Semantic Data Model Research Articles

A generic framework for the semantic contextualization of indicators

Indicators are quantitative or qualitative measures used to gauge various aspects of society and assess change over time (such as monitoring the progress or effectiveness of a public policy). Ideally, indicators should be precisely defined and measured according to harmonized procedures that may not be feasible in practice, especially in domains such as health, where indicators are often derived from preexisting, heterogeneous datasets. Integrating such data has posed a persistent challenge, but semantic technologies offer advantages by enriching data in a relatively simple, linkable, and non-disruptive way. However, without harmonized frameworks, the difficulties associated with data integration are unlikely to be resolved. In this article, we propose a generic, domain-neutral indicator contextualization framework for structuring and linking distributed datasets with contextual metadata according to a standard model. The framework integrates the concepts of the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 11179 metadata registry standard with the common core ontologies (CCO) mid-level ontology suite, and incorporates other semantic technologies to make it adaptable and interoperable within and across domains. Application of the framework to an example indicator illustrates the versatility and adaptability of the approach in a federated data architecture. The contextual information can be dereferenced using standard query tools to provide data users a comprehensive understanding and overview of the indicator. The framework is amenable to deep learning applications via the principles of semantic data models, linked open data, and knowledge organization systems. The ideas are presented to stimulate further reflection and consolidation of standard data contextualization frameworks.

A unified approach to publish semantic annotations of agricultural documents as knowledge graphs

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

Semantic interoperability for an AI-based applications platform for smart hospitals using HL7 FHIR

The digitization of the healthcare domain has the potential to drastically improve healthcare services, reduce the time to diagnosis, and lower costs. However, digital applications for the healthcare domain need to be interoperable to maximize their potential. Additionally, with the rapid expansion of Artificial Intelligence (AI) and, specifically, Machine Learning (ML), large amounts of diverse types of data are being utilized. Thus, to achieve interoperability in such applications, the adoption of common semantic data models becomes imperative. In this paper, we describe the adoption of such a common semantic data model, using the well-known Health Level Seven Fast Health Interoperability Resources (HL7 FHIR) standard, in a platform that assists in the creation and storage of a plethora of AI-based applications for several medical conditions. The FHIR server’s efficiency is being showcased by using it in an application predicting coronary artery stenosis as well as for managing the platform’s key performance indicators.

Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems

Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous use of the file system and RDMS. This simultaneous use can have many benefits, but keeping data in RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow for keeping file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS LinkAhead (previously named “CaosDB”).

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genesand diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions

This paper presents a comprehensive understanding of current digital twin (DT) implementations in the construction industry, along with providing an overview of technologies enabling the operation of DTs in the industry. To this end, 145 publications were identified using a systematic literature review. The results revealed eight key areas of DT implementation including (i) virtual design, (ii) project planning and management, (iii) asset management and maintenance, (iv) safety management, (v) energy efficiency and sustainability, (vi) quality control and management, (vii) supply chain management and logistics, and (viii) structural health monitoring. The findings demonstrate that DT technology has the capacity to revolutionise the construction industry across these areas, enabling optimised designs, improved collaboration, real-time monitoring, predictive maintenance, enhanced safety practices, energy performance optimisation, quality inspections, efficient supply chain management, and proactive maintenance. This study also identified several challenges that hinder the widespread implementation of DT in construction, including (i) data integration and interoperability, (ii) data accuracy and completeness, (iii) scalability and complexity, (iv) privacy and security, and (v) standards and governance. To address these challenges, this paper recommends prioritising standardised data formats, protocols, and APIs for seamless collaboration, exploring semantic data modelling and ontologies for data integration, implementing validation processes and robust data governance for accuracy and completeness, harnessing high-performance computing and advanced modelling techniques for scalability and complexity, establishing comprehensive data protection and access controls for privacy and security, and developing widely accepted standards and governance frameworks with industry-wide collaboration. By addressing these challenges, the construction industry can unlock the full potential of DT technology, thus enhancing safety, reliability, and efficiency in construction projects.

DIGITAL HOLISTIC DOCUMENTATION OF CULTURAL HERITAGE: CHALLENGES AND RISKS, TOWARDS SHAPING THE FUTURE

Abstract. This paper presents the results of the Horizon 2020 ERA Chair in Digital Cultural Heritage: ‘Mnemosyne’ project, which aims to achieve excellence in research and innovation in the holistic documentation of the past. The project's research agenda includes six broad thematic areas, which encompass data acquisition, processing, semantic data modelling, knowledge management, data long-term preservation, and data use and reuse. The paper focuses on the key results of the project during which several researchers have been training, some of whom propose presenting their experience in three out of the total seventeen heritage-documentation case studies chosen to be studied and digitized. The paper illustrates the significant results of the case studies and how they can fulfil the needs of the multidisciplinary community of experts. Mnemosyne’s research team has developed useful research methodologies for the holistic documentation and digitization of tangible cultural heritage objects, monuments, and sites, as well as novel research approaches regarding their embedded intangible aspects and features.

HL7-FHIR-Based ContSys Formal Ontology for Enabling Continuity of Care Data Interoperability.

The rapid advancement of digital technologies and recent global pandemic-like scenarios have pressed our society to reform and adapt health and social care toward personalizing the home care setting. This transformation assists in avoiding treatment in crowded secondary health care facilities and improves the experience and impact on both healthcare professionals and service users alike. The interoperability challenge through standards-based roadmaps is the lynchpin toward enabling the efficient interconnection between health and social care services. Hence, facilitating safe and trustworthy data workflow from one healthcare system to another is a crucial aspect of the communication process. In this paper, we showcase a methodology as to how we can extract, transform and load data in a semi-automated process using a common semantic standardized data model (CSSDM) to generate a personalized healthcare knowledge graph (KG). CSSDM is based on a formal ontology of ISO 13940:2015 ContSys for conceptual grounding and FHIR-based specification to accommodate structural attributes to generate KG. The goal of CSSDM is to offer an alternative pathway to discuss interoperability by supporting a unique collaboration between a company creating a health information system and a cloud-enabled health service. The resulting pathway of communication provides access to multiple stakeholders for sharing high-quality data and information.

From Record to Data. New purposes for Archival Description processes

The archival description has undergone a long process of development and standardization. The work of the Experts Group on Archival Description in developing the Records in Contexts (RiC-CM) and Records in Contexts Ontology (RiC-O) models has contributed to rekindling reflection on the integration of archival description into broader information retrieval systems which could potentially facilitate the dissemination of knowledge. The contribution attempts to reflect on the potential and possible critical issues in semantic data modeling from an integrated description perspective, inviting greater reflection on the design and on the management of possible digital information restitution environments.

FEED2SEARCH: a framework for hybrid-molecule based semantic search

ABSTRACT Adopting semantic technologies has proven several benefits for enabling a better representation of the data and empowering reasoning capabilities over it. However, there are still unresolved issues, such as the shift from heterogeneous documents to semantic data models and the representation of search results. Thus, in this paper, we introduce a novel F ram E work for hybrid mol E cule-base D SE mantic SEARCH , entitled FEED2SEARCH, which facilitates Information Retrieval over a heterogeneous document corpus. We first propose a semantic representation of the corpus, which automatically generates a semantic graph covering both structural and domain-specific aspects. Then, we propose a query processing pipeline based on a novel data structure for query answers, extracted from this graph, which embeds core information together with structural-based and domain-specific context. This provides users with interpretable search results, helping them understand relevant information and track cross document dependencies. A set of experiments conducted using real-world construction projects from the Architecture, Engineering and Construction (AEC) industry shows promising results, which motivates us to further investigate the effectiveness of our proposal in other domains.

Fuzzy RDF Knowledge Graph Embeddings Through Vector Space Model

Resource description framework (RDF) is a World Wide Web consortium recommendation and has been widely accepted for semantic data models (e.g., knowledge graphs). As such, a large amount of RDF data is readily available. Information in the real world is often uncertain, and uncertain RDF data models have been proposed and received increasing attention. Knowledge graph embedding is an effective way to perform vector projection of structured knowledge, after which we can infer a large-scale knowledge graph. Nowadays, knowledge graph embedding models are widely proposed, but most of them can only handle crisp information. In this article, we concentrate on modeling fuzzy RDF graphs in the vector space. We propose a fuzzy RDF knowledge graph embedding model (FRKGE), which can project the fuzzy entities and fuzzy relations with membership degree to the vector space. Our proposed model preserves the structure of the fuzzy RDF knowledge graph and the explicit expression of membership degree in the vector space, which provides a natural expression of fuzzy RDF tuples. Our experimental results demonstrate that the FRKGE model is competent for the task of representation learning for fuzzy RDF knowledge graphs.

Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks

In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy.

Constructing a Semantic Data Model for the Field of Material Science through Ontology

This research presents a method for constructing a semantic data model for the field of material science based on ontology. The method involves designing the semantic data model and establishing mapping rules between the data model and the ontology. Data semantic analysis and annotation are carried out based on the semantic data model, supporting the integration and efficient retrieval of heterogeneous data in the material science field. Furthermore, the method utilizes the OWL-S technique to describe, issue, and acquire relevant data services, enabling higher-level service semantic synergy in the material science domain. The proposed method effectively enables semantic integration, intelligent inquiry, and personalized services for data in the material science field.

Semantic data model for knowledge representation and dissemination of cultural heritage site, Poompuhar

Semantic data model for knowledge representation and dissemination of cultural heritage site, Poompuhar

Applied personal profile ontology for personnel appraisals

Purpose The purpose of this paper is to apply the earlier enhanced personal profile ontology (e-PPO) developed by the authors as a case study for the appraisal of the lecturers in the department of computer science, University of Uyo, Uyo for the purposes of promotions. The developed e-PPO was a sample smart résumé for the selection of the best among three personnel using linguistic variables and formal rules representing the combination of the criteria and subcriteria was illustrated which was used to allocate competent personnel for software requirement engineering tasks. The need for the use of the smart resume for appraisal purposes was pointed out in the conference paper, calling for the applicant’s data to be inputted into the enhanced personal profile ontology (e-PPO) for personnel appraisa. Design/methodology/approach Appraisal is a regular review of employees’ performances and their overall contribution to the organization they are working for. The availability of a web application for personnel appraisal requires PPO which includes both static and dynamic features. Personal profile is often modified for several purposes calling for augmentation and annotation when needs arise. Resume is one resulting extract from personal profile and often contain slightly different information based on needs. The urgent preparation of resume may introduce bias and incorrect information for the sole aim of projecting the personnel as being qualified for the available job. Religious and gender biases may sometimes be observed during appointments of new personnel, which may not be the case during appraisals for promotions or reassignment of tasks because such biases become insignificant given the fact that job targets and the skills needed are already set and the appraisals passes through several phases that are not determined by a single individual. This work therefore applied the earlier developed e-PPO for appraisal of the academic staff of the department of computer science, university of Uyo, Uyo, Nigeria. A mixed approach of existing ontologies like Methontology and Neon have been followed in the creation of the e-PPO, which is a constraint-based semantic data model tested using Protégé inbuilt reasoner with its updated plugins. Upon application of e-PPO on personnel appraisals, promotion and selection of employee for specific assignments in any organization is possible using the smart resume. Findings The use of the smart resume reduces the numerous task that would have been taken up by the human resource team, thereby reducing the processing time for the appraisals. The appraisal task is done void of biases of any kind such as gender and religion. Originality/value This work is an extension of the original work done by the authors.

MaCOnto: A robust maize crop ontology based on soils, fertilizers and irrigation knowledge

• A robust OWL rule based autonomous evolution ontology for representing the maize crop knowledge. • MaCOnto: implemented as rules via SWRL, and queried by SQWRL. • Novel algorithmic design for the ontology to autonomously evolved is implemented in java environment. • MaCOnto capable to infer and respond to user's query based on its contextual information. The demand for relevant information in a timely manner portrays the significance of knowledge management in all areas of lives; for instance, agriculture. To this end, soils, fertilizers and irrigation as agronomic concepts are essential knowledge inputs for any crops, such as maize. Conversely, there is always difficulty in timely retrieval of these relevant information owing to the unstructured nature of data in repositories, and complexity of concepts mismatch. Sequel to this development, ontology, a semantic data modeling technique is promising as it has been recently employed to deal with these challenges across different domains. However, the robustness of ontology, in terms of semantic expressivity of hidden knowledge, and autonomous growth of ontology leave some gaps to contend with. In view of this development, this research aims to design a robust OWL Rule based ontology for maize crop domain by considering primarily soils, fertilizers and irrigation agronomic concepts capable to evolve autonomously. The proposed ontology herein christened MaCOnto , is developed using the adapted six steps ontology-engineering principle. Over 1,430 entities are encoded in OWL; eighty Competency Questions (CQs) validated by domain experts are modeled in FOL, and implemented as rules via SWRL. Thus, the ontology is queried by SQWRL. Besides, the novel algorithmic design for the ontology to autonomously evolve is implemented in Java environment by employing WordNet. The results obtained from structural based evaluation show an outstanding performance across the eight metrics. Similarly, the results of the competency-based evaluation are also promising. Therefore, the proposed MaCOnto is a robust application based ontology capable to infer and responds to user's query based on its contextual information.

EXTENDING CITYGML 3.0 TO SUPPORT 3D UNDERGROUND LAND ADMINISTRATION

Abstract. Rapid development of underground space necessitates the efficient management of underground areas. Data modelling plays an underpinning role in integrating and managing underground physical and legal data. The physical data refers to semantic and spatial data of underground assets such as utilities, tunnels, and basements, while the legal data comprises the ownership information and the extent of underground legal spaces and the semantic and spatial relationships between legal spaces. Current Underground Land Administration (ULA) practices mainly focus on representing only either legal spaces or the physical reality of subsurface objects using fragmented and isolated 2D drawings, leading to ineffective ULA. A complete and accurate 3D representation of underground legal spaces integrated with the 3D model of their physical counterparts can support different use cases of ULA beyond underground land registration, such as planning, design and construction of underground assets (e.g. tunnels and train stations), utility management and excavation. CityGML is a prominent semantic data model to represent 3D urban objects at a city scale, making it a good choice for underground because underground assets such as tunnels and utilities are often modelled at city scales. However, CityGML, in its current version, does not support legal information. This research aims to develop an Application Domain Extension (ADE) for CityGML to support 3D ULA based on the requirements defined in the Victorian state of Australia. These requirements include primary underground parcels and secondary underground interests. This work extends CityGML 3.0, which is the new version of this model. In CityGML 3.0, UML conceptual models as platform-independent models are suggested to express ADEs. Thus, the ADE proposed in this study will be based on UML. The findings of this study show that extending CityGML to support legal information can be a viable solution to meet the requirements of a 3D integrated model for ULA. The CityGML ADE proposed in this study can potentially provide a new solution for 3D digital management of underground ownership rights in Victoria, and it can be used to implement an integrated 3D digital data environment for ULA.

Agile Research Data Management with FDOs using LinkAhead

Agile Research Data Management with FDOs using LinkAhead

Database development, design and trending tools

It is very important for database management system (DBMS) users to make sure there is illustration of the logical view of data using a precise data model; it includes hierarchical, network and relational models. The commercially available DBMS are based on the models but the relational product seems to have overshadowed other models. There is an effort been made to break the monopoly of the relational model which resulted in new data models like Object-oriented data model, Entity-Relationship data model, Functional data model, and Semantic data model. Also, an advance in the area of networking has given rise to database servers and its descendant, distributed databases. With this development, geographically distributed organization has its communication bills reduced significantly. For future research, database development tools may depend on the involvement of programming language researchers in databases, the impact of the object technology on the dominating relational model and whether the attempts to integrate knowledge and data bases ultimately yield commercially affordable products.

Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.