Dynamic Classification for Web Documents Using Semantic Knowledge (DBpedia) | ||||
| The Egyptian Journal of Language Engineering | ||||
| Article 2, Volume 5, Issue 2, September 2018, Page 16-25 PDF (657.65 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ejle.2018.59345 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Passent Elkafrawy  1; Dina Eid Eldemerdash2
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| 1Mathematics and computer science department, Faculty Science, Menoufia University | ||||
| 2Mathematics and Computer Science Department, Faculty of Sciences, Menoufia University | ||||
| Abstract | ||||
| we present a dynamic Web document Classification using semantic knowledge (DBpedia). We present a method for a dynamic Web document Classification and automatic classification. The proposed approach required only a domain ontology and a set of user predefined categories. Currently, most approaches to text classification represent document as (bag of words) and training the large set of documents to train the classifier. Our approach doesn't require a training set of documents. In our proposed method, we use DBpedia ontology as the main classifier, representing documents as (bag of concepts). We extract the terms from the document, extract their resources from DBpedia Spotlight, use Sparqle query to determine class ontology and map them to their concepts then we determine the best category. | ||||
| Keywords | ||||
| classification; ontology; DBpedia; DBpedia Spotlight | ||||
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