Linguistic Variable Ontologies Research Articles

Fuzzy Knowledge Representation Based on Fuzzy Linguistic Variable Ontology and SWRL on the Semantic Web

Ontology is adopted as a standard for knowledge representation on the Semantic Web, and Ontology Web Language (OWL) is used to add structure and meaning to web applications. In order to share and resue the fuzzy knowledge on the Semantic Web, we propose the fuzzy linguistic variables ontology (FLVO), which utilizes ontology to represent formally the fuzzy linguistic variables and defines the semantic relationships between fuzzy concepts. Then fuzzy rules are described in Semantic Web Rule Language (SWRL) on the basis of FLVO model. Taking a sample case for students’ performance in physics for example, the fuzzy rule management system is built by using the tool protégé and SWRLTab, which shows that this research enables distributed fuzzy applications on the Semantic Web.

Semantic Retrieval Based on SPARQL and Fuzzy Ontology for Electronic Commerce

Information retrieval is the important work for Electronic Commerce. Ontology-based semantic retrieval is a hotspot of current research. In order to achieve fuzzy semantic retrieval, this paper proposes an approach using Resource Description Framework (RDF) and fuzzy ontology. First, we apply RDF/RDFS data model to represent e-commerce information on the Semantic Web. Then, introducing new data type referred as fuzzy linguistic variables to RDF data model, the semantic query expansion in SPARQL query language is constructed by order relation, equivalence relation and inclusion relation between fuzzy concepts defined in linguistic variable ontologies. Examples show that this research facilitates the semantic retrieval through fuzzy concepts for Electronic Commerce on the Semantic Web.

Knowledge Modeling of Product Data Based on Fuzzy Ontology

Ontology is the basis of knowledge modeling on the Semantic Web, and fuzzy ontology is an extension of domain ontology for solving the uncertainty problems. Ontology-base knowledge modeling of product data management (PDM) is meaningful for product design and trade etc. In order to handle fuzzy phenomenon and uncertainty of product knowledge, this paper proposes a series of fuzzy ontology models that consists of fuzzy domain ontology and fuzzy linguistic variable ontologies. Then, a fuzzy ontology framework is presented, including three parts: concepts, properties of concepts and values of properties. The application, which uses fuzzy ontology to model product knowledge, shows that these models can overcome the localization of other fuzzy ontology models, and this research facilitates the fuzzy knowledge sharing and reuse for PDM on the Semantic Web.

Fuzzy Semantic Retrieval for Traffic Information Based on Fuzzy Ontology and RDF on the Semantic Web

Normal 0 7.8 磅 0 2 false false false MicrosoftInternetExplorer4 Information retrieval is the essential task for Traffic Information Service System in Intelligent Transportation Systems (ITS). There a lot of fuzzy traffic information derived from human factor. To achieve fuzzy semantic retrieval, this paper proposes an approach using Resource Description Framework (RDF) and fuzzy ontology. First, we apply RDF data model to represent traffic information on the Semantic Web. Then we present fuzzy linguistic variable ontology models and its formal representation with RDF. Introducing new data type referred as fuzzy linguistic variables to RDF data model, the semantic query expansions in SeRQL query language are constructed by order relation, equivalence relation, inclusion relation and complement relation between fuzzy concepts defined in linguistic variable ontologies. Examples show that the extended query can return all results which satisfy research requirement at semantic level without upgrading current main search algorithm, and this research facilitates the semantic retrieval of traffic information through fuzzy concepts for ITS on the Semantic Web.

Semantic Information Retrieval Based on Fuzzy Ontology for Electronic Commerce

Information retrieval is the important work for Electronic Commerce. Ontology-based semantic retrieval is a hotspot of current research. In order to achieve fuzzy semantic retrieval, this paper applies a fuzzy ontology framework to information retrieval system in E-Commerce. The framework includes three parts: concepts, properties of concepts and values of properties, in which property’s value can be either standard data types or linguistic values of fuzzy concepts. The semantic query expansions are constructed by order relation, equivalence relation, inclusion relation, reversion relation and complement relation between fuzzy concepts defined in linguistic variable ontologies with Resource Description Framework (RDF). The application to retrieve customer, product and supplier information shows that the framework can overcome the localization of other fuzzy ontology models, and this research facilitates the semantic retrieval of information through fuzzy concepts on the Semantic Web.