Problem Of Semantic Interoperability Research Articles

A Semantic Gateway for Internet of Things Interoperability at the Application Layer

Abstract Due to the rapid growth of the Internet of Things (IoT), researchers have demonstrated various IoT solutions, which are used to interconnect a wide range of IoT devices through the Internet. However, IoT stumbled into vertical silos; the available solutions provide specific IoT infrastructure, devices, protocols, data formats and models. This diversity and heterogeneity lead to interoperability issues. Heterogeneity happens at all IoT layers, especially at the application layer; devices often adopt mutually incompatible application-layer communication protocols to connect devices to IoT services. Furthermore, in order to integrate semantics to raw data, each system uses its one domain-specific ontology to make data more understandable and interpretable by adding semantic annotations. Working in isolation reduces the interoperability among IoT devices and systems, things across domains need to internetwork and collaborate to provide high level IoT services. Therefore, to alleviate the problem of both communication protocol interoperability and semantic interoperability across vertical silos of systems at the application layer, this paper proposes a semantic gateway (SGIoT) that acts as a bridge between heterogeneous sink nodes at the physical level and IoT services. SGIoT enables interconnectivity between communication protocols such as CoAP and MQTT regardless of their communication model, meanwhile it enables semantics integration throu gh cross-domain ontology (CDOnto) for semantic annotation, in order to provide interpretation of messages among IoT applications across domains. Our approach focuses on modularity and extensibility.

Ontological Approach to Support the Horizontal and Vertical Information Integration in Smart Manufacturing Systems: An Experimental Case in a Long-Life Packaging Factory

Digital manufacturing has been challenged by the manufacturing industry to rationalize different ways to connect and exchange information and knowledge across different phases of manufacturing systems. One of the Industry 4.0 pillars is the horizontal and vertical integration with intelligent and self-adaptive systems. For this to be possible, the manufacturing industry applies an extensive range of software tools, such as GRAI, CIMOSA, MO2GO, ARIS, SCADA, MES, ERP, CAD, and CAM. Individually, each one performs its function to support the manufacturing process. However, when these multiple tools operate together using technical standards, some misinterpretation and mistake gaps are identified due to a lack of machine-to-machine (M2M) communication and users’ interpretation. This is recognized as a semantic interoperability problem. Semantic technologies, such as ontologies, have been proven to be a promising way to overcome semantic interoperability obstacles. Based on this context, this study is proposing a conceptual framework based on semantic technologies to create a solution to the horizontal and vertical integration and semantic interoperability obstacle. MANUMATE is the framework proposed, and it consists of three artifacts, 1) reference ontologies, 2) requirements, and 3) application ontology, and two processes, 1) ontology specialization and 2) information application. The MANUMATE framework is applied to two experimental case studies to validate the conceptual solution in two different applications, in the context of a long-life package for the beverages industry. These case studies help elucidate how the application of the framework could improve the information and knowledge exchange by providing a standard way to represent information among different stakeholders in the productive process. A discussion about the results is presented, revealing the benefits and limitations of the solution.

Achieving Semantic Consistency for Multilingual Sentence Representation Using an Explainable Machine Natural Language Parser (MParser)

In multilingual semantic representation, the interaction between humans and computers faces the challenge of understanding meaning or semantics, which causes ambiguity and inconsistency in heterogeneous information. This paper proposes a Machine Natural Language Parser (MParser) to address the semantic interoperability problem between users and computers. By leveraging a semantic input method for sharing common atomic concepts, MParser represents any simple English sentence as a bag of unique and universal concepts via case grammar of an explainable machine natural language. In addition, it provides a human and computer-readable and -understandable interaction concept to resolve the semantic shift problems and guarantees consistent information understanding among heterogeneous sentence-level contexts. To evaluate the annotator agreement of MParser outputs that generates a list of English sentences under a common multilingual word sense, three expert participants manually and semantically annotated 75 sentences (505 words in total) in English. In addition, 154 non-expert participants evaluated the sentences’ semantic expressiveness. The evaluation results demonstrate that the proposed MParser shows higher compatibility with human intuitions.

Industrial ontologies for interoperability in agile and resilient manufacturing

Ontologies provide an opportunity to tackle the interoperability challenge in digital manufacturing. Although ontologies have been used in numerous industrial projects, the problem of semantic interoperability in the industry is still unresolved. One reason for the lack of success of ontologies in the industry is that most industrial ontologies have been developed based on local needs without taking wider applications into account or attempting to learn from earlier ontology initiatives or reuse the existing ontologies. In this position paper, we argue that for widespread adoption and implementation of ontologies, more systematic and coordinated efforts are needed with the participation of industry, academia, and government at an international level. We also hypothesise that a coordinated and systematic ontology development effort will incrementally lead to the creation of an ecosystem of interoperable software applications that support consistent data access and reasoning across the product life cycle. We discuss why efforts such as Industrial Ontology Foundry (IOF) can promote a methodological ontology development process by introducing modular, and reusable ontologies. We draw examples from two domain areas, namely supply chain management and production planning and scheduling, to demonstrate how industrial notions can be analysed ontologically according to IOF procedures and methods.

Monitoring of Unaccounted for Gas in Energy Domain Using Semantic Web Technologies

Smart Urbanization has increased tremendously over the last few years, and this has exacerbated problems in all areas of life, especially in the energy sector. The Internet of Things (IoT) is providing effective solutions in gas distribution, transmission and billing through very sophisticated sensory devices and software. Billions of heterogeneous devices link to each other in smart urbanization, and this has led to the Semantic interoperability (SI) problem between the connected devices. In the energy field, such as electricity and gas, several devices are interlinked. These devices are competent for their specific operational role but unable to communicate across the operational units as required for accounting and monitoring of gas losses due to heterogeneity in device communication standards. To overcome this problem, we have proposed a model and ontology by applying semantic web technologies and cloud storage to address the tracking of customers to observe Unaccounted for gas (UFG) in the gas domain of energy. Semantization is achieved by replicating heterogeneous devices Sensor Model Language (SenML) data into Resource description framework (RDF) without human interventions. As semantic interoperability is used to efficiently and meaningfully share the information from one location to another. Therefore, the proposed ontology and model focus more efficiently on customer tracking, forecasting, and monitoring to detect UFG in gas networks. This also helps to save Gas Companies from financial gas losses.

A Novel Deep Learning Approach for Semantic Interoperability Between Heteregeneous Multi-Agent Systems

This article focuses on the issue of semantic interoperability in heterogeneous distributed multi-agent systems. Existing middleware technologies offer programming models that strongly combine agents’ learning models and communication models, which can lead to performance weaknesses when the number of agents is very important. Moreover, existing methods in the field of semantic interoperability solve the problem of understanding messages exchanged between distributed agents with heterogeneous ontologies, using several techniques to combine these ontologies. The first category of these methods relies on the fusion principle, others use alignment, and finally, there are those founded on Semantic Web technique. All these methods are limited to abstract concepts and do not deal with concrete concepts such as those represented by images. We propose in this paper a new approach that addresses the problem of semantic interoperability between heterogeneous distributed agents based on two principles: At first, the communication aspect of the agent from the learning aspect is separated. Then, we propose extending semantic interoperability to concrete concepts by combining two techniques: Semantic Web technology, which allows terms representing abstract concepts to be interpreted and deep learning technology, which is introduced as a new method to ensure semantic interoperability in the case of concrete concepts such as images. A detailed description of the proposed approach is provided, showing that it is very useful in solving the disadvantages of existing multi-agent platforms.

Ontologias para organização da informação em processos de transformação digital

Atualmente, a aplicação das tecnologias digitais, pela sociedade contemporânea e nas organizações, tornou-se tão frequente e ampliada que é comum caracterizá-la como o processo de mudança conhecido como transformação digital. O objetivo deste artigo é discutir os conceitos e processos da transformação digital a serem utilizados como estratégias nas organizações, dando um enfoque principal ao uso e aplicação de ontologias, nesse contexto, e aos desafios atuais de aplicação. Nesta pesquisa, as ontologias são tratadas como instrumentos para representação e organização do conhecimento em diferentes domínios, no âmbito da Ciência da Informação. A metodologia de pesquisa utilizada foi exploratória com abordagem qualitativa, na qual se realizou o estudo da literatura científica, a seleção do estado da arte e a análise de caráter subjetivo. Os resultados indicaram que ontologias possibilitam representar as informações no processo de transição entre o projeto e o desenvolvimento do produto, atuando como a solução de problemas de interoperabilidade semântica. Ontologias são úteis, também, no processo de construção da nuvem de serviços de uma empresa, ao representarem conceitos básicos, terminologia e semântica de uma arquitetura orientada a serviços. Ao final, concluiu-se que ontologias são úteis nos processos de transformação digital e de desenvolvimento de produtos das empresas, pois tornam mais explícito o conhecimento adquirido de um certo domínio, possibilitam uma compreensão mais clara dos conceitos arquitetônicos integrados e fornecem mecanismos adicionais de inferência do conhecimento.

Natural language processing driven ontology modelling to address semantic interoperability in information extraction

The aim of this research is to investigate the use of natural language processing techniques for ontology modelling that would address semantic interoperability problems arising due to terminological and semantic differences during information extraction from documents. These documents contain terminologies that are specific to the group and experts involved in creating them. As manual extraction is a tedious process, natural language processing techniques are suggested in this work. Text from different documents describing new terms are taken as input. These texts are processed to establish either semantic equivalence of the new term with concepts in reference ontology or add the term as a new concept if appropriate. Semantic role labelling is used for concept modelling and semantic role labelling tool is used in deriving the concept instances from the text, which are matched against concepts in the reference ontology to establish equivalence. If instances of attributes are equivalent, then concepts are said to be equivalent and if equivalence is limited to attributes, then ontology is populated with new concept. Results on technical text are presented with discussion on how the limitations observed can be removed.

Distributed electronic health record based on semantic interoperability using fuzzy ontology: a survey

Over the past years, the knowledge and data are rising rapidly with the development of communication technology. There is a growing demand for integrating data, especially the medical healthcare data. Electronic Health Record (EHR) provides the patient with secure, real-time, and reliable access to the health record information at any time where any location is needed. Semantic interoperability plays an essential role in improving the medical decision-making, lowering the costs of healthcare, and improving the healthcare quality. Semantic concerns on the study of meanings and interoperability refer to getting systems to work together. There are many models try to solve the EHR semantic interoperability problem. Many different e-health standards are proposed, but they had some problems. Some of these standards do not support full semantic interoperability, whereas some others are poor community support. This paper focuses on the following goals: (1) realizing the urgent need for interoperability in EHR to improve healthcare quality; (2) discussing some of its main problems; (3) surveying some of the existing standards for solving its problems; (4) discussing some key issues required during achieving it; and (5) trying to solve that problem by recommending fuzzy ontology as an intelligent information system solution.

Efficient Results in Semantic Interoperability for Health Care. Findings from the Section on Knowledge Representation and Management.

To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. The selection and evaluation process of this Yearbook's section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowdbased method for ontology engineering. The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

Alignment Methodological Approach of Evolving Domain Sub-Ontologies using Terminological and Structural Matchers Applied to Tourism Domain

The use of ontologies to resolve problems of semantic interoperability has become a considerable challenge. To pool distributed and shared knowledge, the handling of ontologies requires defining “set of matches” to align them. This alignment process of ontologies is deemed to be complex as it is based on the measure analysis of similarity (Matchers). We suggest in this paper the classification of different alignment techniques combining at the same time several methods to generate semi-automatically Mapping. Opposite to these techniques, we present helping methodological approach of sub-ontologies alignment. We propose a realistic approach adapted to tourism domain which covers many sub-domains.

Governance of Online Intermediaries: Observations from a Series of National Case Studies

Online intermediaries in various forms – including search engines, social media, or app platforms – play a constitutive role in today’s digital environment. They have become a new type of powerful institution in the 21st century that shape the public networked sphere, and are subject to intense and often controversial policy debates. This paper focuses on one particular force shaping the emergence and future evolution of online intermediaries: the rapidly changing landscape of intermediary governance at the intersection of law, technology, norms, and markets. Building upon eight in-depth case studies and use cases, respectively, this paper seeks to distill key observations and provide a high-level analysis of some of the structural elements that characterize varying governance regimes, with a focus on intermediary liability regimes and their evolution.Analyzing online intermediary governance issues from multiple perspectives, and in the context of different cultures and regulatory frameworks, immediately creates basic problems of semantic interoperability. Lacking a universally agreed-upon definition, the synthesis paper and its’ underlying case studies are based on a broad and phenomenon-oriented notion of online intermediaries, as further described below. In methodological terms, the observations shared in the synthesis paper offer a selective reading and interpretation by the authors of the broader take-ways of a diverse set of case studies examining online intermediary governance frameworks and issues in Brazil, the European Union, India, South Korea, the United States, Thailand, Turkey, and Vietnam. These case studies, in turn, have emerged in the context of an international research pilot by the Global Network of Internet & Society Research Centers (NoC), through a process of in-person consultations and remote collaborations among the researchers, and are based on a set of broader questions regarding the role of online intermediaries in the digital age.

SEMANTIC CONFLICTS DETECTION OF HETEROGENEOUS MESSAGES OF WEB SERVICES: CHALLENGES AND SOLUTION

Service Oriented Architecture (SOA) is a new design style that has promised to alleviate the semantic interoperability problem between Web services. Exchanging the messages seamlessly between heterogeneous Web services is required but, it is still difficult due to data heterogeneity. As a result, a noticeable number of works has been proposed with the aim of solving this problem, but yet, the problem has not been solved efficiently. Furthermore, it is important to observe the lack of sufficient approaches in the state of the art, whose purposes are semantic conflicts detection between heterogeneous messages of Web services. It is for these reasons we take a step back to the detection step before providing the solution. This study proposes a new ontology-based approach that aim at detecting semantic conflicts between heterogeneous messages of Web services. The main purposes of this approach are to detect any conflict types between the messages during message exchange process and to identify the conflict type for each detected conflict. The proposed approach plays as a vital step for improving the semantic interoperability between heterogeneous Web service messages, since it facilitate the process of addressing semantic conflicts. A real scenario was used in order to demonstrate the feasibility of the proposed approach.

Design and Implementation of Oilfield Heterogeneous Data Integration Model Based on Ontology

This paper provides an ontology-based distributed heterogeneous data integration framework (ODHDIF). The framework resolves the problem of semantic interoperability between heterogeneous data sources in semantic level. By metadatas specifying the distributed, heterogeneous data and by describing semantic information of data source , having "ontology" as a common semantic model, semantic match is established through ontology mapping between heterogeneous data sources and semantic difference institutions are shielded, so that semantic heterogeneity problem of the heterogeneous data sources can be effectively solved. It provides an effective technology measure for the interior information of enterprises to be shared in time accurately.

Research and Application on Oilfield Product Heterogeneous Data Integration Based on Ontology

The Oil & Gas industry is moving forward with Integrated Operations (IO). There are different ways to achieve data integration, and ontology-based approaches have drawn much attention. This paper introduces an ontology-based distributed data integration framework (ODDIF). The framework resolves the problem of semantic interoperability between heterogeneous data sources in semantic level. By metadatas specifying the distributed, heterogeneous data and by describing semantic information of data source , having "ontology" as a common semantic model, semantic match is established through ontology mapping between heterogeneous data sources and semantic difference institutions are shielded, so that semantic heterogeneity problem of the heterogeneous data sources can be effectively solved. The proposed method reduces developing difficulty, improves developing efficiency, and enhances the maintainability and expandability of the system.

Proposal of a controlled vocabulary to solve semantic interoperability problems in social security information exchanges

Purpose – This report aims to present the state of play of semantic interoperability problems in social security data exchanges. Design/methodology/approach – The subject is presented as an open issue and taking into account the circumstances of the exchanges. Electronic exchanges in European Union social security are used as a case study. Findings – Semantic level of these exchanges seems to be the weakness and the use of controlled vocabularies is proposed as possible solution. The creation and maintenance of metadata sets are finally considered as a compromise solution. Originality/value – The article proposes a solution for the current semantic problems in electronic exchanges of social security information. The solution could be useful for social security institutions all over the world.

Manufacturability verification through feature-based ontological product models

To achieve efficient, fast and cost effective production, designers must consider all the manufacturing stages a product has to go through. A case study in a manufacturing setup shows that owing to the differences in perception of an engineering component, the coordination between design and manufacturing becomes difficult. Semantic interoperability problems are therefore faced when knowledge sharing for the purpose of manufacturability verification is attempted through computer-based knowledge bases. Ontologies have a reputation for solving semantic interoperability problems. Combined with shape feature-based models of components, ontologies provide a basis for seamless knowledge sharing. This article demonstrates the use of ontologies for analyzing the manufacturability of engineering components in the early design stages. This is done by developing shape feature-based ontological models of these components and associating manufacturability knowledge with these models. To achieve this, an ontological modelling technique is proposed that uses shape feature-based geometrical models of engineering components as building blocks. The knowledge associated with these models to demonstrate their use for manufacturability verification is derived from the findings of a case study also detailed in this article.

Temporal Logic To Query Semantic Graphs Using The Model Checking Method

Semantic interoperability problems have found their solutions due to the use of languages and tec hniques from the Semantic Web. The proliferations of ontologies and meta-information have improved the understanding of information and the relevance of search engine resp onses. However, the construction of semantic graphs is a s ource of numerous errors of interpretation or modeling, and scalability remains a major problem. The processing of large semantic graphs is a limit to the use of sema ntics in current information systems. The work presented in this paper is part of a new research at the border of tw o areas: the semantic web and the model checking. This line of research concerns the adaptation of model checking techniques to semantic graphs. We present a first method of converting RDF (Resource Description Framework) graphs into NµSMV and PROMELA (Process Meta Language) languages in order to be checked with the temporal logic property and queried by the temporal logic query. SPARQL (Simple Protocol and RDF query Language) query language is the standard for querying the Semantic Web, but it has a lot of limitations. Our primary g oal with the temporal logic query is to overcome this limita tion of the SPARQL query language. To reach this goal, three tools have been developed. The first two tools RDF2SPIN and RDF2NµSMV are used to transform the Semantic graph into a model written in PROMELA and respectively in NµSMV languages - in order to be understood by the SPIN and respectively the NµSMV model checkers. The STL Resolver tool is used to find solutions to the temp oral logic query. It is based on the model checking algorithms .

Instance-based Semantic Interoperability in the Cultural Heritage

This paper gives a comprehensive overview over the problem of Semantic Interoperability in the Cultural Heritage domain, with a particular tbcus on solutions centered around extensional, i.e., instance-based, ontology matching methods. It presents three typical scenarios requiring interoperability, one with homogeneous collections, one with heterogeneous collections, and one with multi-lingual collections. It discusses two different ways to evaluate potential alignments, one based on the application of re-indexing, one using a reference alignment. To these scenarios we apply extensional matching with different similarity measures, which gives interesting insight into the applicability of this matching approach. Finally, we firmly position our work in the Cultural Heritage context through an extensive discussion of the relevance for, and isstles related to this specific field. The findings are as unspectacular as expected, but nevertheless important: the provided methods can really improve interoperability in a number of important cases, but they are not universal solutions to all related problems. This paper provides a solid foundation for any future work on Semantic Interoperability in the Cultural Heritage domain, in particular tbr anybody intending to apply extensional methods.

S-Match: An open source framework for matching lightweight ontologies

Achieving automatic interoperability among systems with diverse data structures and languages expressing different viewpoints is a goal that has been difficult to accomplish. This paper describes S-Match, an open source semantic matching framework that tackles the semantic interoperability problem by transforming several data structures such as business catalogs, web directories, conceptual models and web services descriptions into lightweight ontologies and establishing semantic correspondences between them. The framework is the first open source semantic matching project that includes three different algorithms tailored for specific domains and provides an extensible API for developing new algorithms, including possibility to plug-in specific background knowledge according to the characteristics of each application domain.