RDF Data Research Articles

A SHACL-Based Approach for Enhancing Automated Compliance Checking with RDF Data

Automated Compliance Checking (ACC) has emerged as a critical tool for enforcing legal regulations across various domains. This paper contributes to ongoing research in Semantic Web technologies, particularly focusing on the execution of SHACL-SPARQL rules on RDF data. The RDF, being one of the most widely used knowledge representation (KR) formats, serves as the foundation of our approach, ensuring compatibility with existing standards and enhancing interoperability. Our research enhances the aggregate and temporal aspects of ACC by addressing the limitations of traditional ACC methodologies, which often fall short in managing the nuanced temporal and aggregate requirements essential for legal reasoning. Through a case study analysis of selected regulations with aggregate and temporal facets in LI 2204, which regulates local content and participation in Ghana’s upstream petroleum industry, this paper demonstrates the effectiveness of the proposed solution in achieving these dimensions of ACC. The findings underscore the potential of Semantic Web technologies to transform ACC practices by moving towards standardized, interoperable solutions. All source codes are freely available online together with instructions to locally reproduce the simulations.

DIAERESIS: RDF data partitioning and query processing on SPARK

The explosion of the web and the abundance of linked data demand effective and efficient methods for storage, management, and querying. Apache Spark is one of the most widely used engines for big data processing, with more and more systems adopting it for efficient query answering. Existing approaches exploiting Spark for querying RDF data, adopt partitioning techniques for reducing the data that need to be accessed in order to improve efficiency. However, simplistic data partitioning fails, on one hand, to minimize data access and on the other hand to group data usually queried together. This is translated into limited improvement in terms of efficiency in query answering. In this paper, we present DIAERESIS, a novel platform that accepts as input an RDF dataset and effectively partitions it, minimizing data access and improving query answering efficiency. To achieve this, DIAERESIS first identifies the top-k most important schema nodes, i.e., the most important classes, as centroids and distributes the other schema nodes to the centroid they mostly depend on. Then, it allocates the corresponding instance nodes to the schema nodes they are instantiated under. Our algorithm enables fine-tuning of data distribution, significantly reducing data access for query answering. We experimentally evaluate our approach using both synthetic and real workloads, strictly dominating existing state-of-the-art, showing that we improve query answering in several cases by orders of magnitude.

Uniqorn: Unified question answering over RDF knowledge graphs and natural language text

Question answering over RDF data like knowledge graphs has been greatly advanced, with a number of good systems providing crisp answers for natural language questions or telegraphic queries. Some of these systems incorporate textual sources as additional evidence for the answering process, but cannot compute answers that are present in text alone. Conversely, the IR and NLP communities have addressed QA over text, but such systems barely utilize semantic data and knowledge. This paper presents a method for complex questions that can seamlessly operate over a mixture of RDF datasets and text corpora, or individual sources, in a unified framework. Our method, called Uniqorn, builds a context graph on-the-fly, by retrieving question-relevant evidences from the RDF data and/or a text corpus, using fine-tuned BERT models. The resulting graph typically contains all question-relevant evidences but also a lot of noise. Uniqorn copes with this input by a graph algorithm for Group Steiner Trees, that identifies the best answer candidates in the context graph. Experimental results on several benchmarks of complex questions with multiple entities and relations, show that Uniqorn significantly outperforms state-of-the-art methods for heterogeneous QA – in a full training mode, as well as in zero-shot settings. The graph-based methodology provides user-interpretable evidence for the complete answering process.

Formalizing and validating Wikidata’s property constraints using SHACL and SPARQL

In this paper, we delve into the crucial role of constraints in maintaining data integrity in knowledge graphs with a specific focus on Wikidata, one of the most extensive collaboratively maintained open data knowledge graphs on the Web. The World Wide Web Consortium (W3C) recommends the Shapes Constraint Language (SHACL) as the constraint language for validating Knowledge Graphs, which comes in two different levels of expressivity, SHACL-Core, as well as SHACL-SPARQL. Despite the availability of SHACL, Wikidata currently represents its property constraints through its own RDF data model, which relies on Wikidata’s specific reification mechanism based on authoritative namespaces, and – partially ambiguous – natural language definitions. In the present paper, we investigate whether and how the semantics of Wikidata property constraints, can be formalized using SHACL-Core, SHACL-SPARQL, as well as directly as SPARQL queries. While the expressivity of SHACL-Core turns out to be insufficient for expressing all Wikidata property constraint types, we present SPARQL queries to identify violations for all 32 current Wikidata constraint types. We compare the semantics of this unambiguous SPARQL formalization with Wikidata’s violation reporting system and discuss limitations in terms of evaluation via Wikidata’s public SPARQL query endpoint, due to its current scalability. Our study, on the one hand, sheds light on the unique characteristics of constraints defined by the Wikidata community, in order to improve the quality and accuracy of data in this collaborative knowledge graph. On the other hand, as a “byproduct”, our formalization extends existing benchmarks for both SHACL and SPARQL with a challenging, large-scale real-world use case.

Molecular Insights into the Binding and Conformational Changes of Hepcidin25 Blood Peptide with 4-Aminoantipyrine and Their Sorption Mechanism by Carboxylic-Functionalized Multiwalled Carbon Nanotubes: A Comprehensive Spectral Analysis and Molecular Dynamics Simulation Study.

In this work, the main purpose is to analyze and understand the mechanism and thermodynamic interactions of carboxylic acid-functionalized multiwalled carbon nanotubes (cf-MWCNTs) and 4-aminoantipyrine (AAP) with human hepcidine25 (Hep25) using multispectroscopic and molecular docking modeling methods, binding free energy calculations, and molecular dynamics (MD) simulations under physiological conditions. AAP belongs to a class of persistent environmental contaminants, and its residue is a potential hazard to human health, exhibiting a high binding affinity with blood peptides. Hepcidin is a 25-residue peptide hormone with four disulfide bonds that regulates the iron balance in vertebrates and contributes to host immunity as a cysteine-rich antimicrobial peptide. Due to their diverse properties and pollutant absorption capabilities, CNTs demonstrate important biological effects in biological applications, particularly in the noncovalent interactions with blood peptides. A comprehensive molecular dynamics simulation integrated with molecular docking methodologies was employed to explore the binding free energy between AAP and Hep25, identify binding sites, elucidate thermodynamic characteristics, and evaluate the binding forces governing their interaction. The investigation delved into elucidating the precise binding site of AAP within the Hep25 protein and thoroughly analyzed the impact of AAP on the microenvironment and conformational dynamics of Hep25. The circular dichroism (CD) experimental results highlight a reduction in β-sheet composition following the introduction of AAP and cf-MWCNT. In addition, outcomes from fluorescence spectroscopy demonstrate that both cf-MWCNT and AAP significantly attenuated Hep-25 fluorescence via a static quenching mechanism. According to the MD simulations, the presence of AAP induces changes in the secondary structure of Hep25 and enhances its hydrophobicity. Additionally, our findings demonstrated that alongside the alteration in protein structure and functionality induced by contaminants, cf-MWCNTs possess the capability to mitigate the contaminant-induced effects on Hep25 activity while preserving the overarching structural integrity of Hep25. Based on the distance and RDF data, we found that during the simulation the presence of the cf-MWCNT causes the AAP to move away from the Hep25, and as a result fewer and weaker interactions of the AAP with the Hep25 will be observed. Likewise, free energy calculations indicate that the binding of Hep25 to AAP and cf-MWCNT involves electrostatic, π-cationic, and π-π stacking interactions. The research findings offer invaluable insights into the intricate influence of pollutants and carbon nanotubes on protein functionality within the circulatory system and their toxicity in vivo for prospective investigations.

Rule based complex event processing for an air quality monitoring system in smart city

Smart city development is accelerating, aiming for systematic urban living, but challenges like air quality and traffic management need addressing for sustainability in cities. The World Health Organization (WHO) links air pollution to severe respiratory diseases, emphasizing the need for real-time air quality monitoring and timely decisions based on varying pollutant concentrations (NH3, O3, SO2, NO2) across locations. This research work proposes a framework combining Siddhi Complex Event Processing (CEP) and SPARQL queries for air quality monitoring. Dataset collected from Central Pollution Control Board (CPCB) India, is preprocessed, passed through Apache Kafka, and transformed into a knowledge graph. The RDF data is integrated with a knowledge graph via Apache Jena to enhance CEP performance. Decision trees generate rules and enrich CEP engines with CPCB parameters to identify complex patterns. SPARQL queries on real-time RDF data categorize air quality conditions for all stakeholders. The model's effectiveness is validated by deploying RDF chunks to the CEP engine and evaluating performance through various query execution times. Our model efficiently processed numerous air pollutant events (200, 400, 600 etc.), filtering out about 80% and demonstrating robust event processing capabilities, which enhances air quality management and supports sustainable urban ecosystems and societies.

SPARQL Optimization Using Re-ordering Joining Patterns with Surrogate Key Concept and Subset Patterns

Semantic web data resides on the web in the form of knowledge graphs known as RDF graphs and searching around the web has been always a crucial task. For the data retrieval of RDF data of the semantic web, SPARQL query language has been used which in turn is based on triple patterns and joins. Optimization of SPARQL query has been a problematic concern for decades due to the large amount of triple patterns associated with RDF data. Although several researchers have put a lot of effort into the optimization of SPARQL query, it is difficult to understand the concept from scratch due to its diversified nature. This paper analyses various optimization techniques for the SPARQL query used with the semantic web to process knowledge graphs. These techniques include join-based, heuristic-based, rule-based, and indexing-based approaches for optimization. This paper will help researchers in this domain to easily get into the core concept of SPARQL execution along with various optimization approaches used for query processing, which can help in various other domains like linked open data and information retrieval. In this paper, an optimization algorithm HSOA (hybrid SPARQL optimization algorithm) has been proposed, which comprises the features of index-based, cost-based, and triple reordering-based optimization approaches. The proposed hybrid algorithm has been designed specifically for n-triple RDF data, which comprises subset patterns, and surrogate key concepts. The results produced by the proposed algorithm are encouraging and have also been tested and compared with the benchmark dataset and SPARQL queries like LUBM, BSBM, and SP2Bench.

Differential privacy and SPARQL

Differential privacy is a framework that provides formal tools to develop algorithms to access databases and answer statistical queries with quantifiable accuracy and privacy guarantees. The notions of differential privacy are defined independently of the data model and the query language at steak. Most differential privacy results have been obtained on aggregation queries such as counting or finding maximum or average values, and on grouping queries over aggregations such as the creation of histograms. So far, the data model used by the framework research has typically been the relational model and the query language SQL. However, effective realizations of differential privacy for SQL queries that required joins had been limited. This has imposed severe restrictions on applying differential privacy in RDF knowledge graphs and SPARQL queries. By the simple nature of RDF data, most useful queries accessing RDF graphs will require intensive use of joins. Recently, new differential privacy techniques have been developed that can be applied to many types of joins in SQL with reasonable results. This opened the question of whether these new results carry over to RDF and SPARQL. In this paper we provide a positive answer to this question by presenting an algorithm that can answer counting queries over a large class of SPARQL queries that guarantees differential privacy, if the RDF graph is accompanied with semantic information about its structure. We have implemented our algorithm and conducted several experiments, showing the feasibility of our approach for large graph databases. Our aim has been to present an approach that can be used as a stepping stone towards extensions and other realizations of differential privacy for SPARQL and RDF.

Tuning fuzzy SPARQL queries

During the last years, the study of fuzzy database query languages has attracted the attention of many researchers. In this line of research, our group has proposed and developed FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL), which is a fuzzy extension of the Semantic Web query language SPARQL. FSA-SPARQL works with fuzzy RDF datasets and allows the definition of fuzzy queries involving fuzzy conditions through fuzzy connectives and aggregators. However, there are two main challenges to be solved for the practical applicability of FSA-SPARQL. The first problem is the lack of fuzzy RDF data sources. The second is how to customize fuzzy queries on fuzzy RDF data sources. Our research group has also recently proposed a fuzzy logic programming language called FASILL that offers powerful tuning capabilities that can accept applications in many fields. The purpose of this paper is to show how the FASILL tuning capabilities serve to accomplish in a unified framework both challenges in FSA-SPARQL: data fuzzification and query customization. More concretely, from a FSA-SPARQL to FASILL transformation, data fuzzification and query customization in FSA-SPARQL become FASILL tuning problems. We have validated the approach with queries against datasets from online communities.

A review of reasoning characteristics of RDF‐based Semantic Web systems

AbstractPresented as a research challenge in 2001, the Semantic Web (SW) is now a mature technology, used in several cross‐domain applications. One of its key benefits is a formal semantics of its RDF data format, which enables a system to validate data, infer implicit knowledge by automated reasoning, and explain it to a user; yet the analysis presented here of 71 RDF‐based SW systems (out of which 17 reasoners) reveals that the exploitation of such semantics varies a lot among all SW applications. Since the simple enumeration of systems, each one with its characteristics, might result in a clueless listing, we borrow from Software Engineering the idea of maturity model, and organize our classification around it. Our model has three orthogonal dimensions: treatment of blank nodes, degree of deductive capabilities, and explanation of results. For each dimension, we define 3–4 levels of increasing exploitation of semantics, corresponding to an increasingly sophisticated output in that dimension. Each system is then classified in each dimension, based on its documentation and published articles. The distribution of systems along each dimension is depicted in the graphical abstract. We deliberately exclude resources consumption (time and space) since it is a dimension not peculiar to SW.This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Explainable AI

Reference Relations in RDA Guidelines and Instructions: A Network Analysis

ABSTRACT This study aimed (a) to understand the actual state of reference relations in RDA guidelines and instructions, expressed as RDF data, and (b) to grasp its characteristics at a global level by applying network analysis to those reference relations. The reference relations were examined and extracted from four datasets – RDA guidelines, instructions on entities, and those on Manifestation and Work elements. Furthermore, they were aggregated by source and destinations. The characteristics of the four graphs formed by the reference relations were analyzed using measures such as centrality.

Artificial Intelligence Techniques in Semantic Web Services

It relates to the conventional machine interaction services, is the server end of a Client-Server system relates to the data on web that is understandable by the machines, its primary objective is to permit the humans and machines together. The World Wide Web is progressing quickly in the direction of the concept of semantic language for database. The ontology tool Protégé consists of the SPARQL Query Tab to execute the queries, whereas the alternative is the Apache Jena Fuseki. The semantic web framework is comprised of different Application interfaces that interacting collectively to execute RDF data. The Fuseki supports the SPARQL queries over Hyper Text Transfer Protocol. Apache Jena Fuseki is based framework for developing Artificial Intelligence Techniques in Semantic Web Services. The proposed methodology can handle queries of various convolutions and complexities. Semantic web services on the server end provide structured and standardized information about the services they offer, typically using technologies related to ontology ensure that the data is both readable and process by machines based on ontology. The Experimental Analysis is given along with the results based on different ontology based technologies. In the disciplines of design, Thinking and implementation, along with representation ontologies are used for semantic web services.

Shape Expressions (ShEx) schemas for the FHIR R5 specification

This work continues along a visionary path of using Semantic Web standards such as RDF and ShEx to make healthcare data easier to integrate for research and leading-edge patient care. The work extends the ability to use ShEx schemas to validate FHIR RDF data, thereby enhancing the semantic web ecosystem for working with FHIR and non-FHIR data using the same ShEx validation framework. It updates FHIR's ShEx schemas to fix outstanding issues and reflect changes in the definition of FHIR RDF. In addition, it experiments with expressing FHIRPath constraints (which are not captured in the XML or JSON schemas) in ShEx schemas. These extended ShEx schemas were incorporated into the FHIR R5 specification and used to successfully validate FHIR R5 examples that are included with the FHIR specification, revealing several errors in the examples.

Finding Minimum Connected Subgraphs With Ontology Exploration on Large RDF Data

In this paper, we study the following problem: given a knowledge graph (KG) and a set of input vertices (representing concepts or entities) and edge labels, we aim to find the smallest connected subgraphs containing all of the inputs. This problem plays a key role in KG-based search engines and natural language question answering systems, and it is a natural extension of the Steiner tree problem, which is known to be NP-hard. We present RECON, a system for finding approximate answers. RECON aims at achieving high accuracy with instantaneous response (i.e., sub-second/millisecond delay) over KGs with hundreds of millions edges without resorting to expensive computational resources. Furthermore, when no answer exists due to disconnection between concepts and entities, RECON refines the input to a semantically similar one based on the ontology, and attempts to find answers with respect to the refined input. We conduct a comprehensive experimental evaluation of RECON. In particular we compare it with five existing approaches for finding approximate Steiner trees. Our experiments on four large real and synthetic KGs show that RECON significantly outperforms its competitors and incurs a much smaller memory footprint.

Preparing the Romanian National Archaeological Datasets for ARIADNE

The contribution of the Romanian National Institute of Heritage (NIH) to the ARIADNE catalogue consists of 22,298 cultural resources, 18,277 of which are archaeological sites and 4021 are fieldwork reports. The two types of data were extracted from the two repositories that INP manages: the National Archaeological Repertory and the Chronicle of Archaeological Research. In Romania, the legislation regarding the protection of archaeological heritage establishes the National Archaeological Repertory as the means of managing the scientific data relating to the archaeological heritage, and of mapping archaeological sites as well as the threats to them. In addition, the same legislation stipulates that preliminary excavation reports should be published in a volume edited by the National Archaeology Commission, if the research was financed by public funds. As a result, for better dissemination, the reports are uploaded to a database. Both the Repertory and the Chronicle are accessible online through the INP management web portal. In order to fulfil the NIH's obligations within the ARIADNEplus project, several corrections and changes were made to the structures of both repositories (RAN and Chronicle) and data had to be updated. First of all, a photo upload module that illustrates both an archaeological site, perceived as a landscape, and its various components was developed. At the same time, the NIH team carried out a campaign to map the archaeological sites using coordinates in the WGS84 system, using various cartographic resources, such as topographic maps, aerial photographs and satellite images. The third activity consisted of creating a hierarchical list of archaeological site types and their components to support a more accurate description and retrieval of database records. Conversion of the RAN archaeological records to AO-Cat compliant RDF data was the fourth activity conducted by NIIH in order to include the RAN records in the ARIADNE Knowledge Base. Subsequently, a hierarchical list of the archaeological periods used in Romanian archaeology was developed, where every period is described by three main components: name, time interval and geographical area. This list has been converted to the PeriodO format and will be imported into the gazetteer.

Organizing Scholarly Knowledge in the Open Research Knowledge Graph

The Open Research Knowledge Graph (ORKG) is an Open Science digital infrastructure for the production, curation, publication, and reuse of machine-actionable scholarly knowledge. Built on top of the RDF data model and extensible ontologies, the ORKG provides a common vocabulary for researchers to describe their research contributions and data, improving the discoverability and reusability of scholarly knowledge and research data. The ORKG includes tools for visualizing the relationships between different entities, making it easier to understand the connections between different pieces of research and their findings. It facilitates collaboration between researchers by providing a collaborative platform for organizing and sharing scholarly knowledge and data, reducing duplication and enabling more efficient use of resources. As research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research.

A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

AbstractDuring the last few years, there has been increasing attention paid to serious games (SGs), which are games used for non‐entertainment purposes. SGs offer the potential for more valid and reliable assessments compared to traditional methods such as paper‐and‐pencil tests. However, the incorporation of assessment features into SGs is still in its early stages, requiring specific design efforts for each game and adding significant time to the design of Game‐based Assessments (GBAs). In this research, we present a completely novel framework that aims to perform interoperable GBAs by: (a) integrating a common GBA ontology model to process RDF data; (b) developing in‐game metrics to infer useful information and assess learners; (c) integrating a service API to provide an easy way to interact with the framework. We then validate our approach through performance evaluation and two use cases, demonstrating its effectiveness in real‐world scenarios with large‐scale datasets. Our results show that the developed framework achieves excellent performance, replicating metrics from previous literature. We anticipate that our work will help alleviate current limitations in the field and facilitate the deployment of GBAs as a Service.

Developing a model semantic‐based image retrieval by combining KD‐Tree structure with ontology

AbstractThe paper proposes an alternative approach to improve the performance of image retrieval. In this work, a framework for image retrieval based on machine learning and semantic retrieval is proposed. In the preprocessing phase, the image is segmented objects by using Graph‐cut, and the feature vectors of objects presented in the image and their visual relationships are extracted using R‐CNN. The feature vectors, visual relationships, and their symbolic labels are stored in KD‐Tree data structures which can be used to predict the label of objects and visual relationships later. To facilitate semantic query, the images use the RDF data model and create an ontology for the symbolic labels annotated. For each query image, after extracting their feature vectors, the KD‐Tree is used to classify the objects and predict their relationship. After that, a SPARQL query is built to extract a set of similar images. The SPARQL query consists of triple statements describing the objects and their relationship which were previously predicted. The evaluation of the framework with the MS‐COCO dataset and Flickr showed that the precision achieved scores of 0.9218 and 0.9370, respectively.

Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering

We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains.

A non-functional requirements-based ontology for supporting the development of industrial energy management systems

Non-functional requirements (NFRs) are essential in the development of industrial energy management systems (IEMS). This paper presents a comprehensive ontology model for NFRs in IEMS, derived from an extensive survey of NFRs in both the software industry and the energy domain. The proposed ontology encompasses four critical factors influencing the quality attributes of IEMS: technologies, stakeholders, markets, and regulations. Implemented using the OWL 2 DL standard, the ontology model aims to provide a clear and consistent understanding of NFRs and their relationship to the energy domain, as well as identify which factors have a significant impact on the environmental performance of IEMS. The ontology is evaluated through various methods, such as technical validation, user evaluations, its applications in RDF data management, and application-based evaluation, including software architecture, knowledge base data model design, and regulatory framework design. By understanding the link between software quality requirements and the characterizing factors of the energy domain, as provided by our ontology model, this information can be used to inform life cycle assessments and quantify potential reductions in energy consumption, emissions, waste generation, and other environmental impacts associated with implementing cleaner and more sustainable solutions. The results demonstrate that the proposed ontology effectively supports IEMS development and serves as a foundation for developing reusable and adaptable software systems across different industrial domains.