Semantic interoperability:a metadata mediator approach

In this paper, we present a metadata mediation approach to intelligent integration of semantically heterogeneous geographical information (GI). Based on formalized metadata and expert system technology. the system can understand metadata of GI, represent explicit metadata knowledge, and use metadata mediator to automatically detect Semantic conflicts and resolve them: Furthermore, the prototype of metadata mediator has been given in this paper.

Semantic Interoperability Aggregation in Service Requirements Refinement

Semantic refinement of stakeholders＇ requirements is a fundamental issue in requirements engineering. Facing with the on-demand collaboration problem among the heterogeneous, autonomous, and dynamic service resources in the Web, service requirements refinement becomes extremely important, and the key issue in service requirements refinement is semantic interoperability aggregation. A method for creating connecting ontologies driven by requirement sign ontology is proposed. Based on connecting ontologies, a method for semantic interoperability aggregation in requirements refinement is proposed. In addition, we discover that the necessary condition for semantic interoperability is semantic similarity, and the sufficient condition is the coverability of the agreed mediation ontology. Based on this viewpoint, a metric framework for calculating semantic interoperability capability is proposed. This methodology can provide a semantic representation mechanism for refining users＇ requirements; meanwhile, since users＇ requirements in the Web usually originate from different domains, it can also provide semantic interoperability guidance for networked service discovery, and is an effective approach for the realization of on-demand service integration. The methodology will be beneficial in service-oriented software engineering and cloud computing.

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.

WMO:an ontology for the semantic enrichment of wetland monitoring data

Rich observation data generated by ubiquitous sensors are vital for wetland monitoring,spanning from the prediction of natural disasters to emergency response.Such sensors use different data acquisition and description methods and,if combined,could provide a comprehensive description of the wetland.Unfortunately,these data remain hidden in isolated silos,and their variety makes integration and interoperability a significant challenge.In this work,we develop a semantic model for wetland monitoring data using an agile and modular approach,namely,wetland monitoring ontology(WMO),which containsfive modules:wetland ecosystem,monitoring indicator,monitoring context,geospatial context,and temporal context.The proposed ontology supports the semantic interoperability and integration of wetland monitoring data from multiple sources,domains,modes,and spatiotemporal scales.We also provide two real-world use cases to validate the WMO and demonstrate the WMO’s usability and reusability.

Digital twin-enabled smart facility management: A bibliometric review

In recent years,the architecture,engineering,construction,and facility management(FM)industries have been applying various emerging digital technologies to facilitate the design,construction,and management of infrastructure facilities.Digital twin(DT)has emerged as a solution for enabling real-time data acquisition,transfer,analysis,and utilization for improved decision-making toward smart FM.Substantial research on DT for FM has been undertaken in the past decade.This paper presents a bibliometric analysis of the literature on DT for FM.A total of 248 research articles are obtained from the Scopus and Web of Science databases.VOSviewer is then utilized to conduct bibliometric analysis and visualize keyword co-occurrence,citation,and co-authorship networks;furthermore,the research topics,authors,sources,and countries contributing to the use of DT for FM are identified.The findings show that the current research of DT in FM focuses on building information modeling-based FM,artificial intelligence(AI)-based predictive maintenance,real-time cyber–physical system data integration,and facility lifecycle asset management.Several areas,such as AI-based real-time asset prognostics and health management,virtual-based intelligent infrastructure monitoring,deep learning-aided continuous improvement of the FM systems,semantically rich data interoperability throughout the facility lifecycle,and autonomous control feedback,need to be further studied.This review contributes to the body of knowledge on digital transformation and smart FM by identifying the landscape,state-of-the-art research trends,and future needs with regard to DT in FM.

Semantic Extraction for Multi-Enterprise Business Collaboration

Semantic extraction is essential for semantic interoperability in multi-enterprise business collaboration environments. Although many studies on semantic extraction have been carried out, few have focused on how to precisely and effectively extract semantics from multiple heterogeneous data schemas. This paper presents a semi-automatic semantic extraction method based on a neutral representation format （NRF） for acquiring semantics from heterogeneous data schemas. As a unified syntax-independent model, NRF removes all the contingencies of heterogeneous data schemas from the original data environment. Conceptual extraction and keyword extraction are used to acquire the semantics from the NRF. Conceptual extraction entails constructing a conceptual model, while keyword extraction seeks to obtain the metadata. An industrial case is given to validate the approach. This method has good extensibility and flexibility. The results show that the method provides simple, accurate, and effective semantic interoperability in multi-enterprise business collaboration environments.

FAIR Data Point:A FAIR-Oriented Approach for Metadata Publication

Metadata,data about other digital objects,play an important role in FAIR with a direct relation to all FAIR principles.In this paper we present and discuss the FAIR Data Point(FDP),a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles.We present the core components and features of the FDP,its approach to metadata provision,the criteria to evaluate whether an application adheres to the FDP specifications and the service to register,index and allow users to search for metadata content of available FDPs.

Ontology,Ontologies and the“I”of FAIR

According to the FAIR guiding principles,one of the central attributes for maximizing the added value of information artifacts is interoperability.In this paper,I discuss the importance,and propose a characterization of the notion of Semantic Interoperability.Moreover,I show that a direct consequence of this view is that Semantic Interoperability cannot be achieved without the support of,on one hand,(i)ontologies,as meaning contracts capturing the conceptualizations represented in information artifacts and,on the other hand,of(ii)Ontology,as a discipline proposing formal meth-ods and theories for clarifying these conceptualizations and articulating their representations.In particular,I discuss the fundamental role of formal ontological theories(in the latter sense)to properly ground the construction of representation languages,as well as methodological and computational tools for supporting the engineering of ontologies(in the former sense)in the context of FAIR.