Efficient semantic web service matching using common ontology

To solve the bottleneck problem in centralized service discovery methods,a novel architecture based on domain ontology for semantic service discovery is proposed.This distributed architecture can adjust the domain partition and allocate system resources automatically.The characteristics of this mechanism are analyzed,including scalability,self-organization and adaptability.In this mechanism,semantic web service discovery is separated into two parts.First,under balance tree topology,registry proxy can rapidly forward requests to the objective registry center,and avoid the bottleneck problem.Secondly,a semantic distance based service matching algorithm is proposed to promote the effect of service searching.The results of simulation experiments show that the proposed mechanism can serve as a scalable solution for semantic web service publication and discovery.And the improved matching algorithm has higher recall and precision than other algorithms.

Knowledge Representation and Semantic Inference of Process Based on Ontology and Semantic Web Rule Language

The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

Evaluation of Multistrategy Classifiers for Heterogeneous Ontology Matching On the Semantic Web

On the semantic web, data interoperability and ontology heterogeneity are becoming ever more important issues. To resolve these problems, multiple classification methods can be used to learn the matching between ontologies. The paper uses the general statistic classification method to discover category features in data instances and use the first-order learning algorithm FOIL to exploit the semantic relations among data instances. When using multistrategy learning approach, a central problem is the evaluation of multistrategy classifiers. The goal and the conditions of using multistrategy classifiers within ontology matching are different from the ones for general text classification. This paper describes the combination rule of multiple classifiers called the Best Outstanding Champion, which is suitable for heterogeneous ontology mapping. On the prediction results of individual methods, the method can well accumulate the correct matching of alone classifier. The experiments show that the approach achieves high accuracy on real-world domain.

Ontology and metadata for online learning resource repository management based on semantic web

An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.

Web Semantic and Ontology

Ontologies have become a popular research topic in many communities. In fact, ontology is a main component of this research;therefore, the definition, structure and the main operations and applications of ontology are provided. Web content consists mainly of distributed hypertext and hypermedia, and is accessed via a combination of keyword based search and link navigation. Hence, the ontology can provide a common vocabulary, and a grammar for publishing data, and can supply a semantic description of data which can be used to preserve the ontologies and keep them ready for inference. This paper provides basic concepts of semantic web, and defines the structure and the main applications of ontology.

Description logics for fuzzy ontologies on semantic web

To enable representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web, a new fuzzy extension of description logics called the fuzzy description logics with comparison expressions （FCDLs） is presented. The syntax and semantics of FCDLs are formally defined, and the forms of axioms and assertions in FCDLs knowledge bases are specified. FCDLs combine both fuzzy concepts from the fuzzy description logics （FDLs） and cut concepts from the extended fuzzy description logics （EFDLs） in the same theory. Furthermore, cut concepts are extended into comparison cut concepts in FCDLs to represent comparison expressions between fuzzy membership degrees, which are often used in practice but not supported by the other fuzzy extensions of description logics. FCDLs have more expressive power than FDLs and EFDLs, and are able to represent expressive fuzzy knowledge and to perform reasoning tasks based on them. Therefore, FCDLs can enable representation and reasoning for fuzzy ontologies with expressive fuzzy knowledge on the semantic web.

Novel method for searching ontologies on semantic web

In order to solve the problem of information retrieval on the semantic web, a new semantic information retrieval （SIR） model for searching ontologies on the semantic web is proposed. First, SIR transformed domain ontologies into global ontologies. Then semantic index terms were extracted from these global ontologies. Based on semantic index terms, logical inferences can be performed and the logical views of the concept can be obtained. These logical views represent the expanded meaning of the concept. Using logical views, SIR can perform the information retrieval and inferences based on the semantic relationships in the documents, not only on the syntactic analysis of the documents. SIR can significantly enhance the recall and precision of the information retrieval by the semantic inference. Finally, the practicability of the SIR model is analyzed.

Semantic analysis approach for construction of OWL ontology

A semantic analysis approach is proposed, by which semantic relationships between concepts are identified and defined, and then mapped or transformed to OWL （web ontology language） ontology. The most common abstractions （namely, inclusion, aggregation and association） and their implication in ontology are discussed; then the OWL implementation for three abstractions are analyzed and illustrated. Taxonomies, constraints on properties for each class, and the relations between taxonomies in OWL ontology are established after all the semantic relationships are identified and described. This research is the basis for the development of the ontology conceptual model （OCM） and the mapping from OCM to OWL ontology.

Bridge Ontology: A Multi-Ontologies-Based Approach for Semantic Annotation

Representing the relationships between ontologies is the key problem of semantic annotations based on multi-ontologies. Traditional approaches only had the ability of denoting the simple concept subsumption relations between ontologies. Through analyzing and classifying the relationships between ontologies, the idea of bridge ontology was proposed, which had the powerful capability of expressing the complex relationships between concepts and relationships between relations in multi-ontologies. Meanwhile, a new approach employing bridge ontology was proposed to deal with the multi-ontologies-based semantic annotation problem. The bridge ontology is a peculiar ontology, which can be created and maintained conveniently, and is effective in the multi-ontologies-based semantic annotation. The approach using bridge ontology has the advantages of low-cost, scalable, robust in the web circumstance, and avoiding the unnecessary ontology extending and integration. Key words semantic web - bridge ontology - multi-ontologies - semantic annotation CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024). National Grand Fundamental Research 973 Program of China (2002CB312000), National Re-search Foundation for the Doctoral Program of Higher Education of China (20020286004)Biography: WANG Peng (1977-), male, Ph.D candidate, research direction: semantic web, ontology, and knowledge representation on the Web.

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.

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。

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.

An XPath-based OWL storage model for effective ontology management in Semantic Web environment

With the rapid growth of the Web, the volume of information on the Web is increasing exponentially. However, information on the current Web is only understandable to humans, and this makes precise information retrieval difficult. To solve this problem, the Semantic Web was proposed. We must use ontology languages that can assign data the semantics for realizing the Semantic Web. One of the representative ontology languages is the Web ontology language OWL, adopted as a recommen-dation by the World-Wide Web Consortium (W3C). OWL includes hierarchical structural information between classes or prop-erties. Therefore, an efficient OWL storage model that considers a hierarchical structure for effective information retrieval on the Semantic Web is required. In this paper we suggest an XPath-based OWL storage (XPOS) model, which includes hierarchical information between classes or properties in XPath form, and enables intuitive and effective information retrieval. Also, we show the comparative evaluation results for the performance of the XPOS model, Sesame, and the XML file system-based storage (XFSS) model, in terms of query processing and ontology updating.

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.

Heuristic web service composition method based on domain ontology

In order to realize automatic web service composition, a heuristic web service composition method based on domain ontology is proposed. First, this method integrates the domain ontology and the artificial intelligence （AI） planning algorithm. Then, it uses the domain ontology and its reasoning capability to infer the semantic relationship among parameters. Finally, it transforms the web service composition problem into the planning problem based on the AI planning heuristic algorithm. The preliminary experimental results show that the above method compensates for the lack of semantics in the previous AI planning method and it can satisfy the requirements of quality and efficiency of composition, thus generating composite web services according to customer requirements automatically and efficiently.

Fishery Web Service Composition Method Based on Ontology

Various fishery information systems have been developed in different times and on different platforms. Wet） service application composition is crucial in the sharing and integration of fishery data and information. In the present paper, a heuristic web service composition method based on fishery ontology is presented, and the proposed web services are described. Ontology reasoning capability was applied to generate a service composition graph. The heuristic function was introduced to reduce the searching space. The experimental results show that the algorithm used considers the services semantic similarity and adiusts web service composition plan by dynamically relying on the empirical data.

Construction of Domain Web Classification Ontology Based on Suggested Upper Merged Ontology

Aiming at the problem of merging heterogeneous semantic taxonomy emerged in Web information integration, a method of building Web classification ontology （WCO） has been proposed. A WCO that is logically consistent with the suggested upper merged ontology （SUMO） is defined, together with axioms needed to classify Web pages. WCO can be used as a foundation of merging heterogeneous semantic taxonomy, and could be used to support Web information integration and classification based Web information retrieval.

Application of Ontology in the Web Information Retrieval

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.