Large Scale Text Classification Using Map Reduce and Naive Bayes Algorithm for Domain Specified Ontology Building

Internet that covers a large information gives an opportunity to obtain knowledge from it. Internet contains large unstructured and unorganized data such as text, video, and image. Problems arise on how to organize large amount of data and obtain a useful information from it. This information can be used as knowledge in the intelligent computer system. Ontology as one of knowledge representation covers a large area topic. For constructing domain specified ontology, we use large text dataset on Internet and organize it into specified domain before ontology building process is done. We try to implement naive bayes text classifier using map reduce programming model in our research for organizing our large text dataset. In this experiment, we use animal and plant domain article in Wikipedia online encyclopedia as our dataset. Our proposed method can achieve highest accuracy with score about 98.8%. This experiment shows that our proposed method provides a robust system and good accuracy for classifying document into specified domain in preprocessing phase for domain specified ontology building.