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Web Virtual Assistant using Semantic Web Technology and Web Scraping

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Anil Verma
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GPUs can be used in NIDSs to maximize the performance when used in large scale networks
Sabbiu Shah
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The developed virtual assistant is a computer program which responds to a user query relating to that particular website. The virtual assistant is realized using two modules: static module and dynamic module. The static module is used for extracting the answer to the question based on the static content of the website. The dynamic module is used for querying structured information present in the website. The system works for the website which have semantic web technology implemented. The technology used are Web Ontology Language (OWL) and SPARQL querying language. This expert system when fed with 10 test cases, gave 80% accuracy. This is the best case scenario. Keywords: natural language processing, web scraping, chatbot ,semantic web technology, web ontology language, SPARQL, Naivebayes, word2vec.
This report presents the project entitled ’Web Virtual Assistant’ as part of the Major project as per the curriculum of B.E. in Computer Engineering. The developed virtual assistant is a computer program which responds to a user query relating to that particular website. The virtual assistant is realized using two modules: static module and dynamic module. The static module is used for extracting the answer to the question based on the static content of the website. The dynamic module is used for querying structured information present in the website. The system works for the website which have semantic web technology implemented. The technology used are Web Ontology Languge(OWL) and SPARQL querying language. Admin is allowed to enter intents related to database or FAQ. Each intent is created by adding number of examples and the answer structure to that question. The comparison of Naive bayes classifier and word2vec classifier used for identifying intent to the question, is made. The expert system when fed with 10 test cases, gave 80% accuracy. This is the best case scenario. Keywords: natural language processing, web scraping, chatbot,semantic web technology, web ontology language, SPARQL, Naive bayes, word2vec.