By analyzing of the existing Web services,an ontology based on OWL is presented,which has rich semantic information,and the service description language OWL-S based on OWL is put forward. OWL-S through IOPE can descri...By analyzing of the existing Web services,an ontology based on OWL is presented,which has rich semantic information,and the service description language OWL-S based on OWL is put forward. OWL-S through IOPE can describe services,can also combine the service,but the method of combination service is not automatic. So a method is presented by using Situation Calculus for automatic service composition based on the OWL-S model. Finally through the example analysis,the method of automatic service combination was validated.展开更多
Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple sources.Related studies focus on potential SW technologies for resolving...Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple sources.Related studies focus on potential SW technologies for resolving big data problems,such as structurally and semantically heterogeneous data that result from the variety of data formats(structured,semi-structured,numeric,unstructured text data,email,video,audio,stock ticker).SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data.In the current research,we implement a new semantic Extract Transform Load(ETL)model that uses SW technologies for aggregating,integrating,and representing data as linked data.First,geospatial data resources are aggregated from the internet,and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework(RDF)format for successful integration and representation.The principal contribution of this research is the synthesis,aggregation,and semantic representation of geospatial data to solve problems.A case study of city data is used to illustrate the semantic ETL model’s functionalities.The results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation,integration,and representation.展开更多
According to the features of the semantic web technology,it is very suitable to solve the security issue of the current social network environment.Firstly,in this paper,it extends the existing ontology model of the so...According to the features of the semantic web technology,it is very suitable to solve the security issue of the current social network environment.Firstly,in this paper,it extends the existing ontology model of the social network with some relevant classes,and introduces a brand new ontology which is used to represent the malicious information.After introducing these models,a method of identifying the malicious message is raised.Finally,the experiments and simulations analyze the feasibility of the whole system.The results validate that the malicious users can be automatically filtered,and some worthy digital evidence can be effectively provided to forensic investigators.展开更多
Remarkable progress in research has shown the efficiency of Knowledge Graphs(KGs)in extracting valuable external knowledge in various domains.A Knowledge Graph(KG)can illustrate high-order relations that connect two o...Remarkable progress in research has shown the efficiency of Knowledge Graphs(KGs)in extracting valuable external knowledge in various domains.A Knowledge Graph(KG)can illustrate high-order relations that connect two objects with one or multiple related attributes.The emerging Graph Neural Networks(GNN)can extract both object characteristics and relations from KGs.This paper presents how Machine Learning(ML)meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning.The paper also highlights important aspects of this area of research,discussing open issues such as the bias hidden in KGs at different levels of graph representation。展开更多
It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modele...It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data.Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data.Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method.Based on this,this paper proposes the structure,elements,basic calculations and multidimensional reasoning method of the new knowledge model.A modeling example of the regulations defined in electric power system operation standard is demonstrated.Different forms of the model and related technologies are also introduced,including electric power system standard modeling,multi-type data management,unstructured data searching,knowledge display and data analysis based on semantic expansion and reduction.Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data.With the development of electric power big data technology,it is expected that the knowledge model will be improved and will be used in more applications.展开更多
Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one ...Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one hand,and on the other hand are too bespoke.Such limitations impede the transfer,interpretation,and reuse of the geovisualisation knowledge.In this paper,we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies.Specifically,we represent knowledge regarding cartographic scale,data portrayal and geometry source,which are three key aspects of geovisualisation in the contemporary web mapping era,coupling ontologies and semantic rules.The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions.A prototype system is developed in which geospatial linked data are used as underlying data,and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users.The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.展开更多
While significant progress has been made to implement the Digital Earth vision,current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to int...While significant progress has been made to implement the Digital Earth vision,current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to integrate data and models for simulating social and physical processes.To achieve effectiveness of decisionmaking using Digital Earth for understanding the Earth and its systems,new infrastructures that provide capabilities of computational simulation are needed.This paper proposed a framework of geospatial semantic web-based interoperable spatial decision support systems(SDSSs)to expand capabilities of the currently implemented infrastructure of Digital Earth.Main technologies applied in the framework such as heterogeneous ontology integration,ontology-based catalog service,and web service composition were introduced.We proposed a partitionrefinement algorithm for ontology matching and integration,and an algorithm for web service discovery and composition.The proposed interoperable SDSS enables decision-makers to reuse and integrate geospatial data and geoprocessing resources from heterogeneous sources across the Internet.Based on the proposed framework,a prototype to assist in protective boundary delimitation for Lunan Stone Forest conservation was implemented to demonstrate how ontology-based web services and the services-oriented architecture can contribute to the development of interoperable SDSSs in support of Digital Earth for decision-making.展开更多
The growth rates of today’s societies and the rapid advances in technology have led to the need for access to dynamic,adaptive and personalized information in real time.Augmented reality provides prompt access to rap...The growth rates of today’s societies and the rapid advances in technology have led to the need for access to dynamic,adaptive and personalized information in real time.Augmented reality provides prompt access to rapidly flowing information which becomes meaningful and‘‘alive’’as it is embedded in the appropriate spatial and time framework.Augmented reality provides new ways for users to interact with both the physical and digital world in real time.Furthermore,the digitization of everyday life has led to an exponential increase of data volume and consequently,not only have new requirements and challenges been created but also new opportunities and potentials have arisen.Knowledge graphs and semantic web technologies exploit the data increase and web content representation to provide semantically interconnected and interrelated information,while deep learning technology offers novel solutions and applications in various domains.The aim of this study is to present how augmented reality functions and services can be enhanced when integrating deep learning,semantic web and knowledge graphs and to showcase the potentials their combination can provide in developing contemporary,user-friendly and user-centered intelligent applications.Particularly,we briefly describe the concept of augmented reality and mixed reality and present deep learning,semantic web and knowledge graphs technologies.Moreover,based on our literature review,we present and analyze related studies regarding the development of augmented reality applications and systems that utilize these technologies.Finally,after discussing how the integration of deep learning,semantic web and knowledge graphs into augmented reality enhances the quality of experience and quality of service of augmented reality applications to facilitate and improve users’everyday life,conclusions and suggestions for future research and studies are given.展开更多
While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisua...While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisually to the end user and how to automatize the process.In this paper,we first present vocabularies to describe at a high level the elements that make up a geovisualization.We then propose a method that describes at a semantic level how to obtain a geovisualization from an existing data model.This method is based on our vocabularies and on a set of semantic rules encoding rich and complex operations on data.This leads to the derivation of ontological knowledge,ready to be exploited to automate the creation of a geovisualization.The method is implemented in a framework that uses Semantic Web technologies.The singularity and the strength of our proposal is that it enables to describe a geovisualization through a RDF specification file,which once loaded in our system makes the geovisualization directly available for use from a Web browser.This result is obtained by extending a priori an application data model with ad hoc geovisualization semantics features and rules.展开更多
Semantic technologies have emerged as a prominent research area within Big Earth Data.These technologies have provided significant benefits for data discovery and integration.Yet,the formality of the Semantic Web,in l...Semantic technologies have emerged as a prominent research area within Big Earth Data.These technologies have provided significant benefits for data discovery and integration.Yet,the formality of the Semantic Web,in languages such as the Web Ontology Language(OWL),does not always integrate well with the numerical,statistical,and geometric methods of the geosciences.Two prominent challenges in this area are how to semantically model individual measurements and what to do when geoscience needs are not addressed by languages such as OWL.This has led to a fragmented Big Earth Data community with either no solution or incompatible semantic solutions.We use an oceanographic example to highlight the limitations and challenges surrounding the semantic encoding of observations and the use of semantics during analysis.We then present potential solutions to each challenge showing that a full end-to-end application of semantic technologies is not only feasible,but beneficial to Big Earth Data.展开更多
Semantic web technologies have become a popular technique to apply meaning to unstructured data.They have been infrequently applied to problems within the agricultural domain when compared to complementary domains.Des...Semantic web technologies have become a popular technique to apply meaning to unstructured data.They have been infrequently applied to problems within the agricultural domain when compared to complementary domains.Despite this lack of application,agriculture has a large number of semantic resources that have been developed by large NGOs such as the Food and Agriculture Organization(FAO).This survey is intended to motivate further research in the application of semantic web technologies for agricultural problems,by making available a self contained reference that provides:a comprehensive review of preexisting semantic resources and their construction methods,data interchange standards,as well as a survey of the current applications of semantic web technologies.展开更多
Blockchains and smart contracts are gaining momentum as enabling technologies for a wide set of applications where data distribution and sharing among decentralized infrastructures is required.In this work,we present ...Blockchains and smart contracts are gaining momentum as enabling technologies for a wide set of applications where data distribution and sharing among decentralized infrastructures is required.In this work,we present a distributed application developed using blockchain technologies that allows individuals and health insurance organizations to come into agreement during the implementation of the healthcare insurance policies in each contract.For this purpose,health standards and semantic web technologies were used for the formal expression of both the insured individual's data and contract terms.Accordingly,a fine-grained data access policy was applied for evaluating contract terms on the basis of relevant data captured in healthcare settings.A prototype was implemented involving the development of several different smart contracts for the Ethereum platform as well as the necessary visual environment for accessing them.The developed system validates various features related to blockchain and smart contract features that are briefly discussed in this work,part of which can be mitigated or resolved through the use of a private permissioned blockchain.The application of well-established techniques for potential malfunctions of external services could also boost the security of the system and prevent it from potential attacks.展开更多
Purpose-The success of web services involved the adoption of this technology by different service providers through the web,which increased the number of web services,as a result making their discovery a tedious task....Purpose-The success of web services involved the adoption of this technology by different service providers through the web,which increased the number of web services,as a result making their discovery a tedious task.The UDDI standard has been proposed for web service publication and discovery.However,it lacks sufficient semantic description in the content of web services,which makes it difficult to find and compose suitable web services during the analysis,search,and matching processes.In addition,few works on semantic web services discovery take into account the user’s profile.The purpose of this paper is to optimize the web services discovery by reducing the search space and increasing the number of relevant services.Design/methodology/approach-The authors propose a new approach for the semantic web services discovery based on the mobile agent,user profile and metadata catalog.In the approach,each user can be described by a profile which is represented in two dimensions:personal dimension and preferences dimension.The description of web service is based on two levels:metadata catalog and WSDL.Findings-First,the semantic web services discovery reduces the number of relevant services through the application of matching algorithm“semantic match”.The result of this first matching restricts the search space at the level of UDDI registry,which allows the users to have good results for the“functional match”.Second,the use of mobile agents as a communication entity reduces the traffic on the network and the quantity of exchanged information.Finally,the integration of user profile in the service discovery process facilitates the expression of the user needs and makes intelligible the selected service.Originality/value-To the best knowledge of the authors,this is the first attempt at implementing the mobile agent technology with the semantic web service technology.展开更多
Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,an...Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,and journals.From such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies.Keyword-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant results.They are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given word.Therefore,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the data.An ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal cases.The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries.In this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two experiments.In the first experiment,we implemented a keyword-based textual IR systemusing Apache Lucene.In the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries.The keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.展开更多
Multimedia Sensor Networks(MSNs)have enhanced the ability to analyze the environment and provide responses based on its current status.Generally,MSNs are composed of scalar and multimedia sensors that have fixed locat...Multimedia Sensor Networks(MSNs)have enhanced the ability to analyze the environment and provide responses based on its current status.Generally,MSNs are composed of scalar and multimedia sensors that have fixed locations.However,given the advancement of smart mobile device technologies,it is currently possible to dynamically integrate mobile sensors into MSNs.In this paper,we propose a formal platform to manage MSNs and the data gathered from them to detect complex events.Our main contributions include:M^(2)SSN-Onto,a Mobile and Multimedia Semantic Sensor Networks Ontology;Py-CEMiD,an engine for detecting complex events and generate reactions to them;a mobile device location engine to locate mobile sensors;and a proof-of-concept in the context of detecting emergency situations in smart buildings.Several scenarios are validated for emergency events,combining simulated sensor measurements with real measurements of mobile devices.Results show complex events can be detected in near real time(less than 1 s).展开更多
The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information...The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.展开更多
Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The ...Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.展开更多
In the past decade, existing and new knowledge and datasets have been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts...In the past decade, existing and new knowledge and datasets have been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea via studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three gene ontology slims(plant,yeast, and candida, among which the latter two belong to the same kingdom — fungi) using four popular measures commonly applied to biomedical ontologies(Resnik, Lin, Jiang-Conrath,and Sim Rel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performances of JiangConrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by 1) consistently showing that yeast and candida are more similar(as compared to plant) at different scales, and 2) small deviations of the similarity values after excluding a majority of nodes from several lower scales.This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering.展开更多
In this paper,the research advances of ontology and its application are reviewed firstly.With the development of ontology technology,subject-oriented web information retrieval technology combining ontology has been be...In this paper,the research advances of ontology and its application are reviewed firstly.With the development of ontology technology,subject-oriented web information retrieval technology combining ontology has been becoming one of the hot scientific issues.The innovative method of the semantic web technology combined with the traditional information retrieval technology is put forward,and the related algorithm based on ontology for judging the relevancy with different topics is also represented,and has proved to be effective in given experiments.展开更多
Mostly, cloud agreements are signed between the consumer and the provider using online click-through agreements. Several issues and conflicts exist in the negotiation of cloud agreement terms due to the legal and ambi...Mostly, cloud agreements are signed between the consumer and the provider using online click-through agreements. Several issues and conflicts exist in the negotiation of cloud agreement terms due to the legal and ambiguous terms in Service Level Agreements (SLA). Semantic knowledge applied during the formation and negotiation of SLA can overcome these issues. Cloud SLA negotiation consists of numerous activities such as formation of SLA templates, publishing it in registry, verification and validation of SLA, monitoring for violation, logging and reporting and termination. Though these activities are interleaved with each other, semantic synchronization is still lacking. To overcome this, a novel SLA life cycle using semantic knowledge to automate the cloud negotiation has been formulated. Semantic web platform using ontologies is designed, developed and evaluated. The resultant platform increases the task efficiency of the consumer and the provider during negotiation. Precision and recall scores for Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) SLAs were calculated. And it reveals that applying semantic knowledge helps the extraction of meaningful answers from the cloud actors.展开更多
文摘By analyzing of the existing Web services,an ontology based on OWL is presented,which has rich semantic information,and the service description language OWL-S based on OWL is put forward. OWL-S through IOPE can describe services,can also combine the service,but the method of combination service is not automatic. So a method is presented by using Situation Calculus for automatic service composition based on the OWL-S model. Finally through the example analysis,the method of automatic service combination was validated.
文摘Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple sources.Related studies focus on potential SW technologies for resolving big data problems,such as structurally and semantically heterogeneous data that result from the variety of data formats(structured,semi-structured,numeric,unstructured text data,email,video,audio,stock ticker).SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data.In the current research,we implement a new semantic Extract Transform Load(ETL)model that uses SW technologies for aggregating,integrating,and representing data as linked data.First,geospatial data resources are aggregated from the internet,and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework(RDF)format for successful integration and representation.The principal contribution of this research is the synthesis,aggregation,and semantic representation of geospatial data to solve problems.A case study of city data is used to illustrate the semantic ETL model’s functionalities.The results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation,integration,and representation.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61373006)the Foundation of Nanjing University of Posts and Telecommunications(Grant No.NY212059)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘According to the features of the semantic web technology,it is very suitable to solve the security issue of the current social network environment.Firstly,in this paper,it extends the existing ontology model of the social network with some relevant classes,and introduces a brand new ontology which is used to represent the malicious information.After introducing these models,a method of identifying the malicious message is raised.Finally,the experiments and simulations analyze the feasibility of the whole system.The results validate that the malicious users can be automatically filtered,and some worthy digital evidence can be effectively provided to forensic investigators.
文摘Remarkable progress in research has shown the efficiency of Knowledge Graphs(KGs)in extracting valuable external knowledge in various domains.A Knowledge Graph(KG)can illustrate high-order relations that connect two objects with one or multiple related attributes.The emerging Graph Neural Networks(GNN)can extract both object characteristics and relations from KGs.This paper presents how Machine Learning(ML)meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning.The paper also highlights important aspects of this area of research,discussing open issues such as the bias hidden in KGs at different levels of graph representation。
基金supported by Science and Technology Foundation of the State Grid Corporation of China(XT71-14-043).
文摘It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data.Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data.Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method.Based on this,this paper proposes the structure,elements,basic calculations and multidimensional reasoning method of the new knowledge model.A modeling example of the regulations defined in electric power system operation standard is demonstrated.Different forms of the model and related technologies are also introduced,including electric power system standard modeling,multi-type data management,unstructured data searching,knowledge display and data analysis based on semantic expansion and reduction.Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data.With the development of electric power big data technology,it is expected that the knowledge model will be improved and will be used in more applications.
基金This work was supported by China Scholarship Council and Lund University.
文摘Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one hand,and on the other hand are too bespoke.Such limitations impede the transfer,interpretation,and reuse of the geovisualisation knowledge.In this paper,we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies.Specifically,we represent knowledge regarding cartographic scale,data portrayal and geometry source,which are three key aspects of geovisualisation in the contemporary web mapping era,coupling ontologies and semantic rules.The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions.A prototype system is developed in which geospatial linked data are used as underlying data,and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users.The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.
文摘While significant progress has been made to implement the Digital Earth vision,current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to integrate data and models for simulating social and physical processes.To achieve effectiveness of decisionmaking using Digital Earth for understanding the Earth and its systems,new infrastructures that provide capabilities of computational simulation are needed.This paper proposed a framework of geospatial semantic web-based interoperable spatial decision support systems(SDSSs)to expand capabilities of the currently implemented infrastructure of Digital Earth.Main technologies applied in the framework such as heterogeneous ontology integration,ontology-based catalog service,and web service composition were introduced.We proposed a partitionrefinement algorithm for ontology matching and integration,and an algorithm for web service discovery and composition.The proposed interoperable SDSS enables decision-makers to reuse and integrate geospatial data and geoprocessing resources from heterogeneous sources across the Internet.Based on the proposed framework,a prototype to assist in protective boundary delimitation for Lunan Stone Forest conservation was implemented to demonstrate how ontology-based web services and the services-oriented architecture can contribute to the development of interoperable SDSSs in support of Digital Earth for decision-making.
文摘The growth rates of today’s societies and the rapid advances in technology have led to the need for access to dynamic,adaptive and personalized information in real time.Augmented reality provides prompt access to rapidly flowing information which becomes meaningful and‘‘alive’’as it is embedded in the appropriate spatial and time framework.Augmented reality provides new ways for users to interact with both the physical and digital world in real time.Furthermore,the digitization of everyday life has led to an exponential increase of data volume and consequently,not only have new requirements and challenges been created but also new opportunities and potentials have arisen.Knowledge graphs and semantic web technologies exploit the data increase and web content representation to provide semantically interconnected and interrelated information,while deep learning technology offers novel solutions and applications in various domains.The aim of this study is to present how augmented reality functions and services can be enhanced when integrating deep learning,semantic web and knowledge graphs and to showcase the potentials their combination can provide in developing contemporary,user-friendly and user-centered intelligent applications.Particularly,we briefly describe the concept of augmented reality and mixed reality and present deep learning,semantic web and knowledge graphs technologies.Moreover,based on our literature review,we present and analyze related studies regarding the development of augmented reality applications and systems that utilize these technologies.Finally,after discussing how the integration of deep learning,semantic web and knowledge graphs into augmented reality enhances the quality of experience and quality of service of augmented reality applications to facilitate and improve users’everyday life,conclusions and suggestions for future research and studies are given.
基金financed by the French National Research Agency within the project ‘Heterogeneous data integration and spatial reasoning for locating victims in mountain areas–CHOUCAS’[ANR-16-CE23-0018].
文摘While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisually to the end user and how to automatize the process.In this paper,we first present vocabularies to describe at a high level the elements that make up a geovisualization.We then propose a method that describes at a semantic level how to obtain a geovisualization from an existing data model.This method is based on our vocabularies and on a set of semantic rules encoding rich and complex operations on data.This leads to the derivation of ontological knowledge,ready to be exploited to automate the creation of a geovisualization.The method is implemented in a framework that uses Semantic Web technologies.The singularity and the strength of our proposal is that it enables to describe a geovisualization through a RDF specification file,which once loaded in our system makes the geovisualization directly available for use from a Web browser.This result is obtained by extending a priori an application data model with ad hoc geovisualization semantics features and rules.
基金This work was supported from the National Science Foundation grant Earth Cube Building Blocks:Collaborative Proposal:GeoLink-Leveraging Semantics and Linked Data for Data Sharing and Discovery in the Geosciences.The authors would like to acknowledge their respective collaborative grants numbered 1435578 and 1440139.
文摘Semantic technologies have emerged as a prominent research area within Big Earth Data.These technologies have provided significant benefits for data discovery and integration.Yet,the formality of the Semantic Web,in languages such as the Web Ontology Language(OWL),does not always integrate well with the numerical,statistical,and geometric methods of the geosciences.Two prominent challenges in this area are how to semantically model individual measurements and what to do when geoscience needs are not addressed by languages such as OWL.This has led to a fragmented Big Earth Data community with either no solution or incompatible semantic solutions.We use an oceanographic example to highlight the limitations and challenges surrounding the semantic encoding of observations and the use of semantics during analysis.We then present potential solutions to each challenge showing that a full end-to-end application of semantic technologies is not only feasible,but beneficial to Big Earth Data.
基金The research in this review was supported by FAPESP grants number 16/15524-3,15/14228-9,and CNPq grant 302645/2015-2.The authors would also like to thank the anonymous referees for their advice and criticisms.
文摘Semantic web technologies have become a popular technique to apply meaning to unstructured data.They have been infrequently applied to problems within the agricultural domain when compared to complementary domains.Despite this lack of application,agriculture has a large number of semantic resources that have been developed by large NGOs such as the Food and Agriculture Organization(FAO).This survey is intended to motivate further research in the application of semantic web technologies for agricultural problems,by making available a self contained reference that provides:a comprehensive review of preexisting semantic resources and their construction methods,data interchange standards,as well as a survey of the current applications of semantic web technologies.
文摘Blockchains and smart contracts are gaining momentum as enabling technologies for a wide set of applications where data distribution and sharing among decentralized infrastructures is required.In this work,we present a distributed application developed using blockchain technologies that allows individuals and health insurance organizations to come into agreement during the implementation of the healthcare insurance policies in each contract.For this purpose,health standards and semantic web technologies were used for the formal expression of both the insured individual's data and contract terms.Accordingly,a fine-grained data access policy was applied for evaluating contract terms on the basis of relevant data captured in healthcare settings.A prototype was implemented involving the development of several different smart contracts for the Ethereum platform as well as the necessary visual environment for accessing them.The developed system validates various features related to blockchain and smart contract features that are briefly discussed in this work,part of which can be mitigated or resolved through the use of a private permissioned blockchain.The application of well-established techniques for potential malfunctions of external services could also boost the security of the system and prevent it from potential attacks.
文摘Purpose-The success of web services involved the adoption of this technology by different service providers through the web,which increased the number of web services,as a result making their discovery a tedious task.The UDDI standard has been proposed for web service publication and discovery.However,it lacks sufficient semantic description in the content of web services,which makes it difficult to find and compose suitable web services during the analysis,search,and matching processes.In addition,few works on semantic web services discovery take into account the user’s profile.The purpose of this paper is to optimize the web services discovery by reducing the search space and increasing the number of relevant services.Design/methodology/approach-The authors propose a new approach for the semantic web services discovery based on the mobile agent,user profile and metadata catalog.In the approach,each user can be described by a profile which is represented in two dimensions:personal dimension and preferences dimension.The description of web service is based on two levels:metadata catalog and WSDL.Findings-First,the semantic web services discovery reduces the number of relevant services through the application of matching algorithm“semantic match”.The result of this first matching restricts the search space at the level of UDDI registry,which allows the users to have good results for the“functional match”.Second,the use of mobile agents as a communication entity reduces the traffic on the network and the quantity of exchanged information.Finally,the integration of user profile in the service discovery process facilitates the expression of the user needs and makes intelligible the selected service.Originality/value-To the best knowledge of the authors,this is the first attempt at implementing the mobile agent technology with the semantic web service technology.
文摘Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,and journals.From such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies.Keyword-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant results.They are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given word.Therefore,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the data.An ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal cases.The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries.In this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two experiments.In the first experiment,we implemented a keyword-based textual IR systemusing Apache Lucene.In the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries.The keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.
文摘Multimedia Sensor Networks(MSNs)have enhanced the ability to analyze the environment and provide responses based on its current status.Generally,MSNs are composed of scalar and multimedia sensors that have fixed locations.However,given the advancement of smart mobile device technologies,it is currently possible to dynamically integrate mobile sensors into MSNs.In this paper,we propose a formal platform to manage MSNs and the data gathered from them to detect complex events.Our main contributions include:M^(2)SSN-Onto,a Mobile and Multimedia Semantic Sensor Networks Ontology;Py-CEMiD,an engine for detecting complex events and generate reactions to them;a mobile device location engine to locate mobile sensors;and a proof-of-concept in the context of detecting emergency situations in smart buildings.Several scenarios are validated for emergency events,combining simulated sensor measurements with real measurements of mobile devices.Results show complex events can be detected in near real time(less than 1 s).
基金This work is financially supported by the Ministry of Earth Science(MoES),Government of India,(Grant.No.MoES/36/OOIS/Extra/45/2015),URL:https://www.moes.gov.in。
文摘The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A2C1011198).
文摘Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.
基金supported by National Natural Science Foundation of China(71402157)the Natural Science Foundation of Guangdong Province,China(2014A030313753)+2 种基金CityU Start-up(7200399)the Center for Adaptive Super Computing Software-Multi Threaded Architectures(CASS-MT)at the U.S.Department of Energy’s Pacific Northwest National LaboratoryPacific Northwest National Laboratory Is Operated by Battelle Memorial Institute(Contract DE-ACO6-76RL01830)
文摘In the past decade, existing and new knowledge and datasets have been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea via studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three gene ontology slims(plant,yeast, and candida, among which the latter two belong to the same kingdom — fungi) using four popular measures commonly applied to biomedical ontologies(Resnik, Lin, Jiang-Conrath,and Sim Rel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performances of JiangConrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by 1) consistently showing that yeast and candida are more similar(as compared to plant) at different scales, and 2) small deviations of the similarity values after excluding a majority of nodes from several lower scales.This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering.
基金This research was supported by the National Natural Science Foundation of China(NSFC)(Grant No.61772280).
文摘In this paper,the research advances of ontology and its application are reviewed firstly.With the development of ontology technology,subject-oriented web information retrieval technology combining ontology has been becoming one of the hot scientific issues.The innovative method of the semantic web technology combined with the traditional information retrieval technology is put forward,and the related algorithm based on ontology for judging the relevancy with different topics is also represented,and has proved to be effective in given experiments.
文摘Mostly, cloud agreements are signed between the consumer and the provider using online click-through agreements. Several issues and conflicts exist in the negotiation of cloud agreement terms due to the legal and ambiguous terms in Service Level Agreements (SLA). Semantic knowledge applied during the formation and negotiation of SLA can overcome these issues. Cloud SLA negotiation consists of numerous activities such as formation of SLA templates, publishing it in registry, verification and validation of SLA, monitoring for violation, logging and reporting and termination. Though these activities are interleaved with each other, semantic synchronization is still lacking. To overcome this, a novel SLA life cycle using semantic knowledge to automate the cloud negotiation has been formulated. Semantic web platform using ontologies is designed, developed and evaluated. The resultant platform increases the task efficiency of the consumer and the provider during negotiation. Precision and recall scores for Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) SLAs were calculated. And it reveals that applying semantic knowledge helps the extraction of meaningful answers from the cloud actors.
