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.

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.

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.

Capability matchmaking of semantic web services with preconditions and effects

In order to solve the problem that the current matchmaking methods for semantic web service mainly focus on the matchmaking of IO （inputs, outputs） descriptions which may result in one-sidedness, a description-logic-based IOPE （inputs, outputs, preconditions, effects） description and matchmaking method is proposed for semantic web service. The description logic concept is used to annotate service IO and the description logic assertion is employed to describe service PE（preconditions, effects）. TBox subsumption checking is used to measure the subsumption relationship between IO descriptions of service request and advertising; ABox consistency checking is used for checking the logical implication between PE descriptions of service request and advertising. Based upon the logical implication, four kinds of PE matching degrees are proposed to measure and compare the pros and cons of the results of matchmaking. They are the exact, perfect, side-effect and common match. Experiments show that the method has a higher precision rate under the same recall rate compared with the existing method.

Reducing semantic bias of annotations for semantic web service discovery

In order to improve the effectiveness of semantic web service discovery, the semantic bias between an interface parameter and an annotation is reduced by extracting semantic restrictions for the annotation from the description context and generating refined semantic annotations, and then the semantics of the web service is refined. These restrictions are dynamically extracted from the parsing tree of the description text, with the guide of the restriction template extracted from the ontology definition. New semantic annotations are then generated by combining the original concept with the restrictions and represented via refined concept expressions. In addition, a novel semantic similarity measure for refined concept expressions is proposed for semantic web service discovery. Experimental results show that the matchmaker based on this method can improve the average precision of discovery and exhibit low computational complexity. Reducing the semantic bias by utilizing restriction information of annotations can refine the semantics of the web service and improve the discovery effectiveness.

Kullback-Leibler distance based concepts mapping between web ontologies

A Kullback-Leibler（KL）distance based algorithm is presented to find the matches between concepts from different ontologies. First, each concept is represented as a specific probability distribution which is estimated from its own instances. Then, the similarity of two concepts from different ontologies is measured by the KL distance between the corresponding distributions. Finally, the concept-mapping relationship between different ontologies is obtained. Compared with other traditional instance-based algorithms, the computing complexity of the proposed algorithm is largely reduced. Moreover, because it proposes different estimation and smoothing methods of the concept distribution for different data types, it is suitable for various concepts mapping with different data types. The experimental results on real-world ontology mapping illustrate the effectiveness of the proposed algorithm.

New method for query answering in semantic web

To promote the efficiency of knowledge base retrieval based on description logic, the concept of assertional graph （AG）, which is directed labeled graph, is defined and a new AG-based retrieval method is put forward. This method converts the knowledge base and query clause into knowledge AG and query AG by making use of the given rules and then makes use of graph traversal to carry out knowledge base retrieval. The experiment indicates that the efficiency of this method exceeds, respectively, the popular RACER and KAON2 system by 0.4% and 3.3%. This method can obviously promote the efficiency of knowledge base retrieval.

Configuration knowledge modeling of customizable products based on semantic web technologies

In order to solve the problem of modeling product configuration knowledge at the semantic level to successfully implement the mass customization strategy, an approach of ontology-based configuration knowledge modeling, combining semantic web technologies, was proposed. A general configuration ontology was developed to provide a common concept structure for modeling configuration knowledge and rules of specific product domains. The OWL web ontology language and semantic web rule language （SWRL） were used to formally represent the configuration ontology, domain configuration knowledge and rules to enhance the consistency, maintainability and reusability of all the configuration knowledge. The configuration knowledge modeling of a customizable personal computer family shows that the approach can provide explicit, computerunderstandable knowledge semantics for specific product configuration domains and can efficiently support automatic configuration tasks of complex products.

Supporting web services reuse by semantic service component and composition pattern

Due to the fact that the existing web service description methods cannot address the issue of service reuse of various levels of granularity, the concept of service component is introduced, which packages together web services and choreography, and their operations and properties are presented in a consistent and uniform manner. Service components are published externally as normal web services and can thus be employed by webbased applications. In order to improve reusability and testability of service components, the concept of composition pattern is also proposed, which presents the relationships among service components. The relationships and relationship compositions have a rigorous semantic, so that composite components can be validated at the configuration stage. The composition patterns support to integrate service components of various levels of granularity. Experience indicates that the application assembly can effectively be conducted by understanding, selecting, and reusing components easily.

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.

Semantic web-based networked manufacturing knowledge retrieval system

To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of tools for supporting the sharing of knowledge and promoting NM collaboration. A 5-tuple based semantic information retrieval model is proposed, which includes the interoperation on the semantic layer, and a test process is given for this model. The recall ratio and the precision ratio of manufacturing knowledge retrieval are proved to be greatly improved by evaluation. Thus, a practical and reliable approach based on the semantic web is provided for solving the correlated concrete problems in regional networked manufacturing.

Security integrated framework for semantic web based on social intelligence

An integrated security framework for a semantic web is proposed based on the social intelligence of an individual＇s avoiding harm and preserving transaction logic-integrity. The framework extends the semantic web model and controls the dynamic security of semantic web services, such as trust, logic and reasoning. It includes four layers, that is, a trust entrance layer, a social intelligence layer, a transaction layer, and a TCP/IP security protocols layer. The trust entrance layer deals with trustable features from users. Social intelligence layer is responsible for logical questions for a semantic web. The transaction layer carries out transaction reasoning. And the TCP/IP security protocols layer ensures security communication. These layers can cooperate to build closed-security-ring with different security grades. The integrated security framework provides an integrated security method for semantic web flow so that it is universal for various semantic web technologies.

Ontology mapping approach using web search engine

A new mapping approach for automated ontology mapping using web search engines （such as Google） is presented. Based on lexico-syntactic patterns, the hyponymy relationships between ontology concepts can be obtained from the web by search engines and an initial candidate mapping set consisting of ontology concept pairs is generated. According to the concept hierarchies of ontologies, a set of production rules is proposed to delete the concept pairs inconsistent with the ontology semantics from the initial candidate mapping set and add the concept pairs consistent with the ontology semantics to it. Finally, ontology mappings are chosen from the candidate mapping set automatically with a mapping select rule which is based on mutual information. Experimental results show that the F-measure can reach 75% to 100% and it can effectively accomplish the mapping between ontologies.

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.

Linking a Game Engine Environment to Architectural Information on the Semantic Web

Because of the importance of graphics and information within the domain of architecture, engineering and construction （AEC）, an appropriate combination of visualization technology and information management technology is of utter importance in the development of appropriately supporting design and construction applications. Virtual environments, however, tend not to make this information available. The sparse number of applications that present additional information furthermore tend to limit their scope to pure construction information and do not incorporate information from loosely related knowledge domains, such as cultural heritage or architectural history information. We therefore started an investigation of two of the newest developments in these domains, namely game engine technology and semantic web technology. This paper documents part of this research, containing a review and comparison of the most prominent game engines and documenting our architectural semantic web. A short test-case illustrates how both can be combined to enhance information visualization for architectural design and construction.

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.

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.