Showing papers on "Semantic interoperability published in 2023"

Interoperability of Heterogeneous Systems of Systems: Review of Challenges, Emerging Requirements and Options

Abstract: Interoperability is one of the critical challenges in the construction and management of distributed and collaborative systems. Hence, a deep understanding of the fundamental barriers to interoperability and of the key requirements that systems must meet to be interoperable is essential. In this direction, in the first part of this research, we conducted a questionnaire survey of stakeholders and practitioners of distributed and collaborative systems. As a result, we identified eight essential interoperability requirements and corresponding challenges. Then, in the second part of our study, we performed a critical literature survey of the building blocks of interoperability to understand the ability of current conceptual approaches---and related technologies---to address the identified requirements. The results of our research can significantly impact the software engineering of interoperable systems by introducing their fundamental requirements and the best practices to address them.

A systematic review on semantic interoperability in the IoE-enabled smart cities

Abstract: Smart cities have emerged as a result of smart interconnections of people, processes, data, and things, representing an excellent case study of the Internet of Everything (IoE) paradigm. One of the main challenges in realizing the smart city vision is how to provide seamless interoperability between the IoE entities. In this paper we conduct a systematic literature review on the use of semantic technologies to support interoperability between IoE entities in smart cities, with the goal of identifying the main trends and challenges in adopting semantic interoperability solutions for sustainable, green, and resilient smart cities. To this end, we have extracted data from selected primary studies over the last decade that address semantic interoperability issues in smart cities through related technologies and techniques such as ontologies, linked open data, knowledge graphs, ontology alignment/matching methods, and automated reasoning mechanisms. We have analyzed the maturity of this research area by exploring three research questions that focus on: i) the importance of semantic interoperability in the smart city domain; ii) the identification of semantic technologies and tools applied in the smart city domain to promote semantic interoperability; and iii) the identification of smart city application areas where semantic technologies are used to efficiently deliver smart services. The analysis provided research insights, including the introduction of a new evaluation framework that assesses semantic interoperability solutions on four maturity levels. The framework includes specific evaluation criteria for attributes such as modeling, scalability, and availability. Finally, an elaborated list of strengths, opportunities, weaknesses, and threats of semantic interoperability solutions in smart cities is provided, along with a discussion of open challenges and future work in this domain.

Interoperability of heterogeneous health information systems: a systematic literature review

Abstract: The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them.In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis.Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously.The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.

Exploring Blockchains Interoperability: A Systematic Survey

Abstract: The next-generation blockchain ecosystem is expected to integrate both homogeneous and heterogeneous distributed ledgers. These systems require operations across multiple blockchains to enrich advanced functionalities for future applications. However, the development of blockchain interoperability involves much more complexity regarding the variety of underlying architectures. Guaranteeing the properties of ACID (Atomicity, Consistency, Isolation, Durability) across diverse blockchain systems remains challenging. To clear the fog, this paper accordingly provides a comprehensive review of the current progress of blockchain interoperability. We explore the general principles and procedures for interoperable blockchain systems to highlight their design commons. Then, we survey practical instances and compare state-of-the-art systems to present their unique features between distinct solutions. Finally, we discuss critical challenges and point out potential research directions. We believe our work can provide an intuitive guideline for newcomers and also promote rapid development in terms of blockchain interoperability.

A Systematic Review on Semantic Interoperability in the IoE-enabled Smart Cities

Abstract: Smart cities have emerged as a result of smart interconnections of people, processes, data, and things, representing an excellent case study of the Internet of Everything (IoE) paradigm. One of the main challenges in realizing the smart city vision is how to provide seamless interoperability between the IoE entities. In this paper we conduct a systematic literature review on the use of semantic technologies to support interoperability between IoE entities in smart cities, with the goal of identifying the main trends and challenges in adopting semantic interoperability solutions for sustainable, green, and resilient smart cities. To this end, we have extracted data from selected primary studies over the last decade that address semantic interoperability issues in smart cities through related technologies and techniques such as ontologies, linked open data, knowledge graphs, ontology alignment/matching methods, and automated reasoning mechanisms. We have analyzed the maturity of this research area by exploring three research questions that focus on: i) the importance of semantic interoperability in the smart city domain; ii) the identification of semantic technologies and tools applied in the smart city domain to promote semantic interoperability; and iii) the identification of smart city application areas where semantic technologies are used to efficiently deliver smart services. The analysis provided research insights, including the introduction of a new evaluation framework that assesses semantic interoperability solutions on four maturity levels. The framework includes specific evaluation criteria for attributes such as modeling, scalability, and availability. Finally, an elaborated list of strengths, opportunities, weaknesses, and threats of semantic interoperability solutions in smart cities is provided, along with a discussion of open challenges and future work in this domain.

Towards the Correct Interpretation of Real-Time Signals on the Well-Site

Abstract: Drilling operations rely on the collaboration of many participants, and the efficiency of this collaboration depends on timely exchange of information. The complexity and variability of this information make it difficult to achieve interoperability between the involved systems. Recent industry efforts aim at facilitating the many aspects of interoperability. A central element is semantic interoperability: the ability to correctly interpret the real-time signals available on the rig. This contribution presents an implementation of semantic interoperability using OPC UA technology. It translates the principles developed through joint industry efforts into actual drilling operations. The process used the steps of characterizing the drilling real-time data with semantic graphs, and then developing methods to transfer this characterization to an operational real-time environment. A semantic interoperability API (application programming interface) uses the semantic modelling capabilities of OPC UA. Its objectives are to facilitate the acquisition and identification of real-time signals (for data consumers) and their precise description (by data providers). The different components of the API reflect the diversity of scenarios one can expect to encounter on a rig: from WITS-like data streams with minimal semantics to fully characterized signals. The high-level interface makes use of semantical techniques, such as reasoning, to enable advanced features like validation or graph queries. The implementation phase resulted in a series of open-source solutions that cover all the stages of semantic interoperability. The server part integrates real-time sources and exposes their semantics. Data providers can use dedicated applications to accurately describe their own data, while data consumers have access to both predefined mechanisms and to more advanced programming interfaces to identify and interpret the available signals. To facilitate the adoption of this technology, test applications are available that allow interested users to experiment and validate their own interfaces against realistic drilling data. Finally, demonstrations involving several participants took place. The paper discusses both the testing procedures, the results and insights gained. The solutions described in this contribution build on newly developed interoperability strategies: they make on-going industry efforts available to the community via modern technologies, such as OPC UA, semantic modelling, or reasoning. Our hope is that the adoption of the developed technology should greatly facilitate the deployment of next generation drilling automation systems.

Standardizing health care data across an enterprise

Abstract: Semantic interoperability is achieved when two computing systems exchange information and both the sending and receiving system can meaningfully use and similarly understand the meaning or semantics of the information exchanged. A key to achieving semantic interoperability is data standardization, which is the translation, or normalization, of data in which there is a unique representation for each concept present in the data stream. By normalizing data into reference terminologies, data from different sources in the health care ecosystem can be aggregated, analyzed, queried, and reused for a variety of purposes. However, conversion from locally used terminologies into standardized, reference terminologies can be a complex and resource-intensive process. Data standardization therefore requires substantial advance planning, focused implementation, and robust support services, as well as ongoing evaluation and improvement. Terminology Services is a collection of hardware and software components that can be used to facilitate data standardization. This chapter describes the process of and challenges inherent in data standardization, and it provides guidance for the design and implementation of Terminology Services within health information exchange networks.

Realizing health data interoperability in low connectivity settings: The case of VODAN-Africa

Abstract: VODAN Africa has produced FAIR data in low resource settings. Federated machine actionable data is available in a triple store for visiting. Interoperability facets (I1–I3) were followed to achieve semantic interoperability. Vertical interoperability was also realized with DHIS2.

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems

Abstract: This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.

SemBox: Semantic Interoperability in a Box for Wearable e-Health Devices

Abstract: In this work, we propose SemBox - Semantic interoperability in a Box, to enable wireless on-the-go communication between heterogeneous wearable health monitoring devices. It can connect wirelessly to the health monitoring devices and receive their data packets. It uses a Mamdani-based fuzzy inference system with data pre-processing to classify the received data packet into one of the classes of the vital parameters. It enables semantic interoperability by labelling and annotating the data packets based on the extracted packet information. We implement SemBox using three different health monitoring wearables, with different keywords used for each vital parameter representation in the data packet. SemBox shows a maximum classification accuracy of 85.71%, with a maximum PDR of 1 at the SemBox with varying device parameters. Overall, SemBox is a potential plug-and-play solution to achieve semantic interoperability and collaboration between heterogeneous health monitoring wearable devices, irrespective of their commercial and proprietary specifications. It is customizable for applications that use multiple heterogeneous devices for collaborative monitoring and decision support. SemBox enables interoperability among health monitoring devices, introduces flexibility and ease the inter-device dynamics in the domain of biomedical research.

S2NetM: A Semantic Social Network of Things Middleware for Developing Smart and Collaborative IoT-Based Solutions

Abstract: The Social Internet of Things (SIoT) paradigm combines the benefits of social networks with IoT networks to create more collaborative and efficient systems, offering enhanced scalability, better navigability, flexibility, and dynamic decision making. However, SIoT also presents challenges related to dynamic friendship selection, privacy and security, interoperability, and standardization. To fully unlock the potential of SIoT, it is crucial to establish semantic interoperability between the various entities, applications, and networks that comprise the system. This paper introduces the Semantic Social Network of Things Middleware (S2NetM), which leverages social relationships to enhance semantic interoperability in SIoT systems. The S2NetM employs semantic reasoning and alignment techniques to facilitate the creation of dynamic, context-aware social networks of things that can collaboratively work together and enable new opportunities for IoT-based solutions. The main contributions of this paper are the specification of the S2NetM and the associated ontology, as well as the discussion of a case study demonstrating the effectiveness of the proposed solution.

Investigating interoperable event corpora: limitations of reusability of resources and portability of models

Abstract: Abstract Studies on the applicability of heterogeneous semantically interoperable corpora are rare. We investigate to what extent reusability (both of systems and of annotations) is entailed by corpora whose interoperability is based on compliance to standards. In particular, we look at event detection in English texts, supported by the ISO-TimeML annotation scheme. We run two sets of experiments using a common neural network architecture and extensively evaluate our results on both in-distribution and out-of-distribution settings. In all experimental settings, systems obtain state-of-the-art results on the in-distribution data and underperform out-of-distribution ones, setting limits to the benefits of semantically interoperable corpora. By means of a detailed error analysis, we show that while being compliant to a standard guarantees semantic interoperability, this becomes only a necessary condition for reusability, with factors such as differences in the quality of the annotations having a much stronger impact.

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

Abstract: 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.

DynO-IoT: a dynamic ontology for provisioning semantic interoperability in internet of things

Abstract: A wide variety of devices and communication modules are emerging according to the specific requirements of various internet of things (IoT) applications. Provisioning semantic interoperability in this heterogeneous environment is a challenging task. Ontology provides a common platform for all the devices and applications available in a network. However, the existing ontologies are either very complex or unable to serve some specific requirements of IoT applications. Additionally, most of these are semantically static in nature. Addressing these concerns, we propose a semantically dynamic ontology for IoT, DynO-IoT which adapts to the static and dynamic IoT environments very easily. It supports all types of IoT devices and automatically adds new concepts to the ontology using machine learning techniques. The proposed model increases accuracy by 17% and reduces query response time by 35% approximately over the existing static ontologies.

Interoperability of Electronic Health Records for Dyslipidemia using Knowledge graphs

Abstract: Clinical information of a patient is voluminous, varied and critically important for decision making process to the medical experts. The data is complicated and is not organized in a single common platform that leads to complexity. The information is stored in varied frameworks using different formats. The data is structured at varied levels which leads to the difficult management of the information and its interoperability in cases that integrate and reuse the data. This paper presents an alternative to cope with those difficulties using semantic technologies. The data is structured and transformed into information that clinches interoperability and reuse of the information by different platforms/clinicians

Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework

Abstract: Industry 4.0 currently prepares a major shift towards extreme flexibility into production lines management. Digital Twins are one of the key enabling technologies for Industry 4.0. However, the interoperability gap among digital representation of Industry 4.0 assets is still one of the obstacles to the development and adoption of digital twins. If the Asset Administration Shell (AAS), the standard proposed to represent the I4.0 components, caters for syntactic interoperability, a more semantic kind of interoperability is deeply needed to develop flexible and adaptable production lines. In our work, we overcome the limitation of current syntactic-only resource matching algorithms by implementing semantic interoperability based on ontologies i.e., by transforming AAS-based plant models into MaRCO (Manufacturing Resource Capability Ontology) instances and then query the expanded ontology to find the needed resources. This article presents this ontology-based approach as the first step towards the design and implementation of an automated I4.0 flexible plant supervision and control system based on model-driven engineering (MDE) within the “Papyrus for Manufacturing” toolset. We show how an MDE approach can aggregate around digital twin modeling tools from the Papyrus platform both I4.0 technologies and AI (Knowledge Representation and Reasoning) tools. Our platform aligns modeling and ontological elements to get the best of both worlds. This method has two main advantages: (1) to provide semantic descriptions for digital twin models, (2) to complement model-driven engineering tools with automated reasoning. This paper showcases this approach through a robotic cell use case.

Critical infrastructure awareness based on IoT context data

Abstract: The Internet of Things (IoT) represents a powerful new paradigm for connecting and communicating with the world around us. It has the potential to transform the way we live, work, and interact with our surroundings. IoT devices are transmitting information over the Internet, most of them with different data formats, despite they may be communicating similar concepts. This often leads to data incompatibilities and makes it difficult to extract the knowledge underlying that data. Because of the heterogeneity of IoT devices and data, interoperability is a challenge, and efforts are underway to overcome this through research and standardization. While data collection and monitoring in IoT systems are becoming more prevalent, contextualizing the data and taking appropriate actions to address issues in the monitored environment is still an ongoing concern. Context Awareness is a highly relevant topic in IoT, as it aims to provide a deeper understanding of the data collected and enable more informed decision-making. In this paper, we propose a semantic ontology designed to monitor global entities in the IoT. By leveraging semantic definitions, it enables end-users to model the entire process from detection to action, including context-aware rules for taking appropriate actions. The advantages of using semantic definitions include more accurate and consistent data interpretation, which improves the overall monitoring process and enables more effective decision-making based on the collected insights. Our proposal includes semantic models for defining the entities responsible for monitoring and executing actions, as well as the elements that need to be considered for an effective monitoring process. Additionally, we provide a new definition for the components known as gateways, which enable the connection and communication between devices and the Internet. Finally, we show the benefits of our ontology by applying it to a critical infrastructure domain where a rapid response is vital to prevent accidents and malfunction of the entities.

Achieving Interoperable Datasets in Pediatrics: A Data Integration Approach

Abstract: Despite their increased secondary value for developing applications and knowledge gain, routine, harmonized and standardized datasets are often not available in Pediatrics. We propose a data integration pipeline towards an interoperable routine dataset in pediatric intensive care medicine. Our three-level approach involves identifying relevant data from primary source systems, developing local data integration processes, and converting data into a standardized, interoperable format using openEHR. We modeled 15 openEHR templates and established 31 interoperable ETL processes, resulting in anonymized, standardized data of about 4,200 pediatric patients that were loaded into a harmonized database. Based on our pipeline and templates, we successfully integrated the first part of this data in our openEHR data repository. We seek to inspire other pediatric intensive care units to adopt similar approaches, with the aim of breaking down heterogenous data silos and promoting secondary use of routine data.

Evaluation of Semantic Interoperability of Automotive Service API Models Based on Metamodels Similarity Metrics Using a Semi-automated Approach

Abstract: In recent years, mapping of application software components’ ontologies semantically emerged as a big research challenge in automotive application domain that manipulates several cross-enterprise synergy knowledge application frameworks. The same knowledge formalized by different experts in different vehicle application frameworks leads to heterogeneous representations of components’ interface data. Consequently, this causes the most daunting impediment in semantic interoperability between the service components in cooperative automotive systems. From a modeling perspective, in the absence of standardized domain-based unified modeling techniques, the orchestration and resolution of semantic data interoperability between various vehicle application frameworks’ components’ interface models remain a challenge. However, this challenge could be addressed using ontological metamodeling by specifying semantic associations between components’ interface model concepts based on the domain knowledge. Apart from the semantic mapping of interface ontological metamodels, this work also defines quality metrics to determine the degree of semantic alignment achieved between the various interface ontologies. Additionally, to reduce development time and cost towards semantic interoperability, this work proposes a semi-automated plugin tool for the applicability of the evaluated quality metrics to semantic mapping of real-world components’ interface models.

An Annotation Workbench for Semantic Annotation of Data Collection Instruments

Abstract: Semantic interoperability, i.e., the ability to automatically interpret the shared information in a meaningful way, is one of the most important requirements for data analysis of different sources. In the area of clinical and epidemiological studies, the target of the National Research Data Infrastructure for Personal Health Data (NFDI4Health), interoperability of data collection instruments such as case report forms (CRFs), data dictionaries and questionnaires is critical. Retrospective integration of semantic codes into study metadata at item-level is important, as ongoing or completed studies contain valuable information, which should be preserved. We present a first version of a Metadata Annotation Workbench to support annotators in dealing with a variety of complex terminologies and ontologies. User-driven development with users from the fields of nutritional epidemiology and chronic diseases ensured that the service fulfills the basic requirements for a semantic metadata annotation software for these NFDI4Health use cases. The web application can be accessed using a web browser and the source code of the software is available with an open-source MIT license.

Barriers, Priorities and Lessons Learned in Achieving Electronic Health Records Interoperability in Low- and Middle-Income Countries: Workshop Findings

Abstract: Electronic health records (EHR) interoperability is a complex topic that continues to gain traction in the digital health landscape. We facilitated a qualitative workshop consisting of domain experts in EHR implementation and health IT managers. The workshop aimed to identify critical barriers to achieving interoperability, priorities for new EHR implementations and lessons learned from managing existing implementations. The workshop highlighted that data modelling and interoperability standards are vital priorities for maternal and child health data services in low- and middle-income countries (LMICs).

Technological Perspective: Architecture, Integration, and Standards

Abstract: Abstract Health information systems can be described at three layers: The domain layer describing entity types and functions, the logical tool layer describing application components, and the physical tool layer. Data can be classified into personal and non-personal data and into standardized and non-standardized data. The architectures of an information system can be characterized by the number of databases, the number of application systems, the number of application software products and vendors, and the communication pattern. Technical interoperability describes the ability of application systems to send or receive data. Syntactic interoperability comprises the ability to use predefined message structures. Semantic interoperability means the ability to exchange and process meaningful messages. Process interoperability addresses whether application systems can cooperate. Interoperability standards support one or more aspects of interoperability. Integrating application systems leads to integrated health information systems. Data integration is achieved when data that have been recorded once in one application system are made available in other application systems. Semantic integration is achieved when application systems actually use the same system of concepts. User interface integration is guaranteed when different application systems organize their user interfaces in a unified way. Context integration is achieved when context is preserved when switching application systems. Feature integration is achieved when software features are implemented only once. Process integration is guaranteed when business processes are supported by cooperating application systems. Several integration technologies such as transaction management, communication servers, and open platforms support integrity and integration in heterogeneous health information systems.