World-Wide Semantic Web of Agriculture Knowledge

The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.

SemTrust: A Semantic Reputation System in P2P-Based Semantic Web

A reputation mechanism is introduced in P2P- based Semantic Web to solve the problem of lacking trust. It enables Semantic Web to utilize reputation information based on semantic similarity of peers in the network. This approach is evaluated in a simulation of a content sharing system and the experiments show that the system with reputation mechanism outperforms the system without it.

Review of Semantic Web Service Composition Based on OWL-S

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.

Using Semantic Web Technologies to Improve the Extract Transform Load Model

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.

An Automatically Filtering Blacklist Model of Social Network Based on Semantic Web

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.

Machine Learning Meets the Semantic Web

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。

Knowledge Model for Electric Power Big Data Based on Ontology and Semantic Web

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.

Hybrid QoS-aware semantic web service composition strategies

Hybrid QoS model which consists of certain and uncertain expressions has strong power of semantic QoS description. For solving the hybrid QoS-aware semantic web service composition problem, this paper presents an Uncertain Multi-attribute decision making based Composition algorithm （UMC）. The UMC includes two parts. First, UMC-Core can be used to synthetically evaluate the hybrid service quality information. Second, UMC-DH （Distributed and Heuristic framework for UMC） aims at enhancing the run-time performance of UMC when the problem space increases. The simulation results show that the UMC has lower execution cost, higher approximation ratio and success ratio than other similar approaches.

Towards knowledge-based geovisualisation using Semantic Web technologies:a knowledge representation approach coupling ontologies and rules

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.

Efficient Composition of Semantic Web Services with End-to-End QoS Optimization

The efficiency of QoS-aware service composition is important since most service composition problems are known to be NP-hard. With the growing number of web services, service composition is like a decision problem on selecting services or/and execution plans to satisfy the users＇ end-to-end QoS requirements （e.g. response time, throughput）. Composite services with the same functionality may have dif-ferent execution plans, which may cause different end-to-end QoS. This paper presents a model combining semantic data-links and QoS, which leads to an efficient approach to automatic construction of a composite service with optimal end-to-end QoS. The approach is based on a greedy algorithm to select both services and execution plans for composite services. Empirical and theoretical analyses of the approach show that its time complexity is O（mn2） for a repository with n services and an ontology with m concepts. Moreover, the approach increases linearly in time when using an index to search services in the repository. Tests with a repository with 20 000 services and an ontology with 300 000 concepts show that the algorithm significantly outperforms current existing algorithms in terms of composition efficiency while achieving optimal end-to-end QoS.

The framework of a geospatial semantic web-based spatial decision support system for Digital Earth

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.

Summarizing Vocabularies in the Global Semantic Web

In the Semantic Web, vocabularies are defined and shared among knowledge workers to describe linked data for scientific, industrial or daily life usage. With the rapid growth of online vocabularies, there is an emergent need for approaches helping users understand vocabularies quickly. In this paper, we study the summarization of vocabularies to help users understand vocabularies. Vocabulary summarization is based on the structural analysis and pragmatics statistics in the global Semantic Web. Local Bipartite Model and Expanded Bipartite Model of a vocabulary are proposed to characterize the structure in a vocabulary and links between vocabularies. A structural importance for each RDF sentence in the vocabulary is assessed using link analysis. Meanwhile, pragmatics importance of each RDF sentence is assessed using the statistics of instantiation of its terms in the Semantic Web. Summaries are produced by extracting important RDF sentences in vocabularies under a re-ranking strategy. Preliminary experiments show that it is feasible to help users understand a vocabulary through its summary.

Enhancing the functionality of augmented reality using deep learning,semantic web and knowledge graphs:A review

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.

How to derive a geovisualization from an application data model: an approach based on Semantic Web technologies

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.

Semantics all the way down:the Semantic Web and open science in big earth data

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.

A survey of semantic web technology for agriculture

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.

Using blockchain and semantic web technologies for the implementation of smart contracts between individuals and health insurance organizations

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.

Bootstrapping Object Coreferencing on the Semantic Web

An object on the Semantic Web is likely to be denoted with several URIs by different parties. Object core-ferencing is a process to identify ＂equivalent＂ URIs of objects for achieving a better Data Web. In this paper, we propose a bootstrapping approach for object coreferencing on the Semantic Web. For an object URI, we firstly establish a kernel that consists of semantically equivalent URIs from the same-as, （inverse） functional properties and （max-）cardinalities, and then extend the kernel with respect to the textual descriptions （e.g., labels and local names） of URIs. We also propose a trustworthiness-based method to rank the coreferent URIs in the kernel as well as a similarity-based method for ranking the URIs in the extension of the kernel. We implement the proposed approach, called ObjectCoref, on a large-scale dataset that contains 76 million URIs collected by the Falcons search engine until 2008. The evaluation on precision, relative recall and response time demonstrates the feasibility of our approach. Additionally, we apply the proposed approach to investigate the popularity of the URI alias phenomenon on the current Semantic Web.

An automatic subdigraph renovation plan for failure recovery of composite semantic Web services

A Web service-based system never fulfills a user＇s goal unless a failure recovery approach exists. It is inevitable that several Web services may either perish or fail before or during transactions. The completion of a composite process relies on the smooth execution of all constituent Web services. A mediator acts as an intermediary between providers and consumers to monitor the execution of these services. If a service fails, the mediator has to recover the whole composite process or else jeopardize achieving the intended goals. The atomic replacement of a perished Web service usually does not apply because the process of locating a matched Web service is unreliable. Even the system cannot depend on the replacement of the dead service with a com- posite service. In this paper, we propose an automatic renova- tion plan for failure recovery of composite semantic services based on an approach of subdigraph replacement. A replacement subdigraph is posed in lieu of an original subdigraph, which includes the failed service. The replacement is done in two separate phases, ofltine and online, to make the recovery faster. The ofitine phase foresees all possible subdigraphs, pre-calculates them, and ranks several possible replacements. The online phase compensates the unwanted effects and executes the replacement subdigraph in lieu of the original subdigraph. We have evaluated our approach during an experiment and have found that we could recover more than half of the simulated failures. These achievements show a significant improvement compared to current approaches