As a model for knowledge description and formalization, ontologyís are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles.
This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.
1. Golden Model: TREC Model:
The TREC model was used to demonstrate the interviewing user profiles, which reflected user concept models perfectly. For each topic, TREC users were given a set of documents to read and judged each as relevant or nonrelevant to the topic. The TREC user profiles perfectly reflected the usersí personal interests, as the relevant judgments were provided by the same people who created the topics as well, following the fact that only users know their interests and preferences perfectly.
2. Baseline Model: Category Model:
This model demonstrated the noninterviewing user profiles, a userís interests and preferences are described by a set of weighted subjects learned from the userís browsing history. These subjects are specified with the semantic relations of superclass and subclass in an ontology. When an OBIWAN agent receives the search results for a given topic, it filters and reranks the results based on their semantic similarity with the subjects. The similar documents are awarded and reranked higher on the result list.
3. Baseline Model: Web Model:
The web model was the implementation of typical semi interviewing user profiles. It acquired user profiles from the web by employing a web search engine. The feature terms referred to the interesting concepts of the topic. The noisy terms referred to the paradoxical or ambiguous concepts.
The world knowledge and a userís local instance repository (LIR) are used in the proposed model. World knowledge is commonsense knowledge acquired by people from experience and education
An LIR is a userís personal collection of information items. From a world knowledge base, we construct personalized ontologies by adopting user feedback on interesting knowledge. A multidimensional ontology mining method, Specificity and Exhaustivity, is also introduced in the proposed model for analyzing concepts specified in ontologies. The usersí LIRs are then used to discover background knowledge and to populate the personalized ontologies.
- World Knowledge Base
- Ontology Learning Environment
- Ontology Mining
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