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), to create a unified ontology for diverse accounting information. By leveraging AI techniques, including machine learning and natural language processing, the model aims to enhance knowledge retrieval, ontology enrichment, and case-based reasoning, facilitating more efficient and accurate accounting knowledge management. The findings highlight the potential of this integrated approach to support online training, decision-making, and overall competitiveness of SMEs, addressing the limitations of traditional accounting practices. The study concludes with recommendations for further research to refine the integration of SW, AI, and KM in accounting.

Application of a Question Answering System in the Ontology-Based Health Domain and Question Templates to Improve the Quality of Answers

Developing an effective Question Answering System (QAS) to address a wide range of health-related inquiries presents a significant challenge. This study introduces an innovative approach using an ontology-based QAS within the health domain, leveraging the Resource Description Framework (RDF) and SPARQL query language. By utilizing ontology, the system can more accurately map health concepts, improving the relevance and precision of its responses. Additionally, the incorporation of structured Question Templates enhances the system’s ability to understand and respond to diverse user queries. The system's performance was evaluated using Black Box Testing, which demonstrated that it consistently provides accurate and relevant answers. The system achieved a Mean Average Precision (MAP) of 79.6%, indicating its potential to effectively address a broad spectrum of health issues.

Digital Representation of Materials Testing Data for Semantic Web Analytics: Tensile Stress Relaxation Testing Use Case

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.

ForestQB: Enhancing Linked Data Exploration through Graphical and Conversational UIs Integration

This article introduces the Forest Query Builder (ForestQB), an innovative toolkit designed to enhance the exploration and application of observational Linked Data (LD) within the field of wildlife research and conservation. Addressing the challenges faced by non-experts in navigating Resource Description Framework (RDF) triplestores and executing SPARQL queries, ForestQB employs a novel integrated approach. This approach combines a graphical user interface (GUI) with a conversational user interface (CUI), thereby greatly simplifying the process of query formulation and making observational LD accessible to users without expertise in RDF or SPARQL. Developed through insights derived from a comprehensive ethnographic study involving wildlife researchers, ForestQB is specifically designed to improve the accessibility of SPARQL endpoints and facilitate the exploration of observational LD in wildlife research contexts. To evaluate the effectiveness of our approach, we conducted a user experiment. The results of this evaluation affirm that ForestQB is not only efficient and user-friendly but also plays a crucial role in eliminating barriers for users, facilitating the effective use of observational LD in wildlife conservation and extending its benefits to wider domains. (GitHub Link: github.com/i3omar/ForestQB).

A fine-grained RDF graph model for fuzzy spatiotemporal data

The uncertainty and spatiotemporal dynamics of information necessitate the urgent modeling of fuzzy spatiotemporal knowledge across various applications, with the Resource Description Framework (RDF) serving as a widely recognized data representation model. Existing models suffer from incomplete semantic representation and poor robustness in modeling fuzzy spatiotemporal data, e.g., lack of fuzziness in spatiotemporal semantics; lack of altitude in spatial semantics. Meanwhile, the algebraic framework regarding the model has not been investigated. Thus, in this paper, we first propose a new fine-grained fuzzy spatiotemporal RDF model. This model can represent fine-grained uncertain spatiotemporal semantics that may be associated with any element of a spatiotemporal RDF. We further define its graph algebraic operations. Note that we demonstrate the use of the algebraic operations for fuzzy spatiotemporal RDF querying. Finally, we establish a set of transformation rules for SPARQL query syntax to algebraic operations in fuzzy spatiotemporal RDF. In addition, we used experiments to evaluate the validity and rationality of our model.

Plinian Core: A Data Specification for Species Pages in the Real World

Plinian Core (PliC), a standard in development by a Biodiversity Information Standards (TDWG) task group, is a set of vocabulary terms that can be used to describe different aspects of biological species information, including all kinds of properties or traits related to taxa—biological and non-biological. PliC incorporates terms pertaining to descriptions, legal aspects, conservation, management, uses, demographics, nomenclature, or related resources (Plinian Core Task Group 2021). Having a data specification is just a small part of what it takes to prepare and publish species information. The specification provides a structured way to describe species-related data, ensuring consistency and interoperability across different platforms and databases. Different aspects of Plinian Core and its development have been presented in the past (Pando 2018; Pando et al. 2022). In this contribution, we shift the focus out of the specification itself, to other key aspects of species information dissemination. Plinian Core has been already put to use in various real-world scenarios. Here we select three examples to present the most significant lessons learned: Cross-Nature Project This project developed a Resource Description Framework for PliC to create an ontology-based endpoint for the Species Information System of the Spanish Ministry for the Environment (EIDOS). It provides a structured framework for describing and integrating species data following the Linked Open Data principles. The system provides information for over 85,000 species. Biodiversity Catalogue of SiB Colombia This national catalogue provides information on the natural history, conservation, threats, and biology of Colombian species. Aimed at a broad audience, it maintains scientific rigor while being accessible to anyone interested in Colombia's biodiversity. PliC has been essential in this endeavor, enabling the inclusion of both highly structured technical information and rich, engaging textual descriptions for over 6,000 species. EncicloVida Developed by CONABIO, EncicloVida aims to publicize the Mexican biodiversity. The platform integrates biological species information on more than 114,000 species of plants, animals, fungi, bacteria, and protozoa that CONABIO has gathered through the National Information System on Biodiversity (SNIB) in collaboration with Mexican researchers. These implementations are based on the PliC Core abstract Model that is expressed as an XML Schema definitiion (XSD), and the supporting documentation is provided in the PliC Wiki. Automatic aggregation of species pages is challenging and often ineffective as differences in taxonomic treatments, or in local populations (either biological or otherwise: threat status, laws and regulations, etc.) frequently create inconsistencies or even render false information. Conversely, sharing a standard across projects offers immediate benefits, such as consistency of navigation and information, or the facility for sharing software solutions. Plinian Core terms, vocabularies and structure have been developed in collaboration with implementers and in response to real-world demands. That has resulted in a set of fairly large data specifications, but one flexible and easy to understand by domain experts who may not work comfortably with more technical approaches. Each implementation puts emphasis on different aspects, applying different levels of granularity depending on the requirements. This is done by choosing between "unstructured" terms (for low granularity) or the "atomized classes'' (for highly structured information). For instance, dispersal information can be recorded as a text block ("DispersalUnstructured") or using a set of specific terms: Purpose, DispersalMeans, StructureDisperse ("DispersalAtomized"). PliC reuses a number of terms from other standards (see Borrowed terms section) following a well-established best practice. Using XSD files as a basis, we link borrowed terms from external sources to their original specifications, which were also expressed as XSD files. This should provide consistency and reliability. However, this has proven to be problematic to maintain and validate, as links are broken and files changed. To keep the PliC XSD file valid, we opted for maintaining local copies of the XSD of the respective reused standards. We also aim to present some current and future developments of PliC, especially in the context of the Living Atlases community. Thus, we will explore other elements involved in sharing species information such as the Global Biodiversity Information Facility's Integrated Publishing Toolkit extensions, SPARQL Endpoints, Frictionless Data Packages, Research Object-Crates, and integration with Atlas of Living Australia modules.

A Semantic Digital Twin for the Dynamic Scheduling of Industry 4.0-based Production of Precast Concrete Elements

Precast concrete construction enhances project efficiency, sustainability, and durability by leveraging a controlled production environment that ensures high-quality outputs. Still, the sequential nature of off-site production, encompassing casting, curing, and storage of the precast elements, is subject to significant uncertainties, including supply chain variability and environmental factors affecting curing times. Dynamic adjustments to the production schedule are essential for aligning with real-time changes, necessitating a robust framework for real-time data acquisition and analysis. Dynamic Scheduling (DS) is a responsive and adaptive approach that accommodates real-time changes, optimizes the production flow, and minimizes downtime or delays. However, the DS approach demands real-time data to quickly and efficiently respond to unforeseen challenges, which requires acquiring and analyzing production-relevant data throughout the production process. The Digital Twin (DT) emerges in Industry 4.0 (I4.0) as a bridge between physical operations and digital capabilities, enabling a seamless flow of information, for which the Asset Administration Shell (AAS) is a reference implementation. This study introduces a DS framework to optimize precast element production, utilizing a DT for real-time data aggregation across the production system. The framework implements a Semantic DT based on the AAS and the Linked Data approach. It employs Resource Description Framework (RDF) serialization of the AAS and an ontological representation of the production system for data integration. The framework leverages a simulation-based scheduler, which exchanges data with the DT in a Service-oriented Architecture (SoA) using SPARQL, a language for querying and updating graph databases. The approach is evaluated through a proof of concept, demonstrating effective uncertainty management in a dynamic production environment.

Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation.

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

Different negations and fuzziness in Web resources and resource description and their mathematical foundation

In Web resources and resource descriptions, many resources and descriptions of resources have different negations and fuzziness. In this paper, we investigate and analyze the cognition and characteristics of “negation” in philosophy, logic, linguistics and other knowledge fields, and based on the distinction between the concept of “contradiction” and the concept of “opposition”, and “clear concept” and “fuzzy concept”, we propose that there are three different kinds of negation in knowledge from the conceptual level, namely “contradictory negation”, “opposite negation” and “intermediary negation”. In order to establish a mathematical basis that can fully reflect the internal relations, properties and laws of these different negations and fuzziness, we propose a “Scoi: sets with contradictory negation, opposite negation and intermediary negation” and “Lcoi&PLcoi: logic with contradictory negation, opposite negation and intermediary negation”, and the operational properties of Scoi and the formal deduction properties of Lcoi have been proven. The soundness theorem, completeness theorem and compactness theorem for Lcoi are proved under the three-valued semantic interpretation of Lcoi, and the soundness and completeness for Lcoi&PLcoi are discussed under the given an infinite-valued semantics interpretation of Lcoi&PLcoi. Furthermore, we also demonstrated that descriptions of objective things such as “x is neither a positive nor a negative integer” and “he is neither rich nor poor” typle of statements are “intermediary negation” in Scoi and Lcoi&PLcoi. It shows that “intermediary negation” in Scoi and Lcoi&PLcoi has objectively.In order to enable RDF(Resource Description Framework) to describe Web resources with different negations and fuzziness, we extend RDF based on Scoi and Lcoi&PLcoi, and propose a resource description framework RDFcoi which can fully handle fuzzy attribute values and their different negations in attributes of resources. How to reflect the uncertainty of the relationship between Web resources and its attribute values, we believe that the relationship between a resource and fuzzy attribute value in one of its attributes should be a composite mapping from the resource domain to the fuzzy attribute value set and then to [0, 1], and a mathematical representation is given in this paper. Thus, RDFcoi differ from other RDF extensions. RDFcoi maintains the basic syntax and normative of RDF, and extends the basic syntax and semantics of RDF. Where, the concepts of RDFcoi triple with three kinds of negation, RDFcoi formula and RDFcoi graph are defined. RDFcoi vocabulary is determined and an interpretation is given. RDFcoi-entailment is defined and its properties are discussed. In addition, we also study the reasoning regimes of RDFcoi. By using the given logical translation method, a well-typed and interoperable RDFcoi graph can be transformed into a set of formulas in Lcoi&PLcoi, thus establishing “interoperability” between RDFcoi and Lcoi&PLcoi. From the perspective of reasoning mechanism of RDFcoi-entailment, RDFcoi reasoning can be carried out in Lcoi&PLcoi, and RDFcoi has the same reasoning mechanism as formal inference in Lcoi&PLcoi.

Integrating adaptive fuzzy embedding with topology and property hypergraphs: Enhancing membership degree-aware knowledge graph reasoning

Knowledge Graphs (KG) leverage the Resource Description Framework (RDF) to model clear and static resources, enabling the revelation of latent meanings through embedding and reasoning. However, their effectiveness diminishes when facing the inherent vagueness and dynamism of real-world information. To overcome this limitation, we introduce a novel adaptive fuzzy RDF KG embedding approach. This method represents fuzzy knowledge by integrating topological structures and descriptive properties while generating membership degrees to enhance the reasoning capabilities of KG. Specifically, our approach introduces a way to create embeddings based on local and global topological pattern mining. Then, path quality-based adaptive learning is implemented. Next, we incorporate entity descriptive information and construct a property hypergraph from another perspective to learn embeddings. By creatively combining these three components, our method can generate a fuzzy membership degree to aid knowledge reasoning. To thoroughly assess the method's effectiveness, experiments were conducted around four key questions, evaluating algorithm performance compared with other work and assessing knowledge reasoning effectiveness based on logical inference rules on Unmanned Aerial Vehicle (UAV) datasets.

Digital Twin Smart City: Integrating IFC and CityGML with Semantic Graph for Advanced 3D City Model Visualization

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard’s levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study’s results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning.

Semantic precision: crafting RDF-based digital editions for unveiling the layers of historical correspondence

Abstract This article proposes that Resource Description Framework (RDF) technology is well suited for representing and storing inherently connected epistolary data. We justify this proposition by creating an RDF-based digital edition of the correspondence of members of the Bernoulli dynasty and Leonhard Euler and depicting the benefits this type of edition provides, which are hardly achievable otherwise. We describe the ontologies defined to fully represent letters and illustrate how we formulated letters’ historical, scientific content, rich with mathematical formulae, markups, figures, and references, as RDF statements to facilitate complex queries common in the humanities field. Next, we outline the tools we developed for presenting these editions in an efficient and user-friendly form, hiding the complexity of the data structure from non-technical users while offering sophisticated analysis features to explore atoms of knowledge and their relations. Additionally, since the citability and durability of the studied digital sources play a crucial role for scholars who publish research on the editions, we explain the steps we undertook to ensure the sustainability of the RDF-based editions and to offer persistent citation possibility.

Analyzing workload trends for boosting triple stores performance

The Resource Description Framework (RDF) is widely used to model web data. The scale and complexity of the modeled data emphasized performance challenges on the RDF-triple stores. Workload adaption is one important strategy to deal with those challenges on the storage level. Current workload-adaption approaches lack the necessary generalization of the problem and only optimize part of the storage layer with the workload (mostly the replication). This creates a big performance gap within other data structures (e.g. indexes and cache) that could heavily benefit from the same workload adaption strategy. Moreover, the workload statistics are built collectively in most of the current approaches. Thus, the analysis process is unaware of whether workloads’ items are old or recent. However, that does not simulate the temporal trends that exist naturally in user queries which causes the analysis process to lag behind the rapid workload development. We present a novel universal adaption approach to the storage management of a distributed RDF store. The system aims to find optimal data assignments to the different indexes, replications, and join cache within the limited storage space. We present a cost model based on the workload that often contains frequent patterns. The workload is dynamically and continuously analyzed to evaluate predefined rules considering the benefits and costs of all options of assigning data to the storage structures. The objective is to reduce query execution time by letting different data containers compete on the limited storage space. By modeling the workload statistics as time series, we can apply well-known smoothing techniques allowing the importance of the workload to decay over time. That allows the universal adaption to stay tuned with potential changes in the workload trends.

Semantic requirements construction using ontologies and boilerplates

This paper presents a combination of an ontology and boilerplates, which are requirements templates for the syntactic structure of individual requirements that try to alleviate the problem of ambiguity caused using natural language and make it easier for inexperienced engineers to create requirements. However, still the use of boilerplates restricts the use of natural language only syntactically and not semantically. Boilerplates consists of fixed and attributes elements. Using ontologies, restricts the vocabulary of the words used in the requirements boilerplates to entities, their properties and entity relationships that are semantically meaningful to the application domain, leading thus to fewer errors. In this work we combine the advantages of boilerplates and ontologies. Usually, the attributes of boilerplates are completed with the help of the ontology. The contribution of this paper is that the whole boilerplates are stored in the ontology, based on the fact that RDF triples have similar syntax to the boilerplate syntax, so that attributes and fixed elements are part of the ontology. This combination helps to construct semantically and syntactically correct requirements. The contribution and novelty of our method is that we exploit the natural language syntax of boilerplates mapping them to Resource Description Framework triples which have also a linguistic nature. In this paper we created and present the development of a domain-specific ontology as well as a minimal set of boilerplates for a specific application domain, namely that of engineering software for an ATM, while maintaining flexibility on the one hand and generality on the other.

Embedding-based entity alignment between multi-source temporal knowledge graphs

The goal of entity alignment is to identify entities in two multi-source knowledge graphs (KGs) that represent the same real-world object. Recent researches on multi-source entity alignment mainly concentrate on static KGs. In fact, temporal KGs have become valuable resources for numerous artificial intelligence applications, and entity alignment between multi-source temporal KGs is becoming more and more important. Current entity alignment models cannot support temporal tasks and fail to deal with the attributes with low literal similarity that share the same semantics through attribute embedding. Therefore, we propose a RDF (Resource Description Framework)-based model for representing temporal KGs, and an embedding-based entity alignment method for multi-source temporal KGs. This method computes for the similarity of temporal information and generates aligned attribute pairs in the predicate alignment module. We design an interactive module to make matched attributes and the matched entities help to find each other based on aligned attribute pairs. This module can calculate the similarity of attributes with low literal similarity. After getting the structure similarity of the structure embedding module, the final entity alignment result of temporal KGs is produced by the calculation of a binary linear regression function. Experimental results demonstrate that our proposed model outperforms existing approaches significantly.

FAIR and Structured Data: A Domain Ontology Aligned with Standard‐Compliant Tensile Testing

The digitalization of materials science and engineering (MSE) is currently leading to remarkable advancements in materials research, design, and optimization, fueled by computer‐driven simulations, artificial intelligence, and machine learning. While these developments promise to accelerate materials innovation, challenges in quality assurance, data interoperability, and data management have to be addressed. In response, the adoption of semantic web technologies has emerged as a powerful solution in MSE. Ontologies provide structured and machine‐actionable knowledge representations that enable data integration, harmonization, and improved research collaboration. This study focuses on the tensile test ontology (TTO), which semantically represents the mechanical tensile test method and is developed within the project Plattform MaterialDigital (PMD) in connection with the PMD Core Ontology. Based on ISO 6892‐1, the test standard‐compliant TTO offers a structured vocabulary for tensile test data, ensuring data interoperability, transparency, and reproducibility. By categorizing measurement data and metadata, it facilitates comprehensive data analysis, interpretation, and systematic search in databases. The path from developing an ontology in accordance with an associated test standard, converting selected tensile test data into the interoperable resource description framework format, up to connecting the ontology and data is presented. Such a semantic connection using a data mapping procedure leads to an enhanced ability of querying. The TTO provides a valuable resource for materials researchers and engineers, promoting data and metadata standardization and sharing. Its usage ensures the generation of finable, accessible, interoperable, and reusable data while maintaining both human and machine actionability.