24-02-2015, 01:07 PM
please provide ppts. we are unable to download anything from your site. please provide ease of use to the users
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ppt on personalized ontology model for web information gathering
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please provide ppts. we are unable to download anything from your site. please provide ease of use to the users
12-04-2017, 12:00 PM
Ontology is the model of description and formalization of knowledge, which is widely used to represent user profiles in the collection of personalized information on the web. When representing user profiles, many models have used only knowledge of a global knowledge base or local user information. In this paper we propose an ontological model of personalization for the representation of knowledge and reasoning about user profiles. It will contain user profiles of both the global knowledge base and the repository of local user instances. The ontological model is evaluated in comparison to the reference models in the compilation of information on the web. The user profile represents the conceptual models by the user when collecting web information. A concept model is owned by the users and is generated from there background knowledge. This conceptual model can not be tested in laboratories; Many web ontologists have observed it in a user behavior the results show that this ontology model is successful.
The amount of information available on the Internet has increased dramatically. Collecting useful information from the web becomes a challenging problem for users. The current information collection system on the Web attempts to satisfy the requirements of users. It will capture your information needs. For this purpose, user profiles are created for the background description of the user. User profiles represent the conceptual models owned by users when collecting web information. The conceptual model is implicitly owned by users. Generated from there the background knowledge. Although this concept model can not be tested in laboratories ontologists have observed it in user behavior. When users read a document they can easily determine if it is of their interest or relevance to them, a judgment that arises from implicit conceptual models. If the user concept model can be simulated, a superior representation of the user profiles can be constructed. To simulate these models, Ontologies uses a model of description and formulation of web knowledge in the collection of personalized information on the Internet. These ontologies are called ontological user profiles or custom ontologies. Commonly used knowledge bases include generic ontologies (eg, WorldNet), thesauri (eg, digital libraries) and online knowledge bases (eg online categorization and Wikipedia). To represent user profiles, many researchers have attempted to discover the background knowledge of the user through global or local repositories. The global analysis uses the existing global knowledge bases for background knowledge of the user. Global analysis techniques produce effective performance for extracting background knowledge from the user. However, the overall analysis is limited by the quality of knowledge used. For example, WorldNet was reported as useful for capturing user interest in some areas, but useless for others. The local analysis observes the behavior of the user in the user profiles also investigates the user's local information. For example, Li and Zhong discovered the taxonomic pattern of the user's local text documents for learning the user profile of the ontologies. Other researchers learned custom ontologies from the user's browsing history. Alternatively, Sekin and Suzuki analyzed the query logs to discover the background of the user. Another way to get the user background is to ask the user some questions. In some other jobs, such as, users were provided a set of documents and ask for feedback of relevance to the user profile. However, classification techniques for knowledge discovery and local analysis techniques are based on data mining. Above all, the information discovered contains noisy and uncertain information. As results, local analysis suffers from inefficiency in knowledge capture.
The amount of information available on the Internet has increased dramatically. Collecting useful information from the web becomes a challenging problem for users. The current information collection system on the Web attempts to satisfy the requirements of users. It will capture your information needs. For this purpose, user profiles are created for the background description of the user. User profiles represent the conceptual models owned by users when collecting web information. The conceptual model is implicitly owned by users. Generated from there the background knowledge. Although this concept model can not be tested in laboratories ontologists have observed it in user behavior. When users read a document they can easily determine if it is of their interest or relevance to them, a judgment that arises from implicit conceptual models. If the user concept model can be simulated, a superior representation of the user profiles can be constructed. To simulate these models, Ontologies uses a model of description and formulation of web knowledge in the collection of personalized information on the Internet. These ontologies are called ontological user profiles or custom ontologies. Commonly used knowledge bases include generic ontologies (eg, WorldNet), thesauri (eg, digital libraries) and online knowledge bases (eg online categorization and Wikipedia). To represent user profiles, many researchers have attempted to discover the background knowledge of the user through global or local repositories. The global analysis uses the existing global knowledge bases for background knowledge of the user. Global analysis techniques produce effective performance for extracting background knowledge from the user. However, the overall analysis is limited by the quality of knowledge used. For example, WorldNet was reported as useful for capturing user interest in some areas, but useless for others. The local analysis observes the behavior of the user in the user profiles also investigates the user's local information. For example, Li and Zhong discovered the taxonomic pattern of the user's local text documents for learning the user profile of the ontologies. Other researchers learned custom ontologies from the user's browsing history. Alternatively, Sekin and Suzuki analyzed the query logs to discover the background of the user. Another way to get the user background is to ask the user some questions. In some other jobs, such as, users were provided a set of documents and ask for feedback of relevance to the user profile. However, classification techniques for knowledge discovery and local analysis techniques are based on data mining. Above all, the information discovered contains noisy and uncertain information. As results, local analysis suffers from inefficiency in knowledge capture.
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