Ontology-Based News Linking for Semantic Temporal Queries

Daily newspapers publish a tremendous amount of information disseminated through the Internet.Freely available and easily accessible large online repositories are not indexed and are in an un-processable format.The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed.There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora,especially for South Asian languages.The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.Lack of indexing,primarily semantic indexing of the daily news items,makes news items impracticable for any querying.Moreover,the most straightforward search facility does not support these unindexed news resources.Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages,i.e.,Urdu.The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler.The corpus is then semantically linked and annotated with daily news items.Two techniques are proposed for image annotation,free annotation and fixed cross examination annotation.The second technique got higher accuracy.Build news ontology in protégéusing OntologyWeb Language(OWL)language and indexed the annotations under it.The application is also built and linked with protégéso that the readers and journalists have an interface to query the news items directly.Similarly,news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.

Semantic QoS Ontology and Semantic Service Ranking Approach for IoT services

Due to a rapid increase in the number of functionally equivalent web services at open and dynamic Io T service environment,Qo S has become a major discrimination factor to reflect the user's expectation and experience of using a service.There are different languages and models for expressing Qo S advertisements and requirements among service providers and consumers.Therefore,it leads to the issues of semantic interoperability of Qo S information and semantic similarity match between a semantic description of the service being requested by the service consumer,and a formal description of the service being offered by the service provider.In this paper,we propose a hierarchical two-layer semantic Qo S ontology to promote the description and declaration of Qo S-based service information in detail for any domain and application.And,we develop a semantic matchmaking algorithm to compare the web services according to their Qo S information and adopt analytical hierarchy process( AHP) to make decision for the ranked services depending on the Qo S criteria.The comparison study and experimental result show that our proposed system is superior to other service ranking approaches.

Ontology-Based Semantic Multi-agent Framework for Micro-grids with Cyber Physical Concept

The distributed hierarchical control based on multi-agent system(MAS) is the main control method of micro-grids.By allowing more flexible interactions between computing components and their physical environments,cyber physical system(CPS) presents a new approach for the distributed hierarchical engineering system,with micro-grids included.The object of this paper is to integrate the CPS concept with MAS technology and propose a new control framework for micro-grids.With the analysis of the operating mode and control method of micro-grids,the cyber physical control concepts of ontologybased semantic agent are discussed.Then an MAS-based architecture of cyber physical micro-grid system and an intelligent electronic device(IED) function structure are proposed.Finally,in order to operate and test the cyber physical micro-grid concept,an integrated simulation model is presented.

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。

Semantic analysis and retrieval of spatial data based on the uncertain ontology model in Digital Earth

Metadata are the information about and description of data.In Digital Earth,metadata become variant and heterogeneous with many uncertainties.This paper studies uncertain features in the generation and application of metadata,and two types of uncertainties(incomplete and imprecise)are described based on semantic quantitative measurement method semantic relationship quantitative measurement based on possibilistic logic and probability statistic(SRQ-PP).Moreover,in the case study,we apply two types of quantitative measurements based on SRQ-PP to describe incomplete(uncertain)knowledge and imprecise(vague)information separately in spatial data service retrieval,which in turn is helpful to identify additional potential data resources and provide a quantitative analysis of the results.

A Semantic Ontology Structure-based Approach for Retrieving Similar Medical Images

Radiology doctors perform text-based image retrieval when they want to retrieve medical images.However,the accuracy and efficiency of such retrieval cannot keep up with the requirements.An innovative algorithm is being proposed to retrieve similar medical images.First,we extract the professional terms from the ontology structure and use them to annotate the CT images.Second,the semantic similarity matrix of ontology terms is calculated according to the structure of the ontology.Lastly,the corresponding semantic distance is calculated according to the marked vector,which contains different annotations.We use 120 real liver CT images(divided into six categories)of a top three-hospital to run the algorithm of the program.Result shows that the retrieval index"Precision"is 80.81%,and the classification index"AUC(Area Under Curve)"under the"ROC curve"(Receiver Operating Characteristic)is 0.945.

Dynamic knowledge graph approach for modelling the decarbonisation of power

This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data representation of infrastructure data,socio-demographic data,areal attributes like demand,and models describing power systems.The knowledge graph links the data with a hierarchical representation of administrative regions,supporting geospatial queries to retrieve information about the population within the vicinity of a power plant,the number of power plants,total generation capacity,and demand within specific areas.Computational agents were developed to operate on the knowledge graph.The agents performed tasks including data uploading,updating,retrieval,processing,model construction and scenario analysis.A derived information framework was used to track the provenance of information calculated by agents involved in each scenario.The knowledge graph was populated with data describing the UK power system.Two alternative models of the transmission grid with different levels of structural resolution were instantiated,providing the foundation for the power system simulation and optimisation tasks performed by the agents.The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.