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.
Elkafrawy, P., & Eldemerdash, D. (2018). Dynamic Classification for Web Documents Using Semantic Knowledge (DBpedia). The Egyptian Journal of Language Engineering, 5(2), 16-25. doi: 10.21608/ejle.2018.59345
MLA
Passent Elkafrawy; Dina Eid Eldemerdash. "Dynamic Classification for Web Documents Using Semantic Knowledge (DBpedia)", The Egyptian Journal of Language Engineering, 5, 2, 2018, 16-25. doi: 10.21608/ejle.2018.59345
HARVARD
Elkafrawy, P., Eldemerdash, D. (2018). 'Dynamic Classification for Web Documents Using Semantic Knowledge (DBpedia)', The Egyptian Journal of Language Engineering, 5(2), pp. 16-25. doi: 10.21608/ejle.2018.59345
VANCOUVER
Elkafrawy, P., Eldemerdash, D. Dynamic Classification for Web Documents Using Semantic Knowledge (DBpedia). The Egyptian Journal of Language Engineering, 2018; 5(2): 16-25. doi: 10.21608/ejle.2018.59345
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