Resource Description Framework Research Articles

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.

Delving into Causal Discovery in Health-Related Quality of Life Questionnaires

Questionnaires on health-related quality of life (HRQoL) play a crucial role in managing patients by revealing insights into physical, psychological, lifestyle, and social factors affecting well-being. A methodological aspect that has not been adequately explored yet, and is of considerable potential, is causal discovery. This study explored causal discovery techniques within HRQoL, assessed various considerations for reliable estimation, and proposed means for interpreting outcomes. Five causal structure learning algorithms were employed to examine different aspects in structure estimation based on simulated data derived from HRQoL-related directed acyclic graphs. The performance of the algorithms was assessed based on various measures related to the differences between the true and estimated structures. Moreover, the Resource Description Framework was adopted to represent the responses to the HRQoL questionnaires and the detected cause–effect relationships among the questions, resulting in semantic knowledge graphs which are structured representations of interconnected information. It was found that the structure estimation was impacted negatively by the structure’s complexity and favorably by increasing the sample size. The performance of the algorithms over increasing sample size exhibited a similar pattern, with distinct differences being observed for small samples. This study illustrates the dynamics of causal discovery in HRQoL-related research, highlights aspects that should be addressed in estimation, and fosters the shareability and interoperability of the output based on globally established standards. Thus, it provides critical insights in this context, further promoting the critical role of HRQoL questionnaires in advancing patient-centered care and management.

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 (AI), and machine learning (ML). 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 (SWT) 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 was developed within the project Plattform MaterialDigital (PMD) in connection with the PMD Core Ontology (PMDco). 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 (RDF) 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 (FAIR) data while maintaining both human and machine actionability.This article is protected by copyright. All rights reserved.

Graph Theory Applications in Database Management

Graph theory, which is a branch of discrete mathematics, has emerged as a powerful tool in various domains, including database management. This abstract investigates the ways in which ideas and methods from graph theory which can be applied to database systems, offering a thorough synopsis of their benefits. Complex interactions within data can be well-modeled by using the basic concepts of graph theory, such as nodes, edges, and relationships. Because of its capacity to represent and query complex relationships, graph databases have become more and more popular in the field of database administration. Graph databases are well-suited for situations such as social networks, recommendation systems, and interconnected data domains because they are excellent at representing and traversing relationships, in contrast to standard relational databases, which are excellent at managing structured data. The abstract delves into the key graph-based data models, such as property graphs, RDF (Resource Description Framework), explaining how they facilitate the representation of diverse relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets. The document highlights specific use cases where graph theory contributes to database management, including fraud detection, social network analysis, and recommendation systems. Additionally, it discusses the challenges associated with integrating graph databases into existing infrastructures and proposes solutions to address scalability and performance concerns. The abstract also touches upon the advancements in graph database query languages (Cypher) and SPARQL, showcasing their expressive power in querying complex relationships. The inclusion of graph-based indexing and optimization techniques demonstrates how database systems can efficiently handle queries involving large-scale graph data. As graph databases continue to evolve, this abstract concludes by outlining potential future directions in the intersection of graph theory and database management. It emphasizes the importance of ongoing research in developing scalable and efficient solutions for managing interconnected data, ultimately paving the way for more sophisticated and context-aware database systems relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets.

Implementing Data Workflows and Data Model Extensions with RDF-star

Purpose The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications. Design/methodology/approach In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information. Findings The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model. Practical implications Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level. Originality/value With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

An Accounting Classification System Using Constituency Analysis and Semantic Web Technologies

ABSTRACT To aid accountants in making professional judgments and decisions regarding the accounting methods for transactions, we propose a classification system by integrating computational linguistics with semantic web technologies. We use constituency parsing to convert the classification rules in accounting standards into a machine-processable data structure: Resource Description Framework (RDF) triples. When an accounting classification question is input, the system converts it into an RDF triple, compares it with the established triples of different accounting methods, and subsequently identifies the most appropriate accounting method. We showcased and evaluated our proposed model using IFRS 9 and IAS 28. Our study provides both scholarly and practical applications by (1) incorporating computational linguistics and semantic web technologies to create an interpretable, process-traceable, and explainable classification system aligned with regulatory requirements; and (2) proving that the knowledge-based model can be established without substantial training data, enhancing its accessibility and utility for accounting professionals. Data Availability: Data are available from the authors upon request.

A survey for managing temporal data in RDF

The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web, which is organized into layers known as the Semantic Web Layer Cake. However, RDF’s basic construct is a binary relationship in the format of <subjectpredicateobject>. Representing higher-order relationships with RDF requires reification, which can be cumbersome. Time-varying data is prevalent, but cannot be adequately represented using only binary relationships. We conducted a detailed review of the literature on extending RDF with temporal data, comparing approaches for representation, querying, storage, implementation, and evaluation. In addition, we briefly reviewed approaches for extending RDF with spatial, probability, and other dimensions in conjunction with temporal data.

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.

Ontology learning from object-relational mapping metadata and relational database

Ontologies play an important role in representing the semantics of data sources. Building an ontology as a representation of domain knowledge from available data sources is not a simple process, particularly when dealing with relational data, which remains prevalent in existing knowledge systems. In this study, we create an ontology from a relational database using object-relational mapping (ORM) metadata as additional rules for mapping. Our method comprises two main phases: ontology schema construction using ORM metadata and the generation of ontology instances from the relational database. During the initial phase, we analyzed the ORM metadata to map it to an resource description framework schema (RDF(S))-OWL representation of the ontology. In the subsequent phase, we applied mapping rules to convert the relational database (RDB) data into ontological instances, which are then represented as RDF triples. Using ORM metadata, we enhance the accuracy of the resulting ontology, particularly in terms of extracting concepts and hierarchical relationships. This study contributes to the field of ontology learning by showcasing a novel approach that leverages ORM metadata to create ontologies from relational databases.

A bitemporal RDF index based on skip list

The Resource Description Framework (RDF) is a framework for expressing information about resources in the form of triples (subject, predicate, object). The information represented by the standard RDF is static, i.e., that does not change over time. To better deal with a large amount of time-related information, temporal RDF is proposed. Consequently, how to explore index technology to efficiently query temporal information has become an important research issue, but the research on the index of temporal RDF is still short, especially the index of bitemporal RDF. BitemporalRDF can represent more complicated situations (e.g., RDF triples with both validtime and transactiontime). Indexes for bitemporal RDF can further expand the application scenarios and functions of temporal RDF. In this paper, we propose an efficient index for bitemporal RDF queries. The index innovatively introduces and re-designs skip list structure into the bitemporal RDF query. We also investigate how to cover almost all query patterns with as few indexes as possible. In addition, although the proposed index is conceived for temporal RDF, it also takes into account the performance of standard RDF queries when the time element is unknown. Finally, we run experiments with synthetic data sets of different sizes using the Lehigh University Benchmark (LUBM), and results prove that the proposed index is scalable and effective.

Linked data to streaming data sensors sources for direct access

Goal: Industrial operations are complex, and data sensors assure safety and reliable information for production improvements. Multiple stakeholders can take advantage of data acquisition for post-analysis and process control. Providing users and systems with friendly access to operating data is fundamental to the digital transition in the industry 4.0 scenario. Linking data and systems over ontologies and Industry Foundation Classes will boost supply chain performance in many layers. This paper presents the concept of valid data points over Uniform Resource Identifiers for sensor time-series into triples stores via Application Programming Interfaces. Design/Methodology/Approach: A streaming data source approach to integrating industrial sensor data and sharing it via Uniform Resource Identifiers is developed and tested using Node-Red with multiple data connection types, such as the Industry Foundation Classes and open-source time series databases. Results: The detailed proof of concept presented valid the feasibility of sharing sensor data via Uniform Resource Identifiers. The findings provide a backbone of a system able to interop Message Queuing Telemetry Transport data, Resource Description Framework datasets and Industry Foundation Classes schema. Limitations of the investigation: The system envisaged was tested using simulated data. However, it is expected to have similar results from real data use. Nevertheless, more research will be needed to implement more features, such as three-dimensional object integration. Practical implications: The solution designed and tested presented can be used in practice for companies that desire to expand via linked data shareability and interoperability. Also, researchers can advance the solution for specific features, such as creating an open-source data query and manipulation language. Originality/Value: This paper examines future deployments of systems-to-systems interoperability targeting user-friendly data shareability. It is meant to be useful for industrial and academic developments.

Regulus infers signed regulatory relations from few samples' information using discretization and likelihood constraints.

Transcriptional regulation is performed by transcription factors (TF) binding to DNA in context-dependent regulatory regions and determines the activation or inhibition of gene expression. Current methods of transcriptional regulatory circuits inference, based on one or all of TF, regions and genes activity measurements require a large number of samples for ranking the candidate TF-gene regulation relations and rarely predict whether they are activations or inhibitions. We hypothesize that transcriptional regulatory circuits can be inferred from fewer samples by (1) fully integrating information on TF binding, gene expression and regulatory regions accessibility, (2) reducing data complexity and (3) using biology-based likelihood constraints to determine the global consistency between a candidate TF-gene relation and patterns of genes expressions and region activations, as well as qualify regulations as activations or inhibitions. We introduce Regulus, a method which computes TF-gene relations from gene expressions, regulatory region activities and TF binding sites data, together with the genomic locations of all entities. After aggregating gene expressions and region activities into patterns, data are integrated into a RDF (Resource Description Framework) endpoint. A dedicated SPARQL (SPARQL Protocol and RDF Query Language) query retrieves all potential relations between expressed TF and genes involving active regulatory regions. These TF-region-gene relations are then filtered using biological likelihood constraints allowing to qualify them as activation or inhibition. Regulus provides signed relations consistent with public databases and, when applied to biological data, identifies both known and potential new regulators. Regulus is devoted to context-specific transcriptional circuits inference in human settings where samples are scarce and cell populations are closely related, using discretization into patterns and likelihood reasoning to decipher the most robust regulatory relations.

Linguistic summarisation of multiple entities in RDF graphs

&lt;abstract&gt;&lt;p&gt;Methods for producing summaries from structured data have gained interest due to the huge volume of available data in the Web. Simultaneously, there have been advances in natural language generation from Resource Description Framework (RDF) data. However, no efforts have been made to generate natural language summaries for groups of multiple RDF entities. This paper describes the first algorithm for summarising the information of a set of RDF entities in the form of human-readable text. The paper also proposes an experimental design for the evaluation of the summaries in a human task context. Experiments were carried out comparing machine-made summaries and summaries written by humans, with and without the help of machine-made summaries. We develop criteria for evaluating the content and text quality of summaries of both types, as well as a function measuring the agreement between machine-made and human-written summaries. The experiments indicated that machine-made natural language summaries can substantially help humans in writing their own textual descriptions of entity sets within a limited time.&lt;/p&gt;&lt;/abstract&gt;

SMART-FCD: IOT DATA INTEROPERABILITY USING SENSOR BASED FUZZY LINKED RULES FOR CROSS DOMAIN APPLICATIONS

Internet of Things (IoT) is connected everywhere and enables massive information exchange between objects and people. The unified global IoT must manage a massive amount of data generated by these connected smart which arises the interoperability challenges such as lack of communication protocols, device support, and accepted open standards in smart environments. To combat these issues, a novel Smart-Fuzzy linked rules for Cross Domain application (Smart-FCD) framework is proposed to ensure interoperability by enabling efficient communication and data exchange between multiple platforms and systems. Initially, the heterogenous data from the sensors such as temperature sensors and humidity sensors, and the descriptions are implemented according to the Sensor Measurement List (SenML) language. After composing, semantic modelling will occur which converts the relational data into Resource Description Framework (RDF) format. The Linked Open Vocabulary for the Internet of Things (LOV4IoT) dataset is used to extract interoperable domain knowledge, which is then fed into the related sensor-based fuzzy rules. The IoT preset semantic models which utilise interoperable datasets and domain instances, which are updated via fuzzy association rules. Semantic Web of Things (SWoT) is a technology that assists in creating semantic-based IoT applications, thus IoT developers may use it to create intelligent applications. The sensor data, RDF simulation, evolution time and latency are some of the parameters that are used to evaluate the effectiveness of the proposed Smart-FCD methodology.

Using RDF Models to Create Knowledge Bases in the Kazakh Language: Comparison with Other Methods

Currently, there is a rapid development of information technologies, the amount of information on the Internet is growing very fast and it is becoming increasingly difficult to find the necessary information. A search using keywords does not give results adequate to the meaning of the information sought. Therefore, the creation of a technology for designing intelligent question answering systems in the Kazakh language based on the presentation, processing and extraction of knowledge is a very actual problem, since it is in such a system that the linguistic and semantic relationships between the texts of the request and the answer can be taken into account. This research paper focuses on the integration of the Resource Description Framework (RDF) model, a semantic web technology, and provides a detailed evaluation of data mining techniques in Kazakh. The paper examines many Kazakh language data collection methods such as online scraping, community collaboration and translation. It also explores the function of RDF models in organizing knowledge, connecting data points and adding semantic richness to datasets. The paper discusses linguistic features and challenges unique to the Kazakh language and emphasizes the need to address these challenges with domain-specific data. The need for thorough cleaning, annotation and data quality assurance is emphasized to guarantee the reliability and use of the collected datasets. Within global communications and technology, the study emphasizes the importance of languages other than English and examines how semantic web technologies can improve data representation and knowledge retrieval. The study lays the groundwork for future initiatives to address the shortage of datasets in languages with fewer resources and to create semantic web technologies for language diversity.

Cultural heritage on the Semantic Web: The Europeana Data Model

The Semantic Web allows data to be linked on the Web and structures information for use by humans and machines. Furthermore, it makes relationships between data explicit, enabling the creation of Linked Data. Based on a literature review, the principles and technologies underlying Linked Data are presented – namely, the Resource Description Framework and models developed for libraries, archives and museums. Europeana aggregates European institutions’ digital cultural heritage, having developed a model that follows linked-data principles. For a deeper understanding of this model, the Europeana Data Model is presented with examples of two representation approaches and the advantages of metadata enrichment in information discovery. The experience of the University of Coimbra with Europeana is briefly explained. Finally, the authors discuss the challenges that cultural heritage institutions face in adopting these models and freeing their information from the silos it is in, taking advantage of the potential that Linked Data provides.

Development of Semantic-Web Based Forest Resources Database

There are some forest resources (such as Trees, Shrubs, and Water bodies) in the university main campus. These resources are used for various purposes ranging from teaching, researches medicinal and domestic purposes and among many others. However, there is no record of official documentation on them. Thus, this study aims to provide a semantic-web based database system for efficient management of forest resources. We adopted the W3C semantic web framework in the design and development of this work. Specifically, we focused on the layers 1 to 5 of the framework. The layers include; Resource Description Framework (RDF), Web Ontology Language (OWL), Extensible Markup Language (XML), User Interface). The Uschold’s and Kings’ method was applied in creating the ontology. The implementation process was carried out by populating the database with the Taxonomies of the Trees on University of Energy and Natural Resources (U.E.N.R) campus by means of creating an Ontology File using protégé. Some of the basic information that we provided by the proposed semantic web database include trees, their scientific name, geographical coordinate, physical location, medicinal value and other important data. We performed alpha test by involving six senior forest science students who used the application, evaluated it and came out with a good result which is shown in the chapter four of this documentation.

Multicriteria requirement ranking based on uncertain knowledge representation and reasoning

The abundance of complex natural language descriptions related to requirements data presents major challenges for requirement analysis. Knowledge graphs (KGs), as the latest achievement in symbolic studies, are widely used in various fields due to their rich semantic expressive abilities. However, existing methods that mine resource description framework (RDF) triples in classical KGs cannot effectively capture the fuzzy semantic information in the product development system. To address this, we propose a multicriteria requirement ranking method based on uncertain knowledge representation and reasoning (KRR). First, we defined a model for representing uncertain knowledge to organize diverse data from multiple sources. This process was accomplished by constructing the requirement ontology, which is based on the function-behaviour-structure (FBS) model and requirements modelling-related documents. Next, a knowledge representation approach named the fuzzy requirement knowledge graph (FRKG) was devised by combining attribute confidence and predicate fuzziness. Then, knowledge reasoning rules were designed to enhance the edges in the FRKG, unveiling potential relationships between nodes. Utilizing the enriched FRKG (EFRKG), we proposed a multicriteria requirement ranking method based on grey relational analysis (GRA). To validate the effectiveness of the proposed approach, we conducted a case study involving unmanned aerial vehicles (UAVs). Furthermore, the semantic extension capability of FRKG was evaluated, and a comparison with traditional multicriteria requirement ranking methods was performed to demonstrate the efficiency of the proposed approach from both perspectives.

RDF(S) Store in Object-Relational Databases

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases

IntroductionHealthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement.MethodsSixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema.ResultsFor three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources.ConclusionWe assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.