Semantic Web Technologies Research Articles

A review based knowledge organization supported research framework for digital storytelling from travel records

Purpose This study presents a review based research framework that aims to influence memory institutions in their projects on digital storytelling from digitized ancient travel records. This study aims to influence research and policymaking related to design and delivery of services based on memory institutions’ collections of historical records. Design/methodology/approach The demonstrated research framework has been synthesized using inputs from a review of existing studies on the domain accompanied by a short survey created for collecting the opinion of selected experts. Studies demonstrating utilization of semantic web technologies and those that can influence policymaking related to digital storytelling were primarily reviewed. Findings The core tasks behind digital storytelling vary depending on the project goals. So, a two-part framework had to be proposed that covers the generic fundamental tasks with diverse applicability and digital storytelling related specific tasks separately. Also during the review, it was found that studies demonstrating the use of travel records for digital storytelling were less in number compared to studies using digital storytelling for tourism in general. Originality/value The demonstrated research framework can guide memory institutions in exposing their travel-related holdings to a wider audience using innovative semantic web technologies and open up avenues for future empirical research thereby adding to the novelty of the presented research. Also, reviews of articles on digital storytelling or digital humanities in general exist, but, review of digital storytelling initiatives focusing specifically on tourism and travel literature is scarce.

”CITYJSON2RDF” A CONVERTER FOR PRODUCING 3D CITY KNOWLEDGE GRAPHS

Abstract. The increasing prevalence of 3D city models (3DCMs) in various applications, such as mixed reality and navigation, highlights the need for efficient data exchange. CityGML serves as a standard model for this purpose, encompassing geometric and semantic information in 3DCM data. To enhance interoperability, a compatible transfer mechanism is essential. This study introduces a conversion tool that transforms CityGML data into RDF, a knowledge graph (KG) format. Utilizing semantic web technologies, this conversion ensures the data’s seamless integration across applications. The RDF model facilitates linking to open ontologies, promoting data circulation without loss. The tool employs CityJSON encoding for its mappable JSON structure, enabling straight-forward conversion to RDF using Python components. While existing XML to RDF tools exist, this tool distinguishes itself by addressing accessibility and user intervention challenges. Linking is established by matching subject classes with relevant ontology definitions, a process dependent on developers’ understanding of the CityGML data model. The tool, accessible through URL-2, is still in development, offering a promising solution for achieving effective 3DCM data interoperability.

OpenCitations Meta

Abstract OpenCitations Meta is a new database for open bibliographic metadata of scholarly publications involved in the citations indexed by the OpenCitations infrastructure, adhering to Open Science principles and published under a CC0 license to promote maximum reuse. It presently incorporates bibliographic metadata for publications recorded in Crossref, DataCite, and PubMed, making it the largest bibliographic metadata source using Semantic Web technologies. It assigns new globally persistent identifiers (PIDs), known as OpenCitations Meta Identifiers (OMIDs) to all bibliographic resources, enabling it both to disambiguate publications described using different external PIDS (e.g., a DOI in Crossref and a PMID in PubMed) and to handle citations involving publications lacking external PIDs. By hosting bibliographic metadata internally, OpenCitations Meta eliminates its former reliance on API calls to external resources and thus enhances performance in response to user queries. Its automated data curation, following the OpenCitations Data Model, includes deduplication, error correction, metadata enrichment, and full provenance tracking, ensuring transparency and traceability of data and bolstering confidence in data integrity, a feature unparalleled in other bibliographic databases. Its commitment to Semantic Web standards ensures superior interoperability compared to other machine-readable formats, with availability via a SPARQL endpoint, REST APIs, and data dumps.

The evolution of the Semantic Web technologies: problems and prospects

The evolution of the Semantic Web technologies: problems and prospects

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.

Interlinking BIM and GIS data for a semantic pedestrian network and applications in high-density cities

In high-density cities, pedestrians frequently traverse many publicly accessible indoor spaces, such as metro stations and footbridges, which seamlessly connect with outdoor sidewalks, forming indoor-outdoor combined pedestrian networks. However, the information island phenomenon hinders these connections in the digital world because outdoor sidewalks and indoor spaces are modelled using Geographical Information Systems (GIS) and Building Information Modelling (BIM) technologies, respectively, which challenges obtaining integrated information for intelligent pedestrian services. This study presents an approach for interlinking BIM and GIS data for a semantic pedestrian network using semantic web technologies. The proposed approach automatically converts BIM and GIS data into linked data and establishes interlinkages between BIM and GIS data through semantic queries and inferences. The resulting semantic pedestrian network based on integrated linked data graphs forms a knowledge base, including topological and geometrical information and abundant semantic information from BIM and GIS datasets. The application potential of the semantic pedestrian network is demonstrated through information query and semantic route planning. This research contributes to establishing indoor-outdoor combined semantic pedestrian networks ensuring seamless data integration.

A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors.

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.

Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics

ABSTRACT The past decade has witnessed the rapid development of geospatial artificial intelligence (GeoAI) primarily due to the ground-breaking achievements in deep learning and machine learning. A growing number of scholars from cartography have demonstrated successfully that GeoAI can accelerate previously complex cartographic design tasks and even enable cartographic creativity in new ways. Despite the promise of GeoAI, researchers and practitioners have growing concerns about the ethical issues of GeoAI for cartography. In this paper, we conducted a systematic content analysis and narrative synthesis of research studies integrating GeoAI and cartography to summarize current research and development trends regarding the usage of GeoAI for cartographic design. Based on this review and synthesis, we first identify dimensions of GeoAI methods for cartography such as data sources, data formats, map evaluations, and six contemporary GeoAI models, each of which serves a variety of cartographic tasks. These models include decision trees, knowledge graph and semantic web technologies, deep convolutional neural networks, generative adversarial networks, graph neural networks, and reinforcement learning. Further, we summarize seven cartographic design applications where GeoAI have been effectively employed: generalization, symbolization, typography, map reading, map interpretation, map analysis, and map production. We also raise five potential ethical challenges that need to be addressed in the integration of GeoAI for cartography: commodification, responsibility, privacy, bias, and (together) transparency, explainability, and provenance. We conclude by identifying four potential research directions for future cartographic research with GeoAI: GeoAI-enabled active cartographic symbolism, human-in-the-loop GeoAI for cartography, GeoAI-based mapping-as-a-service, and generative GeoAI for cartography.

Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing

High-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.

A Three-Pronged Verification Approach to Higher-Level Verification Using Graph Data Structures

Individual model verification is a common practice that increases the quality of design on the left side of the Vee model, often before costly builds and prototypes are implemented. However, verification that spans multiple models at higher levels of abstraction (e.g., subsystem, system, mission) is a complicated endeavor due to the federated nature of the data. This paper presents a tool-agnostic approach to higher-level verification tasks that incorporates tools from Semantic Web Technologies (SWTs) and graph theory more generally to enable a three-pronged verification approach to connected data. The methods presented herein use existing SWTs to characterize a verification approach using ontology-aligned data from both an open-world and closed-world perspective. General graph-based algorithms are then introduced to further explore structural aspects of portions of the graph. This verification approach enables a robust model-based verification on the left side of the Vee model to reduce risk and increase the visibility of the design and analysis work being performed by multidisciplinary teams.

Leveraging semantic context to establish access controls for secure cloud-based electronic health records

With the continuous growth of cloud-based Electronic Health Record (EHR) systems and medical data, medical organizations are particularly concerned about storing patient data to provide fast services while adhering to privacy and security concerns. Existing EHR systems often face challenges in handling heterogeneous data and maintaining good performance with data growth. These systems mostly use relational databases or partially store data in a knowledge graph, making it challenging to handle big data and allowing flexible schema expansion. Hence, there is a need to address these problems. This paper provides a solution by proposing a novel graph-based EHR system integrating Attribute-Based Encryption and Semantic Web Technologies to ensure fine-grained EHR field-level security of patient records. Our approach leverages semantic context to query through a knowledge graph that stores encrypted medical data in the nodes, making it possible to handle heterogeneous data while ensuring optimal performance and preserving patient privacy.

Paving the way for automated factory planning – applying rule-based expert systems to capacity planning

Today, numerous different software systems aid factory planners in their tasks. Nevertheless, due to their lacking interoperability and project-specific approaches, generic support for automated decision-making is still missing. Investigating the state of the art, we conclude that knowledge-based information modeling is needed for decision-making support. However, as the identified approaches of previous works propose no general automation concepts. Therefore, we define the guiding research question as how to model processual domain knowledge for automating factory planning processes. In this paper, we propose a planning assistance on rule-based expert systems. The planning assistance is composed of an ontology-based information model, a planning model consisting of individual planning functions, and a domain-specific inference engine. We implement the planning assistance with Semantic Web technologies and validate the solution using an application example from capacity planning. Thereby, we demonstrate the applicability of rule-based expert systems for automated factory planning. Finally, implications for future research are drawn for exploring further application areas and developing anticipated hybrid solution concepts.

Semantic Web of Things for pollution measurement and validation interoperability using AI Techniques

In response to the growing IoT device diversity, efforts are underway to better integrate data, applications, and services. The Semantic Web, known for its simplicity in integration, has the potential to improve data interpretation and interoperability. In this research, a pollution management model is used, combining the Semantic Web of Things (SWoT) and Artificial Intelligence (AI), to create smarter cities, providing real-time environmental information. The dataset has been sourced from Aarhus City, Denmark, and the study outlines Semantic Web Technologies (SWTs) in IoT frameworks, including common ontologies for IoT-based architecture. The dataset’s relationship between various gases/pollutants is analyzed using correlation matrix. Machine learning methods like Multi-Layer Perceptron (MLP) with Sigmoid, ReLU, Tanh, Maxout, Swish hybrid activation functions are employed, with results assessed using Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). A comparison of errors for different activation functions is also performed and the findings reveal good results when comparing actual and predicted values in the proposed model.

Ontology-Based Traceability System for Halal Status of Flavour

Recent market survey on halal products indicated an increase in the demand for halal products throughout the world. In Malaysia, JAKIM is the body responsible in matters relating to approve the halal certification. However, the process of issuing the halal certificate is time consuming. This study further identified the issues and contributing factors in the delay in approval for halal certification application by reviewing related works and conducted a preliminary interview with the JAKIM authorities. The reasons for the delay in issuing halal certification is the constraints in determining halal status of flavours due to the absence of halal certificate when auditors were processing the documentation for applying certification. In addition, the inconsistent use of terms among the food producers and the auditors makes it difficult to trace halal status of flavouring. However these factors are isolated. None of the studies have clearly depicted the interrelationship among factors and constructs. This paper therefore aims to theoretically integrate the factors and constructs as a conceptual framework. The conceptual framework shows the relationship of the concepts: halal certification process, halal concept, as well as reference sources and the concept of traceability using an ontology-based semantic web technology approach to trace the halal status of ingredients, especially in the chemical properties section consisting of various reference sources. The conceptual framework could then be used as a basis to design a framework for tracing flavouring information to accelerate the halal certification.

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.

Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity

As Large Language Models (LLMs) become increasingly sophisticated and ubiquitous in natural language processing (NLP) applications, ensuring their robustness, trustworthiness, and alignment with human values has become a critical challenge. This paper presents a novel framework for contextual grounding in textual models, with a particular emphasis on the Context Representation stage. Our approach aims to enhance the reliability and ethical alignment of these models through a comprehensive, context-aware methodology. By explicitly capturing and representing relevant situational, cultural, and ethical contexts in a machine-readable format, we lay the foundation for anchoring a model's behavior within these contexts. Our approach leverages techniques from knowledge representation and reasoning, such as ontologies, semantic web technologies, and logic-based formalisms. We evaluate our framework on real-world textual datasets, demonstrating its effectiveness in improving model performance, fairness, and alignment with human expectations, while maintaining high accuracy. Furthermore, we discuss the other key components of the framework, including context-aware encoding, context-aware learning, interpretability and explainability, and continuous monitoring and adaptation. This research contributes to the growing body of work on responsible AI, offering a practical approach to developing more reliable, trustworthy, and ethically-aligned language models. Our findings have significant implications for the deployment of LLMs in sensitive domains such as healthcare, legal systems, and social services, where contextual understanding is paramount.

A Methodology for Integrating Hierarchical VMAP-Data Structures into an Ontology Using Semantically Represented Analyses

Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 ”Standard for the Exchange of Product model data”) fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard ”Virtual Material Modelling in Manufacturing” (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated.

Application of Semantic Web Technologies for Supporting Customer Relationship Management: a Systematic Literature Review

The purpose of the present paper is to summarize the current state of the existing research on the application of Semantic web technologies in supporting customer relationship management. Its achievement includes providing answers to a set of research questions as a result of conducting a systematic literature review. A total of 44 scientific publications are identified as relevant to the topic and included in the review. Information is extracted from the selected literature sources, which is then summarized, systemized and analyzed according to the predefined research questions and finally reported. The conducted systematic literature review determines that the development of Semantic web technologies is provoked interest among researchers, as a result of which the advantages of using them for descriptions useful for various CRM purposes are investigated and practically confirmed. In addition to defining semantic models for descriptions supporting a variety of CRM activities and processes (such as customized products and services; supporting users of CRM systems; integrated offerings across channels; improved and innovative products and services; customer complaint management, etc.), various research works identify new approaches to support CRM, that can be achieved through the application of appropriate Semantic web technologies. The detailed study represented in this paper contributes to familiarization with the existing experience in the application of Semantic web technologies in supporting customer relationship management, as well as facilitates the discovery of trends and directions for future research. This is the reason for the expected interest from scientists whose research area cover the considered and similar fields; software engineers implementing CRM systems; data analysts exploring CRM domain.

Ontologies in digital twins: A systematic literature review

Digital Twins (DT) facilitate monitoring and reasoning processes in cyber–physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.

Using semantic story maps to describe a territory beyond its map

The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool’s effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.