Resource Description Framework Format Research Articles

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.

Generating Spatial Knowledge Graphs with 2D Indoor Floorplan Data: A Case Study on the Jeonju Express Bus Terminal

With the boom in online information, knowledge graphs like Freebase, Wikidata, and YAGO have emerged, thanks to the introduction of the RDF (Resource Description Framework). As RDF data grew, more and more spatial data was incorporated into it. While we have a lot of 2D data for outdoor spaces, mapping indoor spaces in 3D is challenging because it is expensive and time-consuming. In our research, we turned 2D blueprints into detailed 3D maps and then translated this into RDF format. We used the Jeonju Express Bus Terminal in South Korea as our test case. We made an automated tool that can turn 2D spatial data into 3D data that fits the IndoorGML standard. We also introduced terms like ‘loc’, ‘indoorgml-lite’, and ‘bloc’ to describe indoor spaces in the RDF format. Once we put our data into a GraphDB database, we could easily search for specific details and routes inside buildings. This work fills a significant gap in knowledge graphs concerning indoor spaces. However, the production of large-scale data across varied areas remains a challenge, pointing towards future research directions for more comprehensive indoor spatial information systems.

Infer the missing facts of D3FEND using knowledge graph representation learning

PurposeMITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.Design/methodology/approachD3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.FindingsExperimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.Research limitations/implicationsDespite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.Practical implicationsLink prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.Originality/valueThe representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.

Exploring Historical Australian Expeditions with Time-Layered Cultural Maps

The Australian Time Layered Cultural Map platform was created to help digital humanities scholars investigate how online geospatial tools could provide exemplars to their humanities colleagues on how historical collections and cultural data could be extended and re-examined with geospatial tools. The project discussed here investigated how Recogito/TMT could effectively extract spatial and temporal data from pure text-based historical information and generate time-layered interactive maps of that spatio-temporal data using accessible and user-friendly software. The target audience was humanities scholars relatively new to geospatial technologies and relevant programming systems. The interactive maps were created with two free, open-source web applications and one commercial GIS (Geographic Information System) mapping application. The relative pros and cons of each application are discussed. This paper also investigates simple workflows for extracting spatiotemporal data into RDF (Resource Description Framework) format to be used as Linked Open Data.

Aplicação de inferências em uma modelagem ontológica

Abstract Data modeling supported by ontologies favors the organization of knowledge and the use of inference machines to obtain new knowledge about data and support expert decision-making. However, it is necessary to discuss the techniques to promote inferences and explore the semantics between the data. The research problem is to verify how the use of semantic technologies would represent an advantageous alternative, in relation to the state of the art, to analyze, systematize and organize the information of a domain, accelerating a decision-making process. It presents the concepts behind the development of an artifact that employs semantic technologies and a domain ontology to integrate data from Building Information Modeling projects and other tabular data external to the project to automate Leadership in Energy certification tasks and Environmental Design. To demonstrate the artifact, data is semantically annotated and a knowledge graph is generated in Resource Description Framework format. Once integrated into the graph, inferences are applied to evaluate the certification criteria through queries. The results suggest that the application of these technologies promotes semantic extension, facilitates integration with other knowledge bases, and conceptually organizes data to better retrieve information and generate new knowledge.

Using digital humanity approaches to visualize and evaluate the cultural heritage ontology

PurposeThis study applies digital humanity tools (Gephi and Protégé) for establishing and visualizing ontologies in the cultural heritage domain. According to that, this study aims to develop a novel evaluation approach using five ontology indicators (data overview, visual presentation, highlight links, scalability and querying) to evaluate the knowledge structure presentation of cultural heritage ontology.Design/methodology/approachThe researchers collected and organized 824 pieces of government’s open data (GOD), converted GOD into the resource description framework format, applied Protégé and Gephi to establish and visualize cultural heritage ontology. After ontology is built, this study recruited 60 ontology participants (30 from information and communications technology background; 30 from cultural heritage background) to operate this ontology and gather their different perspectives of visual ontology.FindingsBased on the ontology participant’s feedback, this study discovered that Gephi is more supporting than Protégé when visualizing ontology. Especially in data overview, visual presentation and highlight links dimensions, which is supported visualization and demonstrated ontology class hierarchy and property relation, facilitated the wider application of ontology.Originality/valueThis study offers two contributions. First, the researchers analyzed data on East Asian architecture with novel digital humanities tools to visualize ontology for cultural heritage. Second, the study collected participant’s feedback regarding the visualized ontology to enhance its design, which can serve as a reference for future ontological development.

HAMAP as SPARQL rules-A portable annotation pipeline for genomes and proteomes.

BackgroundGenome and proteome annotation pipelines are generally custom built and not easily reusable by other groups. This leads to duplication of effort, increased costs, and suboptimal annotation quality. One way to address these issues is to encourage the adoption of annotation standards and technological solutions that enable the sharing of biological knowledge and tools for genome and proteome annotation.ResultsHere we demonstrate one approach to generate portable genome and proteome annotation pipelines that users can run without recourse to custom software. This proof of concept uses our own rule-based annotation pipeline HAMAP, which provides functional annotation for protein sequences to the same depth and quality as UniProtKB/Swiss-Prot, and the World Wide Web Consortium (W3C) standards Resource Description Framework (RDF) and SPARQL (a recursive acronym for the SPARQL Protocol and RDF Query Language). We translate complex HAMAP rules into the W3C standard SPARQL 1.1 syntax, and then apply them to protein sequences in RDF format using freely available SPARQL engines. This approach supports the generation of annotation that is identical to that generated by our own in-house pipeline, using standard, off-the-shelf solutions, and is applicable to any genome or proteome annotation pipeline.ConclusionsHAMAP SPARQL rules are freely available for download from the HAMAP FTP site, ftp://ftp.expasy.org/databases/hamap/sparql/, under the CC-BY-ND 4.0 license. The annotations generated by the rules are under the CC-BY 4.0 license. A tutorial and supplementary code to use HAMAP as SPARQL are available on GitHub at https://github.com/sib-swiss/HAMAP-SPARQL, and general documentation about HAMAP can be found on the HAMAP website at https://hamap.expasy.org.

Fog‐based semantic model for supporting interoperability in IoT

Cloud computing has several favourable characteristics for supporting Internet of things (IoT) applications and services. While provisioning support for heterogeneous devices adapting different semantics in IoT, cloud-based technology is highly used. In semantic model, attaching additional information to raw sensory data is accomplished with the help of ontology. However, the longer distance between the cloud and IoT devices becomes a bottleneck for critical IoT applications. In this study, the authors proposed a fog-based semantic model called semantic-fog to reduce this gap. The proposed model shifts some of the frequently used semantic services of the cloud to the edge of the sensor network. Additionally, it provides an efficient off-loading technique among fog-fog and fog-cloud devices to reduce task execution time and energy consumption of the fog nodes. Furthermore, an effective mapping method for converting raw sensory data into Resource Description Framework format is also presented in this work. A comparative analysis among the relevant cloud-based computing models and the proposed semantic-fog model is performed in terms of service delay, energy consumption, network usage, and the total cost of the system. Simulation results show that semantic-fog outperforms the cloud-based models almost in all the aspects considered.

An E-Commerce Platform for Industrialized Construction Procurement Based on BIM and Linked Data

Industrialized construction has raised the requirements of procurement methods used in the construction industry. The rapid development of e-commerce offers efficient and effective solutions, however the large number of participants in the construction industry means that the data involved are complex, and problems arise related to volume, heterogeneity, and fragmentation. Thus, the sector lags behind others in the adoption of e-commerce. In particular, data integration has become a barrier preventing further development. Traditional e-commerce platform, which considered data integration for common product data, cannot meet the requirements of construction product data integration. This study aimed to build an information-integrated e-commerce platform for industrialized construction procurement (ICP) to overcome some of the shortcomings existing platforms. We proposed a platform based on Building Information Modelling (BIM) and linked data, taking an innovative approach to data integration. It uses industrialized construction technology to support product standardization, BIM to support procurement process, and linked data to connect different data sources. The platform was validated using a case study. With the development of an e-commerce ontology, industrialized construction component information was extracted from BIM models and converted to Resource Description Framework (RDF) format. Related information from different data sources was also converted to RDF format, and Simple Protocol and Resource Description Framework Query Language (SPARQL) queries were implemented. The platform provides a solution for the development of e-commerce platform in the construction industry.

Ontology-based framework for building environmental monitoring and compliance checking under BIM environment

Building environmental monitoring and compliance checking are important in ensuring environmental performance. The information required for monitoring and checking is obtained from different data sources in different information systems. In this context, information sharing between stakeholders and the semantic interoperability that prevails with varying information systems are necessary. However, the implementation of information sharing and semantic interoperability can be a challenge. This paper proposes an ontology-based framework to support environmental monitoring and compliance checking under building information modeling (BIM) environment among different information systems. The framework integrates building information from BIM, environmental information provided by sensors, and regulatory information based on building regulations and design requirements. In this framework, four specific ontologies are developed to represent relevant knowledge. Building information is extracted from BIM and then converted, together with environmental information provided by sensors, into resource description framework format as ontology instances. The regulation clauses are transformed into SPARQL (SPARQL Protocol and RDF Query Language) rules. A case study is performed to apply the framework, and environmental monitoring and automated compliance checking are implemented in the context of a real distributed energy station project. The testing results validate the feasibility and effectiveness of the proposed framework.

MAchine Readable Cataloging to MAchine Understandable Data with Distributed Big Data Management

ABSTRACTIn recent years, the library domain has been using semantic web technologies to enable the data-centric information that can be processed directly by machines. Attempts have been evolved for data transitioning from MAchine-Readable Cataloging (MARC) formats into the Resource Description Framework (RDF). Storing library data in RDF format enhances interlinking and reusing of the resources on the web. Moreover, the machine can interpret library resources meaningfully because of rich source of semantics. Existing approaches rely on the single-node environment but they fail when they meet the large volume of the input data. Some of the bibliographic records in MARC 21 formats are huge in size that traditional data-management tools become incapable during data processing and requires larger storage area. Such data need serious attention by the systems that can perform tasks in parallel. In this article, we propose a distributed approach to convert legacy library data into RDF format using Apache Spark and Hadoop. We describe the process of data conversion from MARC 21 formats for Bibliographic data into RDF and show preliminary reports on the processing speed and storage analysis. The performance of the conversion process is improved in terms of processing time and the storage size.

ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles

Motivation Scientists increasingly rely on intelligent information systems to help them in their daily tasks, in particular for managing research objects, like publications or datasets. The relatively young research field of Semantic Publishing has been addressing the question how scientific applications can be improved through semantically rich representations of research objects, in order to facilitate their discovery and re-use. To complement the efforts in this area, we propose an automatic workflow to construct semantic user profiles of scholars, so that scholarly applications, like digital libraries or data repositories, can better understand their users’ interests, tasks, and competences, by incorporating these user profiles in their design. To make the user profiles sharable across applications, we propose to build them based on standard semantic web technologies, in particular the Resource Description Framework (RDF) for representing user profiles and Linked Open Data (LOD) sources for representing competence topics. To avoid the cold start problem, we suggest to automatically populate these profiles by analyzing the publications (co-)authored by users, which we hypothesize reflect their research competences. Results We developed a novel approach, ScholarLens, which can automatically generate semantic user profiles for authors of scholarly literature. For modeling the competences of scholarly users and groups, we surveyed a number of existing linked open data vocabularies. In accordance with the LOD best practices, we propose an RDF Schema (RDFS) based model for competence records that reuses existing vocabularies where appropriate. To automate the creation of semantic user profiles, we developed a complete, automated workflow that can generate semantic user profiles by analyzing full-text research articles through various natural language processing (NLP) techniques. In our method, we start by processing a set of research articles for a given user. Competences are derived by text mining the articles, including syntactic, semantic, and LOD entity linking steps. We then populate a knowledge base in RDF format with user profiles containing the extracted competences.We implemented our approach as an open source library and evaluated our system through two user studies, resulting in mean average precision (MAP) of up to 95%. As part of the evaluation, we also analyze the impact of semantic zoning of research articles on the accuracy of the resulting profiles. Finally, we demonstrate how these semantic user profiles can be applied in a number of use cases, including article ranking for personalized search and finding scientists competent in a topic —e.g., to find reviewers for a paper. Availability All software and datasets presented in this paper are available under open source licenses in the supplements and documented at http://www.semanticsoftware.info/semantic-user-profiling-peerj-2016-supplements. Additionally, development releases of ScholarLens are available on our GitHub page: https://github.com/SemanticSoftwareLab/ScholarLens.

Model WordNet Bahasa Indonesia berbasis Linked Data

WordNet is an online lexical database. In Computer Science domain, it plays important role in solving semantic interoperability issues. It also helps in many researches related to Natural Language Processing topic. Because of the importance of WordNet, there are many works to develop WordNet into several languages, e.g., Japanese, Arabic, and Indonesian. However, those are still not sufficient to address semantic interoperability issues. Therefore, there are several attempts to form WordNet into machine understandable format, i.e, Resource Description Framework (RDF) model. Still, there is no effort to form WordNet Bahasa Indonesia into RDF format. This paper presents the process of forming WordNet Bahasa Indonesia into Linked Data form. This process involves several phases, which are identifying data sources, data extraction, data transformation, data loading into relational database, and mapping database model into RDF model. The latest is done by using D2RQ framework, resulting the WordNet Bahasa Indonesia as Linked Data format. This data set is linked to WordNet-RDF of Princetown University.

PROVIDING GEOGRAPHIC DATASETS AS LINKED DATA IN SDI

In this study, a prototype service to provide data from Web Feature Service (WFS) as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI) are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF) data format. Next, a Web Ontology Language (OWL) ontology is created to describe the dataset information content using the Open Geospatial Consortium’s (OGC) GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML) format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs. <br><br> A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID). The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.

PROVIDING GEOGRAPHIC DATASETS AS LINKED DATA IN SDI

Abstract. In this study, a prototype service to provide data from Web Feature Service (WFS) as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI) are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF) data format. Next, a Web Ontology Language (OWL) ontology is created to describe the dataset information content using the Open Geospatial Consortium’s (OGC) GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML) format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs. A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID). The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.

Nanotechnology Ontology: Semantic Access to Information in the Nano World

Scientific progress in a domain depends greatly on making use of results of previous research activities. Like human beings, it is difficult for computers to encode and retrieve the unsupervised Web information. The basic answer to this problem is to describe the information explicitly and semantically with ontology. The article presents a brief discussion of the study undertaken to design an ontology for Nanotechnology field.Scientific progress in a domain depends greatly on making use of results of previous research activities. Like human beings, it is difficult for computers to encode and retrieve the expanding web information. The basic answer to this problem is to describe the information explicitly and semantically with ontology. The article presents a brief discussion of the study undertaken to design an ontology for Nanotechnology, which was developed by using the Resource Description Framework (RDF). This paper further describes methodology opted to develop the Nanotechnology ontology, where a semantic structure of Nanotechnology thesaurus was utilised for its creation. The study has further presented the features of nMap software, which was developed specifically for the study by using VB.net as front-end tool and MS-access as back-end tool. Outcome of the study offers a semantic knowledge structure to Nanotechnology researchers in machine readable environment. Further, development of ontology in RDF format serves interoperability and will further support semantic interpretation by machines to facilitate human understanding. dx.doi.org/10.14429/djlit.36.1.9198

An application profile for research collaboration and information management

Purpose– The purpose of this paper is to design an application profile that will enable interoperability among research management systems, support research collaboration, and facilitate the management of research information.Design/methodology/approach– The approach is based on the Singapore Framework for Dublin Core Application Profile, a framework for designing metadata schemas for maximum interoperability. The application profile is built from gathering stakeholders’ requirements in research community and integrates four types of research information, i.e., information on researchers, research projects, research outputs, and research reports, which benefits researchers, research managers, and funding agencies.Findings– The resultant application profile is evaluated against widely used similar metadata schemas and requirements; and is found to be more comprehensive than the existing schemas and meets the collected requirements. Furthermore, the application profile is deployed with a prototype of research management system and is found works appropriately.Practical implications– The designed application profile has implications for further development of research management systems that would lead to the enhancement of research collaboration and the efficiency of research information management.Originality/value– The proposed application profile covers information entire the research development lifecycle. Both schema and information can be represented in Resource Description Framework format for reusing purpose and linking with other information. This enables users to share research information, co-operate with others, funding agencies and the community at large, thereby allowing a research management system to increase collaboration and the efficiency of research management. Furthermore, researchers and research information can be linked by means of Linked Open Data technology.

Feature-driven linguistic-based entity matching in linked data with application in pharmacy

The web becomes an overwhelmingly huge repository of data. At the same time, users demand access to the information on the web in a more natural way. In other words, users require interaction with the web using natural linguistic terms and expect human comprehensive answers. The introduction of Resource Description Framework (RDF) is a promising step towards significant changes how systems can utilize the web. The very nature of RDF format that ensures high interconnectivity of pieces of data creates an opportunity to process and analyse data in a different way. In this paper, we address the problem of processing web information using fuzzy-based technologies. In particular, we adopt a linguistic representation model to determining alternatives that match a given reference with the highest possible degree and satisfying some specific criteria. The process of comparing alternatives to the reference is feature-driven while an entity is described by its features. The proposed methodology is able to deal with features of different nature and utilize comparison mechanisms suitable for each type of features. The utilization of 2-tuple allows for comparing and aggregating linguistic-based descriptions of features, especially when the reference does not specify values of features explicitly. In experiments, we show the utilization of our approach in the domain of pharmacy. The obtained results show the advantage of using the feature-based comparison process and linguistic aggregation procedure over results obtained using the RDF query language SPARQL (SPARQL Protocol and RDF Query Language).

Natural Language Generation from Graphs

The Resource Description Framework (RDF) is the primary language to describe information on the Semantic Web. The deployment of semantic web search from Google and Microsoft, the Linked Open Data Community project along with the announcement of schema.org by Yahoo, Bing and Google have significantly fostered the generation of data available in RDF format. Yet the RDF is a computer representation of data and thus is hard for the non-expert user to understand. We propose a Natural Language Generation (NLG) engine to generate English text from a small RDF graph. The Natural Language Generation from Graphs (NLGG) system uses an ontology skeleton, which contains hierarchies of concepts, relationships and attributes, along with handcrafted template information as the knowledge base. We performed two experiments to evaluate NLGG. First, NLGG is tested with RDF graphs extracted from four ontologies in different domains. A Simple Verbalizer is used to compare the results. NLGG consistently outperforms the Simple Verbalizer in all the test cases. In the second experiment, we compare the effort spent to make NLGG and NaturalOWL work with the M-PIRO ontology. Results show that NLGG generates acceptable text with much smaller effort.

Methods for analyzing semantic data of electronic collections in mathematics

A survey of the key approaches to the semantic processing of mathematical texts is presented. A software platform prototype for the electronic storage of mathematical documents, which is based on the linked open-data (LOD) model and uses semantic information for data management, including formula-fragment searching, is proposed. The analysis of mathematical documents and the extraction of semantic information from the latter are carried out based on the electronic collection of the Izv. Vyssh. Uchebn. Zaved., Mat. (1995---2009) using special-purpose ontologies, metadata representation in the RDF (Resource Description Framework) format, and integration with existing LOD sets.