Ontologies play a crucial role in bringing the Semantic Web vision to its full potential. Ontologies are developed at different levels of abstraction and by different people for various purposes. Knowledge represented by the ontologies is scattered because of existence of many ontologies representing the same concepts, therefore it becomes difficult to analyse, study and use the knowledge spread across many ontologies if they are studied individually. Knowledge represented by ontologies can be combined into a single ontology which is shown in this paper. Ontologies can be merged to combine the knowledge from different ontologies. In this paper we have shown the merger of two ontologies first ontology is university second ontology is the Student profile ontology containing details of Student which were developed in educational domain. Ontologies were developed and merged using protégé 4.0 alpha tools.
Semantic Web extends the current web by standardizing the semantics. Purpose of Semantic Web is to make people share data and content among different applications. This is made possible by using ontologies which represents shared understanding of a domain. Semantic Web uses ontologies for representing knowledge in a uniform way that can be easily processed and shared among machine. This paper gives detail description of the role of ontologies in Semantic Web and tools required for constructing Semantic Web applications. An application is created which loads the ontology created in Protégé into Eclipse and displays the properties and classes of ontology.
Social media is one of the biggest forums to express opinions. Sentiment analysis is the procedure by which information is extracted from the opinions, appraisal and emotions of people in regards to entities, events and their attributes. Sentiment analysis is also known as opinion mining. Opinion mining is to analyze and cluster the user generated data like reviews, blogs, comments, articles etc. These data find its way on social networking sites like twitter, facebook etc. Twitter has provided a very gigantic space for prediction of consumer brands, movie reviews, democratic electoral events, stock market, and popularity of celebrities.The main objective of opinion mining is to cluster the tweets into positive and negative clusters. An earlier work is based on supervised machine learning (Naïve bayes, maximum entropy classification and support vector machines). The proposed work is able to collect information from social networking sites like Twitter and the same is used for sentiment analysis. The processed meaningful tweets are cluster into two different clusters positive and negative using unsupervised machine learning technique such as spectral clustering. Manual analysis of such large number of tweets is impossible. So the automated approach of unsupervised learning as spectral clustering is used. The results are also visualized using scatter plot graph and hierarchical graph.
Knowledge based recommendation systems use knowledge about users and products to make recommendations. Knowledge-based recommendations are not dependent on the rating, nor do they have to gather information about a particular user to give recommendations. Knowledge acquisition is the most important task for constructing knowledge-based recommendation system. Acquired knowledge must be represented in some structured machinereadable form, e.g., as ontology to support reasoning about what products meets the user's requirements. In Semantic Web, knowledge is represented in the form of ontology. Representation of knowledge in structured form of ontology in Semantic Web makes the application of knowledge based recommendations system on Semantic Web very easy, as there is no need to construct knowledge base from scratch. Performance of knowledge based recommendations systems can be enhanced by exploiting ontology reasoning characteristics. This paper explores different techniques used to generate knowledge-based recommendations highlighting the advantages of knowledge based recommendation system over other recommendation techniques.
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