Semantic Integration Approach Research Articles

Semantic Interoperability in Multi-Cloud Platforms: A Reference Architecture Utilizing an Ontology-Based Approach

The rapid expansion of cloud computing has necessitated the development of multi-cloud strategies, which leverage the strengths of multiple cloud service providers to mitigate risks such as vendor lock-in and enhance performance and reliability. Nevertheless, semantic interoperability remains a critical challenge in multi-cloud platforms, where diverse cloud services need to communicate and function seamlessly. Current solutions lack a unified semantic-based representation within reference architectures in multi-cloud platforms and mainly focus on the independent interoperability of a service model, i.e., SaaS, PaaS, and IaaS. This study addresses the critical issue of semantic interoperability in multi-cloud platforms, where the heterogeneity of proprietary cloud solutions impedes seamless integration and communication. Thus, we proposed a reference architecture utilizing an ontology-based approach to facilitate semantic interoperability across diverse cloud platforms. The reference architecture is based on five semantic interoperability requirements identified in our previous study. This paper presents the design and development of a reference architecture that includes high-level and low-level components supported by a taxonomy of semantic interoperability in multi-cloud platforms. Expected outcomes of this study include a standardized framework using an ontology-based approach for semantic mapping and integration of cloud services, which will significantly enhance interoperability and efficiency in multi-cloud platforms. The significance of this research lies in its potential to advance the state of knowledge and practice in multi-cloud computing, enabling more robust and flexible cloud service ecosystems.

Semantic Data Integration and Querying: A Survey and Challenges

Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.

A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments.

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.

SNOWL model: social networks unification-based semantic data integration

Integrating social networks data in the process of promoting business and marketing applications is widely addressed by several researchers. However, regarding the isolation between social network platforms managing such data has become a challenging task facing data scientist. In this respect, the present paper is designed to put forward a special semantic data integration approach, whereby a unified presentation and access to social networks data can be maintained. To this end, the novel SNOWL (Social Network OWL) ontology aims to provide a new social network content modeling, following the UPON Lite ontology-construction methodology. The advanced ontology is not created from scratch; it is but a continuation of some previously devised ontologies, elaborated to integrate an additional selection of newly incorporated social entities, such as content and user popularity. Additionally, and for an effective advantage of the model to be gained, a special mapping of the social networks data has been firstly implemented to the designed ontology, developed on the basis of the RML mapping language. Secondly, the SNOWL ontology is evaluated through the OOPS! Pitfall tool. Finally, a set of SPARQL-based services has also been designed on top of the SNOWL ontology in a bid to ensure a unified access to the mapped social data.

Semantic Integration in Big Data: State-of-the-Art

Nowadays, web users and systems continually overload the web with an exponential generation of a massive amount of data. This leads to making big data more important in several domains such as social networks, internet of things, health care, E-commerce, aviation safety, etc. The use of big data has become increasingly crucial for companies due to the significant evolution of information providers and users on the web. However, big data remain meaningless without semantics. In order to get a good comprehension of big data, we raise questions about how big data and semantic are related to each other and how semantic may help. To overcome this problem, researchers devote considerable time to the integration of ontology in big data to ensure reliable interoperability between systems in order to make big data more useful, readable and exploitable. This technology can hide the heterogeneity of different data resources. Moreover, in given domains, users can exchange knowledge without caring to choose the suitable semantic that makes their content more expressive. This paper aims to provide a comprehensive overview for readers about big data and the appropriate tools to manipulate and analyse them such as Hadoop. Afterwards, we talk about ontology and how it can be used to improve big data management and analyses for decision makers. Finally, different semantic integration approaches are seen in a comparative study. This survey is concluded with a discussion and some perspectives.

An approach for semantic integration of heterogeneous data sources.

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view.

Integrating the Mechanical Domain into Seed Approach

AbstractData-driven technologies have found their way into all areas of engineering. In product development they can accelerate the customization to individualized requirements. Therefore, they need a database that exceeds common product data management systems. The creation of this database proves to be challenging because in addition to explicit standards and regulations the product design contains implicit knowledge of product developers. Hence, this paper presents an approach for the semantic integration of the engineering design (SeED). The goal is an automated design of an ontology, which represents the product design in detail.SeED fulfils two tasks. First, the ontology provides a machine-processable representation of the products design, which enables all kind of data-driven technologies. Among other representations, the ontology contains formal logics and semantics. Accordingly, it is a more comprehensible solution for product developers and knowledge engineers. Second, the detailed representation enables discovering of intrinsic knowledge, e.g. design patterns in product generations. Consequently, SeED is a novel approach for efficient semantic integration of the product design.

Data Integration through Ontology-Based Data Access to Support Integrative Data Analysis: A Case Study of Cancer Survival.

To improve cancer survival rates and prognosis, one of the first steps is to improve our understanding of contributory factors associated with cancer survival. Prior research has suggested that cancer survival is influenced by multiple factors from multiple levels. Most of existing analyses of cancer survival used data from a single source. Nevertheless, there are key challenges in integrating variables from different sources. Data integration is a daunting task because data from different sources can be heterogeneous in syntax, schema, and particularly semantics. Thus, we propose to adopt a semantic data integration approach that generates a universal conceptual representation of "information" including data and their relationships. This paper describes a case study of semantic data integration linking three data sets that cover both individual and contextual level factors for the purpose of assessing the association of the predictors of interest with cancer survival using cox proportional hazard models.

Interoperable Information Exchange as Enabler of NFF Related TES

Abstract The lifecycle of a product is managed through the Product Lifecycle Management (PLM) including the integration of Product Service Systems (PSS) as well as Through-life Engineering Services. Both activities are performed by staff throughout the entire lifecycle and require the input of knowledge of different product lifecycle phases. The authors demonstrate the necessity of information exchange between the beginning of life and the middle of life on the example of NFF. The provision of the right information in the middle of life should be an information based TES which can only be achieved if the interoperability challenges are solved between the life-cycle phases. This paper demonstrates how existing semantic data integration approaches could be applied to enable the required information exchange. For that purpose, the modelling of test process related knowledge as ontology and corresponding extraction methods are presented.

A Novel Approach for Semantic Integration of Data using Ontology

Integration process of data is recently recognized like a significant visualization of the Semantic Web explore for which researchers focus on numerous areas, such as integration of information, ontologies and databases. Objectives: Users generally requires an incorporated analysis of information accessible from various data and it was proposed to grant users with this view of data. Methods: The Meta data is created from different data sets like excel data set, RDF data set and XML data set. Findings: In this work a survey was made for integrating databases using ontology and a new approach for integration of databases is devised for finding correspondence between ontologies. Applications: View and Search functionality was provided by filtering data from Meta data. This provides easy access to the integrated data.

Integrating Ontological Data Sources Using Viewpoints-Based Approach

Within the development of Internet and intranets, information integration from various data sources becomes increasingly important and more challenging issue. Recently, the trend in data integration has favored the semantic integration using ontologies. However, the existing ontology-based approaches do not support the aspect of data multi-representations, which is important in the development of multi-user applications. The motivation of this paper is to address a novel semantic integration approach based on ontologies and viewpoints paradigms. This contribution combines the advantages of existing ontology-based integration approaches while avoiding their drawbacks. The proposed integration approach is evaluated using query processing. Profiles are introduced to offer answers to users according to their viewpoints and choices.

A semantic integration approach to publish and retrieve ecological data

Purpose – The purpose of this paper is to present a four-level architecture that aims at integrating, publishing and retrieving ecological data making use of linked data (LD). It allows scientists to explore taxonomical, spatial and temporal ecological information, access trophic chain relations between species and complement this information with other data sets published on the Web of data. The development of ecological information repositories is a crucial step to organize and catalog natural reserves. However, they present some challenges regarding their effectiveness to provide a shared and global view of biodiversity data, such as data heterogeneity, lack of metadata standardization and data interoperability. LD rose as an interesting technology to solve some of these challenges. Design/methodology/approach – Ecological data, which is produced and collected from different media resources, is stored in distinct relational databases and published as RDF triples, using a relational-Resource Description Format mapping language. An application ontology reflects a global view of these datasets and share with them the same vocabulary. Scientists specify their data views by selecting their objects of interest in a friendly way. A data view is internally represented as an algebraic scientific workflow that applies data transformation operations to integrate data sources. Findings – Despite of years of investment, data integration continues offering scientists challenges in obtaining consolidated data views of a large number of heterogeneous scientific data sources. The semantic integration approach presented in this paper simplifies this process both in terms of mappings and query answering through data views. Social implications – This work provides knowledge about the Guanabara Bay ecosystem, as well as to be a source of answers to the anthropic and climatic impacts on the bay ecosystem. Additionally, this work will enable evaluating the adequacy of actions that are being taken to clean up Guanabara Bay, regarding the marine ecology. Originality/value – Mapping complexity is traded by the process of generating the exported ontology. The approach reduces the problem of integration to that of mappings between homogeneous ontologies. As a byproduct, data views are easily rewritten into queries over data sources. The architecture is general and although applied to the ecological context, it can be extended to other domains.

State of the Art on Ontology Alignment

Ontology mapping as a semantic data integration approach has evolved from traditional data integration solutions. The core problems and open issues related to early data integration approaches are also applicable to ontology mapping on the Semantic Web community. Therefore, in this review the authors present the related literature, starting from the traditional data integration approaches, in order to highlight the evolution of data integration from the early approaches. Once the roots of semantic data integration have been presented, the authors proceed to introduce the state-of-the-art of the ontology mappings systems including the early approaches and the systems that can be compared through the Ontology Alignment Initiative (OAEI).

Semantic Data Integration Approaches for E-Governance

Increased generation of data in the e-governance R&D process is required to generate the expected services in terms of enhanced e-services productivity and pipelines. The inability of existing integration strategies to organise and apply the available knowledge to the range of real scientific, business and governance issues is impacting on not only productivity but also transparency of information in crucial safety and regulatory applications. This requires focusing on normative models of e-governance that typically can assert horizontal (inter-agency) and vertical (inter-governmental) integration of data flows to represent the most sophisticated form of e-government delivering greatest payoff for both governments and users. The new range of semantic technologies based on ontology enable proper integration of knowledge in a way that is reusable by several applications across governance business from discovery to ministry affairs. The objective of this paper is to provide an insight on the necessary and sufficient knowledge base to deal with data integration using semantic web technologies applicable for e-governance based on exploratory research using literature survey. It assumes that reader has the capability of understanding some basic knowledge on E-governance, Relational Database Management, Ontology, and Service Oriented Architecture and Semantic Web Technology.

Using Ontologies to Relate Resource Management Actions to Environmental Monitoring Data in South East Queensland

The Health-e-Waterways Project is a multi-disciplinary collaboration between the University of Queensland, Microsoft Research and the South East Queensland Healthy Waterways Partnership (SEQ-HWP). This project develops the underlying technological framework and set of services to enable streamlined access to the expanding collection of real-time, near-real-time and static datasets related to water resource management in South East Queensland. More specifically, the system enables water resource managers to access the datasets being captured by the various agencies participating in the SEQ HWP Ecosystem Health Monitoring Program (EHMP). It also provides online access to the statistical data processing tools that enable users to analyse the data and generate online ecosystem report cards dynamically via a Web mapping interface. The authors examine the development of ontologies and semantic querying tools to integrate disparate datasets and relate management actions to water quality indicators for specific regions and periods. This semantic data integration approach enables scientists and resource managers to identify which actions are having an impact on which parameters and adapt the management strategies accordingly. This paper provides an overview of the semantic technologies developed to underpin the adaptive management framework that is the central philosophy behind the SEQ HWP.

Modeling Approach for Integration and Evolution of Information System Conceptualizations

Most information systems development methodologies are based on conceptual modeling of static and dynamic views, which are represented by totally different types of diagrams. Understanding of the interplay among interactive, behavioral and structural aspects of specifications is necessary for identification of semantic integrity problems between business process and business data. Typically, semantic inconsistencies and discontinuities between collections of conceptual representations are not easy to detect and to comprehend for information system designers due to static and dynamic aspects of models being visualized in isolation. The goal of this paper is to present a modeling approach for semantic integration and evolution of static and dynamic aspects of conceptual models. Visualization of interplay among structural, interactive and behavioral aspects of computation-neutral representations helps to understand crosscutting concerns and integrity problems of information system conceptualizations. The main advantage of the presented conceptual modeling approach is stability and flexibility of diagrams in dealing with the evolutionary changes of requirements. Therefore, the developed modeling foundation is targeted to both business managers and information system designers for the purpose of computation-neutral integration and evolution of information systems specifications.

Instance-based domain ontological view creation towards semantic integration

In many domains today there are very limited explicit ontologies established for implementing information systems. Traditional ontology-driven semantic integration approaches cannot be directly applied in integrating these information systems. Usually, the information systems have schemas, a type of formal information model, for their information repositories which to some extent imply the semantics of the information. Each schema actually reflects a specific view of the domain conceptualization. This paper investigates the theoretical foundation of ontologies and extends the traditional ontology concept to the ontological view concept. It proposes to use ontological views to address the challenge of semantic integration. The proposed approach adopts the schemas to create local ontological views, uses data instances of the information systems to discover semantic relationships between the concepts within the ontological views, and builds a domain ontological view based on the discovered equivalence mappings. It applies the hierarchical clustering technique on the data instances and, in the further analysis, uses the clusters to reduce the cost of processing a large amount of data. The matching of concept properties is based on the probability distribution of the data instances. The experimental results have demonstrated the effectiveness of this approach.

DSI

Considerable research has been done on the content-based multimedia delivery and access in distributed data repositories. As noted in the literature, there is always a trade-off between multimedia quality and access speed. In addition, the overall performance is greatly determined by the distribution of the multimedia data. In this article, an unsupervised multimedia semantic integration approach for a distributed infrastructure, the Distributed Semantic Indexing (DSI), is presented that addresses both the data quality and search performance. With the ability of summarizing content information and guiding data distribution, the proposed approach is distinguished by: (1) logic-based representation and concise abstraction of the semantic contents of multimedia data, which are further integrated to form a general overview of a multimedia data repository—content signature; (2) application of linguistic relationships to construct a hierarchical metadata based on the content signatures allowing imprecise queries; and (3) achieving the optimal performance in terms of search cost. The fundamental structure of the proposed model is presented. The proposed scheme has been simulated and the simulation results are analyzed and compared against several other approaches that have been advocated in the literature.

Framework for the Analysis of the Adaptability, Extensibility, and Scalability of Semantic Information Integration and the Context Mediation Approach

Technological advances such as Service Oriented Architecture (SOA) have increased the feasibility and importance of effectively integrating information from an ever widening number of systems within and across enterprises. A key difficulty of achieving this goal comes from the pervasive heterogeneity in all levels of information systems. A robust solution to this problem needs to be adaptable, extensible, and scalable. In this paper, we identify the deficiencies of traditional semantic integration approaches. The COntext INterchange (COIN) approach overcomes these deficiencies by declaratively representing data semantics and using a mediator to create the necessary conversion programs from a small number of conversion rules. The capabilities of COIN is demonstrated using an example with 150 data sources, where COIN can automatically generate the over 22,000 conversion programs needed to enable semantic interoperability using only six parametizable conversion rules. This paper presents a framework for evaluating adaptability, extensibility, and scalability of semantic integration approaches. The application of the framework is demonstrated with a systematic evaluation of COIN and other commonly practiced approaches.

Integration of industrial information systems: from syntactic to semantic integration approaches

Nowadays, integration of enterprise information systems constitutes a real and growing need for most enterprises, especially for large and dynamic industrial ones. It constitutes the main approach to dealing with heterogeneity that concerns the multiple software applications that make up information systems. This paper constitutes a general survey on integration of industrial information systems, and aims to overview the main approaches that can be used in the context of industrial enterprises either for syntactic or semantic integration issues. In particular, this paper focuses on some semantics-based approaches that promote the use of ontologies, and especially the use of OWL-S service ontology.