This detailed survey of the ontology modelling application Knowledge Seeker demonstrates how it can improve the efficiency of traditional information systems, increase the accuracy of text classification, and enhance the capabilities of search engines.

The Knowledge Seeker is a useful system to develop various intelligent applications such as ontology-based search engine, ontology-based text classification system, ontological agent system, and semantic web system etc. The Knowledge Seeker contains four different ontological components. First, it defines the knowledge representation model ¡V Ontology Graph. Second, an ontology learning process that based on chi-square statistics is proposed for automatic learning an Ontology Graph from texts for different domains. Third, it defines an ontology generation method that transforms the learning outcome to the Ontology Graph format for machine processing and also can be visualized for human validation. Fourth, it defines different ontological operations (such as similarity measurement and text classification) that can be carried out with the use of generated Ontology Graphs. The final goal of the KnowledgeSeeker system framework is that it can improve the traditional information system with higher efficiency. In particular, it can increase the accuracy of a text classification system, and also enhance the search intelligence in a search engine. This can be done by enhancing the system with machine processable ontology.

Well structured presentation of Knowledge Seeker an Ontology Modelling and Learning Framework Compendium of Ontology Modelling for Information Search and Management Written by leading experts in this field

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Part I Introduction.- Part II KnowledgeSeeker - An Ontology Modeling and Learning Framework.- Part III KnowledgeSeeker Applications.