A Survey on Semantic Web Search Engine
The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now a days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Semantic web technologies are playing a crucial role in enhancing traditional web search, as it is working to create machine readable data. but it will not replace traditional search engine. In this paper we made a brief survey on various promising features of some of the best semantic search engines developed so far and we have discussed the various approaches to semantic search. We have summarized the techniques, advantages of some important semantic web search engines that are developed so far.The most prominent part is that how the semantic search engines differ from the traditional searches and their results are shown by giving a sample query as input
Keywords: RDF,OIL, DAML+OIL, OWL, QDEX
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ABOUT THE AUTHORS
G.Sudeepthi
She did her M.Tech (CSE) from Koneru Lakshmaiah College of Engineering, Andhra Pradesh, India. She has got one paper published in International journal. She is pursuing her Ph.D from Andhra University,visakhapatnam. Presently She is working as Assistant Professor in CSE department at CMR institute of technology,Hyderabad,AP,India.Her research area include semantic web on Intelligent systems.
G. Anuradha
She did her M.Tech from GITAM college of engineering, Visakhapatnam, Andhra Pradesh. She is working as an Associate professor in the department of CSE in GMRIT, Rajam, and Srikakulam district. She is pursuing Ph.D. in Andhra University on semantic web and intelligent systems. She has got one paper published in International journal, and more than five paper published in various National & international conferences.
M.Surendra Prasad Babu
He did his M.Phil and Ph.D. degrees from Andhra University. He is presently working as Professor in Dept. of Computer science & systems engineering in Andhra University college of Engineering in Visakhapatnam. He has got more than 30 years of teaching experience. He has more than 120 papers published in various National & International journals and conferences. His research areas include Artificial Intelligence & Expert Systems Machine Learning, Computer Networks, Neural Networks, and Object Oriented Technologies, web Technologies. Pervasive computing and similarity transformations.
G.Sudeepthi
She did her M.Tech (CSE) from Koneru Lakshmaiah College of Engineering, Andhra Pradesh, India. She has got one paper published in International journal. She is pursuing her Ph.D from Andhra University,visakhapatnam. Presently She is working as Assistant Professor in CSE department at CMR institute of technology,Hyderabad,AP,India.Her research area include semantic web on Intelligent systems.
G. Anuradha
She did her M.Tech from GITAM college of engineering, Visakhapatnam, Andhra Pradesh. She is working as an Associate professor in the department of CSE in GMRIT, Rajam, and Srikakulam district. She is pursuing Ph.D. in Andhra University on semantic web and intelligent systems. She has got one paper published in International journal, and more than five paper published in various National & international conferences.
M.Surendra Prasad Babu
He did his M.Phil and Ph.D. degrees from Andhra University. He is presently working as Professor in Dept. of Computer science & systems engineering in Andhra University college of Engineering in Visakhapatnam. He has got more than 30 years of teaching experience. He has more than 120 papers published in various National & International journals and conferences. His research areas include Artificial Intelligence & Expert Systems Machine Learning, Computer Networks, Neural Networks, and Object Oriented Technologies, web Technologies. Pervasive computing and similarity transformations.
