Main Reference PaperA Personalized User Profiles Ontology Model for Web Information Gathering”, IEEE Transactions on Knowledge and Data Engineering April 2011
  • This paper presents a personalized ontology model for knowledge representation and reasoning over user information. The model learns ontological the user information both from a global information base and user local instance repositories. This model simulates the concept models of the user by employing personalized ontologies and improves web information gathering performance. Based on fuzzy class the Ontology mining is proposed.

+ Description
  • This paper presents a personalized ontology model for knowledge representation and reasoning over user information. The model learns ontological the user information both from a global information base and user local instance repositories. This model simulates the concept models of the user by employing personalized ontologies and improves web information gathering performance. Based on fuzzy class the Ontology mining is proposed.

  • Ontological user profiles are gathering the user’s personalized web information.

  • This model learns ontological user profiles from both a world knowledge base and user local instance repositories.

  • LIR is a user’s personal collection of information items.

  • It also has multidimensional ontology mining method. Problem Statement: Global analysis is limited by the quality of the used information base. Local analysis suffers from inefficient and ineffectiveness at capturing formal user information. The accuracy matching of semantic relation is not used for complicated taxonomy in ontology.

+ Aim & Objectives
  • Ontological user profiles are gathering the user’s personalized web information.

  • This model learns ontological user profiles from both a world knowledge base and user local instance repositories.

  • LIR is a user’s personal collection of information items.

  • It also has multidimensional ontology mining method. Problem Statement: Global analysis is limited by the quality of the used information base. Local analysis suffers from inefficient and ineffectiveness at capturing formal user information. The accuracy matching of semantic relation is not used for complicated taxonomy in ontology.

  • The contribution is to propose the ontology mining algorithm based on fuzzy class. Ontology consists of primitive classes and compound classes. The primitive classes are the smallest concepts that cannot be assembled from other classes. The compound classes can be constructed from a set of primitive classes.

+ Contribution
  • The contribution is to propose the ontology mining algorithm based on fuzzy class. Ontology consists of primitive classes and compound classes. The primitive classes are the smallest concepts that cannot be assembled from other classes. The compound classes can be constructed from a set of primitive classes.

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, J2EE

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, J2EE

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

+ Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-Depending on the complexity of the project and requirements.

+ Order To Delivery
  • No Readymade Projects-Depending on the complexity of the project and requirements.

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