Resource Description Framework Research Articles

A Semantics-preserving Approach for Extracting RDF Knowledge from Object-oriented Databases

The Resource Description Framework and RDF Schema recommended by the World Wide Web Consortium provide a flexible model for semantically representing information about resources on the Web, which are playing an increasingly important role in intelligent processing of large-scale data. With the widespread acceptance and applications of RDF(S), construction of RDF(S) is of increasing importance. Automatic construction of RDF(S) with diverse data has attracted more attention. In this paper, we propose a novel approach for constructing an RDF(S) with object-oriented databases that are suitable for non-traditional applications. We propose the formal rules of mapping an object-oriented database model into a RDF(S) model based on the formal definitions of these two models. We develop a tool named OODB2RDF to verify our approach.

Optimizing Keyword Search Over Federated RDF Systems

The issue of keyword search in federated resource description framework(RDF) systems is revisited in this paper. Numerous independent SPARQL endpoints that solely offer SPARQL query interfaces make up a federated RDF system. Others find it challenging to remotely build up the global inverted indices over the full RDF network by downloading the dataset at various SPARQL endpoints. This poses a challenge for existing keyword search approaches. In this paper, we propose a keyword search approach for federated RDF systems without building up global inverted indices on the entire RDF graph. We build an offline schema graph for the federated RDF system. During query evaluation, the full-text search interfaces provided by existing SPARQL endpoints were used to map keywords to vertices in the schema graph. Subsequently, the schema graph was explored to construct SPARQL queries from the keywords. The constructed queries were evaluated over the underlying federated RDF systems. Some cost models were proposed to measure keyword mapping and query construction. On the other hand, to further improve the efficiency, a multiple query optimization strategy was designed to rewrite the queries and share the common computation during evaluation. Extensive experiments on real RDF datasets show that the proposed techniques are effective.

Database Security in RDF Terms

Abstract Databases are perhaps the most attacked and vulnerable elements in the online environment as far as organizations are concerned. The databases contain the most important information of an organization and its confidential data, along with those of its customers. However, little of the effort dedicated to security is intended to protect data. The present paper draws attention to the need to implement a simple and then complex security in a database using RDF (Resource Description Framework) based descriptions. Our example is one implemented for Microsoft Access and covers different security level descriptions considering attack types. We choose to represent the basic concepts and abstract syntax using the RDF data modeling vocabulary and we’re using as a starting point a legacy relational database where the company already catalogued attacks.

Query expansion based on modified Concept2vec model using resource description framework knowledge graphs

The enormous size of the web and the vagueness of the terms used to formulate queries still pose a huge problem in achieving user satisfaction. To solve this problem, queries need to be disambiguated based on their context. One well-known technique for enhancing the effectiveness of information retrieval (IR) is query expansion (QE). It reformulates the initial query by adding similar terms that help in retrieving more relevant results. In this paper, we propose a new QE semantic approach based on the modified Concept2vec model using linked data. The novelty of our work is the use of query-dependent linked data from DBpedia as training data for the Concept2vec skip-gram model. We considered only the top feedback documents, and we did not use them directly to generate embeddings; we used their interlinked data instead. Also, we used the linked data attributes that have a long value, e.g., “dbo: abstract”, as training data for neural network models, and, we extracted from them the valuable concepts for QE. Our experiments on the Associated Press collection dataset showed that retrieval effectiveness can be much improved when a skip-gram model is used along with a DBpedia feature. Also, we demonstrated significant improvements compared to other approaches.

Semantic web-based diagnosis and treatment of vector-borne diseases using SWRL rules

Vector-borne diseases (VBDs) are a kind of infection caused through the transmission of vectors generated by the bites of infected parasites, bacteria, and viruses, such as ticks, mosquitoes, triatomine bugs, blackflies, and sandflies. If these diseases are not properly treated within a reasonable time frame, the mortality rate may rise. In this work, we propose a set of ontologies that will help in the diagnosis and treatment of vector-borne diseases. For developing VBD’s ontology, electronic health records taken from the Indian Health Records website, text data generated from Indian government medical mobile applications, and doctors’ prescribed handwritten notes of patients are used as input. This data is then converted into correct text using Optical Character Recognition (OCR) and a spelling checker after pre-processing. Natural Language Processing (NLP) is applied for entity extraction from text data for making Resource Description Framework (RDF) medical data with the help of the Patient Clinical Data (PCD) ontology. Afterwards, Basic Formal Ontology (BFO), National Vector Borne Disease Control Program (NVBDCP) guidelines, and RDF medical data are used to develop ontologies for VBDs, and Semantic Web Rule Language (SWRL) rules are applied for diagnosis and treatment. The developed ontology helps in the construction of decision support systems (DSS) for the NVBDCP to control these diseases.

Automated approach for quality assessment of RDF resources

IntroductionThe Semantic Web community provides a common Resource Description Framework (RDF) that allows representation of resources such that they can be linked. To maximize the potential of linked data - machine-actionable interlinked resources on the Web - a certain level of quality of RDF resources should be established, particularly in the biomedical domain in which concepts are complex and high-quality biomedical ontologies are in high demand. However, it is unclear which quality metrics for RDF resources exist that can be automated, which is required given the multitude of RDF resources. Therefore, we aim to determine these metrics and demonstrate an automated approach to assess such metrics of RDF resources.MethodsAn initial set of metrics are identified through literature, standards, and existing tooling. Of these, metrics are selected that fulfil these criteria: (1) objective; (2) automatable; and (3) foundational. Selected metrics are represented in RDF and semantically aligned to existing standards. These metrics are then implemented in an open-source tool. To demonstrate the tool, eight commonly used RDF resources were assessed, including data models in the healthcare domain (HL7 RIM, HL7 FHIR, CDISC CDASH), ontologies (DCT, SIO, FOAF, ORDO), and a metadata profile (GRDDL).ResultsSix objective metrics are identified in 3 categories: Resolvability (1), Parsability (1), and Consistency (4), and represented in RDF. The tool demonstrates that these metrics can be automated, and application in the healthcare domain shows non-resolvable URIs (ranging from 0.3% to 97%) among all eight resources and undefined URIs in HL7 RIM, and FHIR. In the tested resources no errors were found for parsability and the other three consistency metrics for correct usage of classes and properties.ConclusionWe extracted six objective and automatable metrics from literature, as the foundational quality requirements of RDF resources to maximize the potential of linked data. Automated tooling to assess resources has shown to be effective to identify quality issues that must be avoided. This approach can be expanded to incorporate more automatable metrics so as to reflect additional quality dimensions with the assessment tool implementing more metrics.

CiTIzen-centric DAta pLatform (TIDAL): Sharing distributed personal data in a privacy-preserving manner for health research

Developing personal data sharing tools and standards in conformity with data protection regulations is essential to empower citizens to control and share their health data with authorized parties for any purpose they approve. This can be, among others, for primary use in healthcare, or secondary use for research to improve human health and well-being. Ensuring that citizens are able to make fine-grained decisions about how their personal health data can be used and shared will significantly encourage citizens to participate in more health-related research. In this paper, we propose a ciTIzen-centric DatA pLatform (TIDAL) to give individuals ownership of their own data, and connect them with researchers to donate the use of their personal data for research while being in control of the entire data life cycle, including data access, storage and analysis. We recognize that most existing technologies focus on one particular aspect such as personal data storage, or suffer from executing data analysis over a large number of participants, or face challenges of low data quality and insufficient data interoperability. To address these challenges, the TIDAL platform integrates a set of components for requesting subsets of RDF (Resource Description Framework) data stored in personal data vaults based on SOcial LInked Data (Solid) technology and analyzing them in a privacy-preserving manner. We demonstrate the feasibility and efficiency of the TIDAL platform by conducting a set of simulation experiments using three different pod providers (Inrupt, Solidcommunity, Self-hosted Server). On each pod provider, we evaluated the performance of TIDAL by querying and analyzing personal health data with varying scales of participants and configurations. The reasonable total time consumption and a linear correlation between the number of pods and variables on all pod providers show the feasibility and potential to implement and use the TIDAL platform in practice. TIDAL facilitates individuals to access their personal data in a fine-grained manner and to make their own decision on their data. Researchers are able to reach out to individuals and send them digital consent directly for using personal data for health-related research. TIDAL can play an important role to connect citizens, researchers, and data organizations to increase the trust placed by citizens in the processing of personal data.

Automated code compliance checking research based on BIM and knowledge graph

Automated code compliance checking plays an important role in moving the construction industry forward. While traditional drawing review relies on the identification of industry experts, the purpose of this study is to realize automatic code review by using BIM technology and knowledge graph technology. The method is based on knowledge graph to transform the specification provisions involved in the review of drawings into a computer-recognizable structured language using natural language processing technology to form a human–machine-readable knowledge graph pattern. For the review of BIM models, the BIM model information is exported and the global resource description framework of the building model is obtained in protégé, and the review report is finally obtained after mapping rules and review rules. Finally, the feasibility of the method is verified by an example. This study can effectively solve the problems of “manual dependency” and “inefficiency” in the process of review.

Data Mining and Analysis Algorithm of Smart City Network Information Resource Description Framework Based on Fuzzy Association Rules

Abstract Aiming at the problems of poor effect, low precision, and a long time frame in the current data mining analysis algorithm of the network information resource description framework (RDF), a data mining analysis algorithm of the network information RDF based on fuzzy association rules is proposed. Using association rule mining technology combined with fuzzy set theory, a fuzzy association rule algorithm is obtained to deal with quantitative data. Fuzzy c-means (FCM) is used to discretize continuous attributes, and the fuzzy concept pattern and its support and credibility are defined. Add a Mining Query flag to extend the SPARQL Protocol and RDF Query Language (SPARQL) syntax, define the user specified mining model, generate the corresponding project set and transaction, adopt the fuzzy association rule algorithm to generate fuzzy association rules, and realize the RDF data mining analysis of network information. The experimental results show that the network RDF data mining analysis effect of the proposed algorithm is good, which can effectively improve the network RDF data mining analysis accuracy and shorten the mining analysis time.

Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.

Modelling with AAS and RDF in Industry 4.0

Industry 4.0 has proposed the Asset Administration Shell (AAS) model for digital twins. This model should help to solve interoperability issues, a topic that is also addressed by the Semantic Web and its Resource Description Framework (RDF). AAS and RDF-based models have their own strengths. AAS models are easier to integrate with operational technologies in a production environment, whereas RDF-based models offer more semantic expressiveness and advanced querying. In the Horizon MAS4AI project we found that both modelling paradigms can complement each other to develop agentbased digital twins for modular production environments. In this work we propose two different approaches to bridge both modelling paradigms. First we define a set of mapping rules to generate an AAS model from a given RDF-based model, supporting model development. Secondly, we propose to use RDF-based models to generate a digital shadow of AASs to improve semantic discoverability. Preliminary results demonstrate that heterogeneity of metamodels does not exclude achieving semantic interoperability, as well as that greater functionality can be obtained compared to using both models in isolation. The solutions will be further developed in collaboration with pilot lines in the MAS4AI project.

A Cost-Driven Top-K Queries Optimization Approach on Federated RDF Systems

RDF (Resource Description Framework) is a model widely used to construct knowledge bases, while SPARQL (SPARQL Protocol and RDF Query Language) is the standardized structured query language to manipulate RDF data. Recently, many data providers have published their RDF datasets in their own autonomous sites and provided SPARQL query interfaces, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RDF sources</i> . In order to integrate multiple RDF sources, researchers put forward the federated RDF system to support the federated SPARQL queries. However, existing studies can only support efficient basic queries but not top-k queries. Toward this end, we propose a cost-driven top-k queries optimization approach in federated RDF systems, which can support both top-k queries for single variable ordering and expression ordering. Firstly, we propose an optimized query decomposition method to decompose the federated query into multiple subqueries. Secondly, while considering the top-k operator, we propose a cost model to evaluate the query cost and join cost of subqueries. The optimal query plan can be obtained by the costed-based query plan generation algorithm. Finally, combined with the characteristics of top-k queries, an incremental query plan execution strategy is developed to minimize the total query cost. Experimental results show that the proposed method is effective, efficient and scalable.

Multiple context-free path querying by matrix multiplication

Many graph analysis problems can be formulated as formal language-constrained path querying problems where the formal languages are used as constraints for navigational path queries. Recently, the context-free language (CFL) reachability formulation has become very popular and can be used in many areas, for example, querying graph databases, Resource Description Framework (RDF) analysis. However, the generative capacity of context-free grammars (CFGs) is too weak to generate some complex queries, for example, from natural languages, and the various extensions of CFGs have been proposed. Multiple context-free grammar (MCFG) is one of such extensions of CFGs. Despite the fact that, to the best of our knowledge, there is no algorithm for MCFL-reachability, this problem is known to be decidable. This paper is devoted to developing the first such algorithm for the MCFL-reachability problem. The essence of the proposed algorithm is to use a set of Boolean matrices and operations on them to find paths in a graph that satisfy the given constraints. The main operation here is Boolean matrix multiplication. As a result, the algorithm returns a set of matrices containing all information needed to solve the MCFL-reachability problem. The presented algorithm is implemented in Python using GraphBLAS API. An analysis of real RDF data and synthetic graphs for some MCFLs is performed. The study showed that using a sparse format for matrix storage and parallel computing for graphs with tens of thousands of edges the analysis time can be 10–20 minutes. The result of the analysis provides tens of millions of reachable vertex pairs. The proposed algorithm can be applied in problems of static code analysis, bioinformatics, network analysis, as well as in graph databases when a path query cannot be expressed using context-free grammars. The provided algorithm is linear algebra-based, hence, it allows one to use high-performance libraries and utilize modern parallel hardware.

CIRO: COVID-19 infection risk ontology.

Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not employed in countries with high patient volumes. Meanwhile, the Japanese government conducted this operation, thereby contributing to the control of infections, at the cost of arduous manual labor by public health officials. To ease the burden of the officials, this study attempted to automate the assessment of each person's infection risk through an ontology, called COVID-19 Infection Risk Ontology (CIRO). This ontology expresses infection risks of COVID-19 formulated by the Japanese government, toward automated assessment of infection risks of individuals, using Resource Description Framework (RDF) and SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we demonstrated that the knowledge graph built could infer the risks, formulated by the government. Moreover, we conducted reasoning experiments to analyze the computational efficiency. The experiments demonstrated usefulness of the knowledge processing, and identified issues left for deployment.

Sidr tree disease diagnosis system Python programming

The Sidr tree is an important fruit tree in many parts of the world, but it is often affected by various diseases and pests that can impact the quality and quantity of its fruit production. This paper proposes the use of ontology and semantic web technologies with Python programing language to program a diagnosis service for the Sidr tree diseases. Programming a Semantic Web application system and services requires knowledge of many technologies such as Resource Description Framework (RDF), Web Ontology Language (OWL), and SPARQL Query. The underline Web service (Sidr tree disease Web service), has to be programmed in connection to a pre-existing ontology and knowledge base. The service program is made of three main components. These components are the user interface, diagnosis service-related functions, Knowledge base, and query engine. The Python 3.10 code listing in this paper represents only the code related to disease diagnosing service and the Sidr tree ontology (SidrTreeonto). In addition to the listed statements and functions, the complete diagnosing service code contains some other unlisted HTML static and dynamic Web pages. The code is run and tested and found to be simple and easy to use even by unexpert users. Overall, this application will provide a useful tool for Sidr tree farmers and researchers to diagnose and manage diseases affecting their trees.

Automated Generation of Human-readable Natural Arabic Text from RDF Data

With the advances in Natural Language Processing (NLP), the industry has been moving towards human-directed artificial intelligence (AI) solutions. Recently, chatbots and automated news generation have captured a lot of attention. The goal is to automatically generate readable text from tabular data or web data commonly represented in Resource Description Framework (RDF) format. The problem can then be formulated as Data-to-text (D2T) generation from structured non-linguistic data into human-readable natural language. Despite the significant work done for the English language, no efforts are being directed towards low-resource languages like the Arabic language. This work promotes the development of the first RDF data-to-text (D2T) generation system for the Arabic language while trying to address the low-resource limitation. We develop several models for the Arabic D2T task using transfer learning from large language models (LLM) such as AraBERT, AraGPT2, and mT5. These models include a baseline Bi-LSTM Sequence-to-Sequence (Seq2Seq) model, as well as encoder-decoder transformers like BERT2BERT, BERT2GPT, and T5. We then provide a detailed comparative study highlighting the strengths and limitations of these methods setting the stage for further advancement in the field. We also introduce a new Arabic dataset (AraWebNLG) that can be used for new model development in the field. To ensure a comprehensive evaluation, general-purpose automated metrics (BLEU and Perplexity scores) are used as well as task-specific human evaluation metrics related to the accuracy of the content selection and fluency of the generated text. The results highlight the importance of pre-training on a large corpus of Arabic data and show that transfer learning from AraBERT gives the best performance. Text-to-text pre-training using mT5 achieves second best performance results even with multilingual weights.

Transforming 3D Models to Semantic Web Representation

"The purpose of the present paper is to research a rule-based approach for transforming X3D (eXtensible 3D) models to RDF (Resource Description Framework). The transformation is performed by using the RDF Mapping Language (RML). Its advantages are summarized, which are mainly due to the fact that the rules created build a knowledge base. By applying SPARQL (SPARQL Protocol and RDF Query Language) queries to it, the possibility of explore in order to validate and improve the defined RML rules themselves, is pointed out. An approach for reversing from the RDF triples to the original X3D in a unique way is considered, and the types of SPARQL queries needed for its implementation are systematized. Rules are formulated for all elements defined in the X3D schema, their attributes and properties are described. Their accessibility is ensured. The conversion of X3D models to RDF is confirmed to be consistent with guidelines and best practices for creating accessible, understandable, and reusable ontologies on the Web. The systematized SPARQL query types for reversing from RDF triples to the original X3D are checked for specific elements and sample data, and the obtained results establish their correctness. The prerequisites and limitations of the represented approach are explained. The proposed approach allows building a comprehensive knowledge base that includes the RML rules, the transformed X3D models and the domain-specific ontology and its use to analyzing data and semantic reasoning. The electronic libraries that include 3D content could take advantage from the benefits and possible future applications of the solutions discussed in this study."

Two-stage Detection of Semantic Redundancies in RDF Data

With the enrichment of the RDF (resource description framework), integrating diverse data sources may result in RDF data duplication. Failure to effectively detect the duplicates brings redundancies into the integrated RDF datasets. This not only increases unnecessarily the size of the datasets, but also reduces the dataset quality. Traditionally a similarity calculation is applied to detect if a pair of candidates contains duplicates. For massive RDF data, a simple similarity calculation may lead to extremely low efficiency. To detect duplicates in the massive RDF data, in this paper we propose a detection approach based on RDF data clustering and similarity measurements. We first propose a clustering method based on locality sensitive hashing (LSH), which can efficiently select candidate pairs in RDF data. Then, a similarity calculation is performed on the selected candidate pairs. We finally obtain the duplicate candidates. We show through experiments that our approach can quickly extract the duplicate candidates in RDF datasets. Our approach had the highest F score and time performance in the OAEI (Ontology Alignment Evaluation Initiative) 2019 competition.

Knowledge Base Reuse With Frame Representation In Artificial Intelligence Applications

The creation and upkeep of knowledge bases have emerged as the primary issue in artificial intelligence development. The knowledge domain is expanding, and application interoperability is urgently demanded. To accommodate this modification, the knowledge base, which serves as the foundation of the artificial intelligence architecture, must be updated. XML (extensible markup language), RDF (resources description framework), DAML (DARPA agent markup language), OIL (ontology inference layer), and OKBC are just a few of the standards that have been introduced as a result of the development of the internet (Open Knowledge-Base Connectivity). One type of knowledge representation that can store information and facts about a subject is frame-based representation. The OKBC, which can help with knowledge base interoperability, supports this approach. In this work, the main issues covered are the comprehension and fundamentals of knowledge base creation with frame representation. To better understand how frame representation functions as a reusable component, a case study from a news publication were used as an example.

FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network

The Swiss Personalized Health Network (SPHN) is a government-funded initiative developing federated infrastructures for a responsible and efficient secondary use of health data for research purposes in compliance with the FAIR principles (Findable, Accessible, Interoperable and Reusable). We built a common standard infrastructure with a fit-for-purpose strategy to bring together health-related data and ease the work of both data providers to supply data in a standard manner and researchers by enhancing the quality of the collected data. As a result, the SPHN Resource Description Framework (RDF) schema was implemented together with a data ecosystem that encompasses data integration, validation tools, analysis helpers, training and documentation for representing health metadata and data in a consistent manner and reaching nationwide data interoperability goals. Data providers can now efficiently deliver several types of health data in a standardised and interoperable way while a high degree of flexibility is granted for the various demands of individual research projects. Researchers in Switzerland have access to FAIR health data for further use in RDF triplestores.