000			02770cam a2200337 a 4500
003			EG-GiCUC
005			20250223032426.0
008			191028s2018 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.18.02.M.Sc.2018.Ab.A
100	0		_aAbdelrahman Mostafa Arab
245	1	0	_aAutomatic mapping of documents to multiple domains using ontology and fuzzy sets / _cAbdelrahman Mostafa Arab ; Supervised Ahmad Gadallah , Akram Salah
246	1	5	_aالمطابقة الآلية للوثائق فى مجالات متعددة بإستخدام الأونطولوجى و الفئات الفازية
260			_aCairo : _bAbdelrahman Mostafa Arab , _c2018
300			_a226 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Computer and Information Science
520			_aClassification is an important technique used in information retrieval. Supervised classification suffers from certain limitations concerning the collection and the labeling of the training dataset. The problem gets more complicated when facing Multi-domain classification where multiple training datasets and classifiers are needed which is typically difficult. This thesis proposes a training-less multi-domain classification approach where each domain is represented by an ontology. A document is mapped on each ontology based on the weights of the mutual tokens between them. A mapping degree for the document with each domain is then determined with the help of fuzzy sets. A Multi-Domain Document Classification information retrieval system (MDDC) is built as an implementation of the proposed approach. A fuzzy matching approach, based on fuzzy triangular numbers, has also been used as another way in determining the mapping degree. The system was tested on a dataset of 180 journal articles of different domains where it succeeded in classifying them with an accuracy of 92.22%. The fuzzy triangular numbers approach succeeded in obtaining comparable results with the original approach. A number of evaluations have also been performed including comparing the system{u2019}s results with those of other algorithms using WEKA and RapidMiner as two of the top machine learning tools nowadays. The evaluation results were highly comparable and promising
530			_aIssued also as CD
653		4	_aInformation retrieval
653		4	_aMachine learning
653		4	_aOntology
700	0		_aAhmad Gadallah , _eSupervisor
700	0		_aAkram Salah , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSamia _eCataloger
942			_2ddc _cTH
999			_c74815 _d74815