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An Ontology Based Text Analytics on Social Media
Pankajdeep Kaur,Pallavi Sharma,Nikhil Vohra 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.5
The amount of digital information that is created and used is progressively rising along with the growth of sophisticated hardware and software. In addition, real-world data come in a diversity of forms and can be tremendously bulky. This has augmented the need for powerful algorithms that can deduce and dig out appealing facts and useful information from these data. Text Mining (TM), which is a very complex process; has been successfully used for this purpose. Text mining alternately referred to as text data mining, more or less equivalent to text analytics, can be defined as the process of extracting high-quality information from text. Text mining involves the process of structuring the input data, deriving patterns within the structured data and lastly interpretation and revelation of the output. This paper provides outline on text analytics and social media analytics. At the end, this paper presents our proposed work based on ontology framework to cope up with excessive social media textual data.
An Ontology Based E-Learning System
Pankajdeep Kaur,Pallavi Sharma,Nikhil Vohra 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.5
Based upon several new technologies that have been developed such as semantic web, SPARQL language and ontology engineering, this paper proposes platform architecture for e-learning. It is an e-learning management system with metadata. This system consists of ontology for the e-learning process, such as teaching methods, learning styles, learning activities and course syllabus. It helps students, administrative staff and teachers to set up and maintain the course data and go through the learning content. This system architecture will be capable of gaining user adaptability, performance scalability and concept reusability. It has ability to act in an intelligent manner by evaluating the academics initially and then provide personalized suggestions to the academics indicating their weaknesses and strengths.