Showing papers on "Semantic interoperability published in 2019"

Toward Global IoT-Enabled Smart Cities Interworking Using Adaptive Semantic Adapter

Abstract: Since the Internet-of-Things (IoT) has been introduced, it is considered as one of the emerging technologies providing great opportunities to many vertical industries. One of the major IoT application areas that gets significant attention is smart city. Since it is unrealistic to expect full convergence toward a single IoT platform in the near future, it is mandatory to enable interworking between different platforms based on multiple standards, coexisting in the emerging smart cities. In this paper, we take the example of two global IoT standards, FIWARE and oneM2M, which are actively used in many smart city projects, and analyze them to show the feasibility of IoT platforms interworking. Based on the analysis, we design and implement a novel IoT interworking architecture providing a semantic driven integration framework suitable for smart city. The core idea behind our approach is to introduce interworking proxies that: 1) conduct a static mapping of sensor information between IoT platforms and 2) perform semantic interoperability using semantically annotated resources via a semantic interworking proxy that dynamically discovers new kinds of information and adapts itself to enable automatic translation of semantic data between given source and target IoT platforms while it is running. We present the system based on these proxies and evaluate it in Santander smart city. The results demonstrate that it is able to discover and manage IoT sensors connected to both oneM2M and FIWARE. It appears that the semantic approach provides the flexibility and dynamic adaptivity needed for fast growing and rapidly changing urban environments.

IoMT Platform for Pervasive Healthcare Data Aggregation, Processing, and Sharing Based on OneM2M and OpenEHR.

Abstract: Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

Abstract: Mobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs. This paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology. This realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients’ wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at . The resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies.

Toward a Model for Personal Health Record Interoperability

Abstract: Health information technology, applied to electronic health record (EHR), has evolved with the adoption of standards for defining patient health records. However, there are many standards for defining such data, hindering communication between different healthcare providers. Even with adopted standards, patients often need to repeatedly provide their health information when they are taken care of at different locations. This problem hinders the adoption of personal health record (PHR), with the patients’ health records under their own control. Therefore, the purpose of this paper is to propose an interoperability model for PHR use. The methodology consisted prototyping an application model named OmniPHR, to evaluate the structuring of semantic interoperability and integration of different health standards, using a real database from anonymized patients. We evaluated health data from a hospital database with 38 645 adult patients’ medical records processed using different standards, represented by open EHR, HL7 FHIR, and MIMIC-III reference models. OmniPHR demonstrated the feasibility to provide interoperability through a standard ontology and artificial intelligence with natural language processing (NLP). Although the first executions reached a 76.39% F1-score and required retraining of the machine-learning process, the final score was 87.9%, presenting a way to obtain the original data from different standards on a single format. Unlike other models, OmniPHR presents a unified, structural semantic and up-to-date vision of PHR for patients and healthcare providers. The results were promising and demonstrated the possibility of subsidizing the creation of inferences rules about possible patient health problems or preventing future problems.

Standards in Healthcare Data

Abstract: Clinical data interoperability requires shared specifications of meaning. This is the rationale for clinical data standards. Up until now, the adoption of such standards has been varied, although they are increasingly advocated in an area where proprietary specifications prevail, and semantic resources are geared to specific purposes and limited by boundaries of languages and jurisdictions. This chapter highlights the need of data standards in the context of the difficult and heterogeneous field of clinical data and the way how they are addressed by terminologies, ontologies and information models. It provides an overview of existing standards and discusses quality and implementation issues. Emphasis is also put on the eStandards methodology, which investigates needs for health data standards, supports the creation of standardised artefacts and defines actions for the implementation of standards.

Automatic Knowledge Extraction to Build Semantic Web of Things Applications

Abstract: The Internet of Things (IoT) primary objective is to make a hyper-connected world for various application domains. However, IoT suffers from a lack of interoperability leading to a substantial threat to the predicted economic value. Schema.org provides semantic interoperability to structure heterogeneous data on the Web. An extension of this vocabulary for the IoT domain (iot.schema.org) is an ongoing research effort to address semantic interoperability for the Web of Things (WoT). To design this vocabulary, a central challenge is to identify the main topics (concepts and properties) automatically from existing knowledge in IoT applications. We designed knowledge extraction for the WoT (KE4WoT) to automatically identify the most important topics from literature ontologies of three different IoT application domains: 1) smart home; 2) smart city; and 3) smart weather—based on our corpus consisting of 4500 full-text conference and journal articles to utilize domain-specific knowledge encoded within IoT publications. Despite the importance of automatically identifying the relevant topics for iot.schema.org, up to know there is no study dealing with this issue. To evaluate the extracted topics, we compare the descriptiveness of these topics for the ten most popular ontologies in the three domains with empirical evaluations of 23 domain experts. The results illustrate that the identified main topics of IoT ontologies can be used to sufficiently describe existing ontologies as keywords.

Breaking Vendors and City Locks through a Semantic-enabled Global Interoperable Internet-of-Things System: A Smart Parking Case

Abstract: The Internet of Things (IoT) is unanimously identified as one of the main technology enablers for the development of future intelligent environments. However, the current IoT landscape is suffering from large fragmentation with many platforms and vendors competing with their own solution. This fragmented scenario is now jeopardizing the uptake of the IoT, as investments are not carried out partly because of the fear of being captured in lock-in situations. To overcome these fears, interoperability solutions are being put forward in order to guarantee that the deployed IoT infrastructure, independently of its manufacturer and/or platform, can exchange information, data and knowledge in a meaningful way. This paper presents a Global IoT Services (GIoTS) use case demonstrating how semantic interoperability among five different smart city IoT deployments can be leveraged to develop a smart urban mobility service. The application that has been developed seamlessly consumes data from them for providing parking guidance and mobility suggestions at the five locations (Santander and Barcelona in Spain and Busan, Seoul and Seongnam in South Korea) where the abovementioned IoT deployments are installed. The paper is also presenting the key aspects of the system enabling the interoperability among the three underlying heterogeneous IoT platforms.

An Ontology and Semantic Web Service for Quantum Chemistry Calculations.

Abstract: The purpose of this article is to present an ontology, termed OntoCompChem, for quantum chemistry calculations as performed by the Gaussian quantum chemistry software, as well as a semantic web service named MolHub. The OntoCompChem ontology has been developed based on the semantics of concepts specified in the CompChem convention of Chemical Markup Language (CML) and by extending the Gainesville Core (GNVC) ontology. MolHub is developed in order to establish semantic interoperability between different tools used in quantum chemistry and thermochemistry calculations, and as such is integrated into the J-Park Simulator (JPS)-a multidomain interactive simulation platform and expert system. It uses the OntoCompChem ontology and implements a formal language based on propositional logic as a part of its query engine, which verifies satisfiability through reasoning. This paper also presents a NASA polynomial use-case scenario to demonstrate semantic interoperability between Gaussian and a tool for thermodynamic data calculations within MolHub.

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support.

Abstract: This paper is an extension of work originally presented to pHealth 2019-16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.

A Survey on Semantic Approaches for IoT Data Integration in Smart Cities

Abstract: Semantic interoperability is used to exchange information from one place to another place in a meaningful way. The data is generated from various heterogeneous devices, communication protocols, and data formats that are enormous in nature. This is a significant problem for the Internet of Things (IoT) application developers to make the IoT sensors and protocols generated data interoperable. In this paper, firstly, presented the semantic interoperable technologies in smart city applications to achieve IoT semantic data integration in a comprehensive survey manner. Secondly, apply the semantic web of things standards to IoT-based smart city applications and highlight with main challenges and current research directions in IoT semantic interoperability.

Abstracting Interactions with IoT Devices Towards a Semantic Vision of Smart Spaces

Abstract: This paper describes a middleware framework for IoT smart spaces, SemIoTic, that provides application developers and end-users with the semantic domain-relevant view of the smart space, hiding the complexity of having to deal with/understand lower-level information generated by sensors and actuators. SemIoTic uses a meta-model, based on the popular SOSA/SSN ontology with some extensions, to represent relationships between the low-level IoT devices' world (i.e., devices, observations) and semantic concepts (i.e., users and spaces and their observable attributes). It supports a language using which users can express their action requirements (i.e., requests for sensor data, commands for actuators, and privacy preferences) in terms of user-friendly high-level concepts. We present an ontology-based algorithmic approach to translate user-defined actions into sensor/actuators commands. Finally, our end-to-end approach includes a cross-layer solution to provide interoperability with diverse IoT devices and their data exchange protocols.

Linked Vocabulary Recommendation Tools for Internet of Things: A Survey

Abstract: The Semantic Web emerged with the vision of eased integration of heterogeneous, distributed data on the Web. The approach fundamentally relies on the linkage between and reuse of previously published vocabularies to facilitate semantic interoperability. In recent years, the Semantic Web has been perceived as a potential enabling technology to overcome interoperability issues in the Internet of Things (IoT), especially for service discovery and composition. Despite the importance of making vocabulary terms discoverable and selecting the most suitable ones in forthcoming IoT applications, no state-of-the-art survey of tools achieving such recommendation tasks exists to date. This survey covers this gap by specifying an extensive evaluation framework and assessing linked vocabulary recommendation tools. Furthermore, we discuss challenges and opportunities of vocabulary recommendation and related tools in the context of emerging IoT ecosystems. Overall, 40 recommendation tools for linked vocabularies were evaluated, both empirically and experimentally. Some of the key findings include that (i) many tools neglect to thoroughly address both the curation of a vocabulary collection and effective selection mechanisms, (ii) modern information retrieval techniques are underrepresented, and (iii) the reviewed tools that emerged from Semantic Web use cases are not yet sufficiently extended to fit today’s IoT projects.

Towards Next Generation Knowledge Graphs for Disaster Management

Abstract: Timely access to information of critical resources is the fundamental requirement for the disaster management domain. Existing information systems to support the disaster management services lack interoperability and have several challenges to efficiently access heterogeneous information, especially open data. Such challenges include heterogeneity of data sources and formats (e.g., a hospital resource entity in Data. Gov versus OpenStreetMap), inconsistency of vocabularies (e.g., an entity type in Data.Gov schema versus OpenStreetMap schema), and incompleteness of information in a single source (e.g., lack of all relevant metadata for a hospital resource entity in Data. Gov). Knowledge graphs facilitate in addressing these challenges. They enable semantic computing applications to timely access relevant information about a critical resource across diverse data sources with both human and machine-understandable interpretations. In this paper, we will illustrate the design of a Disaster Knowledge Graph (DisasterKG) based system for querying critical resources and showcase how such interoperable information access could improve decision making throughout disaster management for preparedness, response, and recovery. On the flip side, our experience shows that such an approach may not be efficient by itself and cost-effective as still the considerable human effort is required for semantic information integration. Towards addressing this, we consider the Deep Learning based KG embedding approach as a potential alternative and argue for combining this approach with the traditional KG construction approach in complementary ways to get the best of both worlds.

Semantic Interoperability for IoT Platforms in Support of Decision Making: An Experiment on Early Wildfire Detection

Abstract: One of the main obstacles towards the promotion of IoT adoption and innovation is data interoperability. Facilitating cross-domain interoperability is expected to be the core element for the realisation of the next generation of the IoT computing paradigm that is already taking shape under the name of Internet of Everything (IoE). In this article, an analysis of the current status on IoT semantic interoperability is presented that leads to the identification of a set of generic requirements that act as fundamental design principles for the specification of interoperability enabling solutions. In addition, an extension of NGSIv2 data model and API (de-facto) standards is proposed aiming to bridge the gap among IoT and social media and hence to integrate user communities with cyber-physical systems. These specifications have been utilised for the implementation of the IoT2Edge interoperability enabling mechanism which is evaluated within the context of a catastrophic wildfire incident that took place in Greece on July 2018. Weather data, social media activity, video recordings from the fire, sensor measurements and satellite data, linked to the location and the time of this fire incident have been collected, modeled in a uniform manner and fed to an early fire detection decision support system. The findings of the experiment certify that achieving minimum data interoperability with light-weight, plug-n-play mechanisms can be realised with significant benefits for our society.

Establishing common ground through INSPIRE: the legally-driven European Spatial Data Infrastructure

Abstract: Back in the 1990s, there were several barriers for accessing and using the spatial data and information necessary for environmental management and policy making in Europe. These included different data policies, encodings, formats and semantics, to name a few. Data was collected for, and applied to, domain specific use cases and comprehensive standards did not exist, all impacting on the re-usability of such public sector data. To release the potential of spatial data held by public authorities and improve evidence-based environmental policy making, action was needed at all levels (Local, Regional, National, European) to introduce more effective data and information management and to make data available for citizens’ interest. The INSPIRE Directive, the Infrastructure for Spatial Information in Europe, directly addresses this set of problems. The Directive came into force on 15 May 2007, with full implementation in every EU Member State required by 2021. It combines both, a legal and a technical framework for the EU Member States, to make relevant spatial data accessible and reused. Specifically, this has meant making data discoverable and interoperable through a common set of standards, data models and Internet services. The Directive’s data scope covers 34 themes of cross-sector relevance as a decentralised infrastructure where data remains at the place it can be best maintained. A great deal of experience has been gained by public administrations through its implementation. Due to its complexity and wide scope, this is taking place in a stepwise manner, with benefits already emerging as important deadlines approached. Efficient and effective coordination are following the participatory approach established in its design. It is timely to reflect on 10 years of progress of the “cultural change” which the European Spatial Data Infrastructure represents. We therefore, consider the lessons INSPIRE is offering for those interested in joined-up and federated approaches to geospatial data-sharing and semantic interoperability across borders and sectors. The approach itself is evolving through this experience.

IoT Semantic Interoperability: A Systematic Mapping Study.

Abstract: The Internet of Things (IoT) is a paradigm in which the Internet connects people and the environment using devices and services that are spread in the user daily routine. In this scenario, different agents, devices and services are able to exchange data and knowledge using a common vocabulary or mappings that represent and integrate heterogeneous sources. This semantic interoperability is facilitated by the Semantic Web that provides consolidated technologies, languages and standards, offering data and platforms interoperability. In this context, this work reviews and analyzes the state-of-the-art of IoT semantic interoperability, investigating and presenting not only which Semantic Web technologies are employed but also the challenges that support the studies in this area of research.

An ontology-based healthcare monitoring system in the Internet of Things

Abstract: Continuous health monitoring is a hopeful solution that can efficiently provide health-related services to elderly people suffering from chronic diseases. The emergence of the Internet of Things (IoT) technologies have led to their adoption in the development of new healthcare systems for efficient healthcare monitoring, diagnosis and treatment. This paper presents a healthcare-IoT based system where an ontology is proposed to provide semantic interoperability among heterogeneous devices and users in healthcare domain. Our work consists on integrating existing ontologies related to health, IoT domain and time, instantiating classes, and establishing reasoning rules. The model created has been validated by semantic querying. The results show the feasibility and efficiency of the proposed ontology and its capability to grow into a more understanding and specialized ontology for health monitoring and treatment.

Toward the Use of Upper-Level Ontologies for Semantically Interoperable Systems: An Emergency Management Use Case

Abstract: In the context of globalization and knowledge management, information technologies require an ample need of unprecedented levels of data exchange and sharing to allow collaboration between heterogeneous systems. Yet, understanding the semantics of the exchanged data is one of the major challenges. Semantic interoperability can be ensured by capturing knowledge from diverse sources by using ontologies and align these latter by using upper-level ontologies to come up with a common shared vocabulary. In this paper, we aim in one hand to investigate the role of upper-level ontologies as a mean for enabling the formalization and integration of heterogeneous sources of information and how it may support interoperability of systems. On the other hand, we present several upper-level ontologies and how we chose and then used basic formal ontology (BFO) as an upper-level ontology and common core ontology (CCO) as a mid-level ontology to develop a modular ontology that defines emergency responders’ knowledge starting from firefighters’ module for a solution to the semantic interoperability problem in emergency management.

A study of semantic integration across archaeological data and reports in different languages

Abstract: This study investigates the semantic integration of data extracted from archaeological datasets with information extracted via natural language processing (NLP) across different languages. The investigation follows a broad theme relating to wooden objects and their dating via dendrochronological techniques, including types of wooden material, samples taken and wooden objects including shipwrecks. The outcomes are an integrated RDF dataset coupled with an associated interactive research demonstrator query builder application. The semantic framework combines the CIDOC Conceptual Reference Model (CRM) with the Getty Art and Architecture Thesaurus (AAT). The NLP, data cleansing and integration methods are described in detail together with illustrative scenarios from the web application Demonstrator. Reflections and recommendations from the study are discussed. The Demonstrator is a novel SPARQL web application, with CRM/AAT-based data integration. Functionality includes the combination of free text and semant...

Semantic Interoperability Methods for Smart Service Systems: A Survey

Abstract: Functional and nonfunctional characteristics of software systems are defined by their architecture. Therefore, research streams such as Internet-of-Things (IoT) or component-based software engineering provide researchers and practitioners with construction guidelines for selected architectural characteristics. Current systems can be categorized in delivering services to the user and being engineered in a smart way. For example, services being provided by IoT-Systems must fulfill users’ goals in a highly dynamic and ad-hoc way. Consequently, this survey aims at answering various research questions regarding the methodical composition of system components and services. Furthermore, new research opportunities are sketched that should be tackled to make the scientific progress available to practitioners. Based on a systematic literature review from a software architecture point of view, in this paper we identify 75 primary studies for domain-specific IoT component composition approaches and architectures. Initial results show that current integration approaches mainly focus on performance evaluation of their integration solutions, which may be too narrow for fulfilling user goals by utilizing of IoT architectures.

Ontology-based electronic health record semantic interoperability: A survey

Abstract: EHR semantic interoperability is urgently needed for systems to improve healthcare quality. However, its achievement has many challenges and barriers. The primary purpose of this chapter is to attract attention to the importance of semantic interoperability in the healthcare industry. We intensely surveyed the current literature for aspects of semantic interoperability, including its main definitions, standards, schemas, models, terminologies, barriers, and future challenges. We depended on the existing databases, including ScienceDirect, IEEE Xplore, PubMed, ELSEVIER, MEDLINE, Cochranelibrary, Informit, and Springer. The results include a comprehensive survey of healthcare semantic interoperability. We noted that the most intuitive EHR semantic interoperability approach is based on standards, but they have some challenges. The medical domain is characterized by its vagueness and uncertainty. All parties in the healthcare domain use imprecise concepts to describe their ideas. As a result, we recommend fuzzy ontology to achieve the target goal of global interoperability.

The symbIoTe Solution for Semantic and Syntactic Interoperability of Cloud-based IoT Platforms

Abstract: The current IoT landscape is dominated by cloud-based platforms offering non-standardized interfaces to access virtualized IoT resources and adopting proprietary information models. The implementation of cross-platform and cross-domain IoT applications becomes cumbersome and usually leads to custom solutions, tailored to the involved platforms, due to the semantic and syntactic incompatibilities. The symbIoTe approach offers mediation services for search and controlled access to IoT resources (sensors, actuators, and related services) across platforms in a uniform way. It provides an IoT Portal with registration and search capabilities using semantic web technologies for semantic interoperability, and an abstraction layer for unified and secure access to those resources across distributed IoT platform instances for syntactic interoperability. In this paper, we present the general concepts and design decisions built into the symbIoTe open source middleware and showcase the evolving symbIoTe ecosystem which facilitates the rapid development of innovative cross-platform IoT applications. The open IoT Portal currently integrates 15 IoT platforms and data sources for Smart City and Smart Residence domains, and hosts metadata registering more than 4,000 various IoT resources.