000			02292nam a2200301Ia 4500
003			OSt
005			20251112151827.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9783642179150
020			_aSL01558459
037			_cTextbook
040			_aCSL _beng _cCSL
041			_aeng
084			_aD65,8(B):71 Q1 TD _qCSL
100			_aLim, Edward H Y _eauthor _9850986
245		0	_aKnowledge Seeker-Ontology Modelling for Information Search and Management
260			_aHeidelberg : _bSpringer-Verlag , _c2011 .
300			_axxvi,237p.
500			_aIncluded References 195-210p.; Appendix 211-232p.
520			_aThe 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.
650			_aInformation retrieval. _9850987
650			_aOntology. _9850988
650			_aComputer Science. _9850989
700			_aLiu, James N K _eco-author _9850990
700			_aLee Raymond S T _eco-author _9850991
942			_hD65,8(B):71 Q1 TD _cTB _2CC _n0
999			_c8264 _d8264