An enhanced approach for querying integrated web analytics ontology using Quepy

Abstract

Web analytics is becoming an inevitable aspect in today's Ecommerce scenario, It allows the companies and Ecommerce website owners to track the behavior of customers visiting their website which helps to improvise them. Eventhough there exists an extensive variety of tools for web investigation in market, for example, google analytics, crazy egg, piwik etc. the significant issue confronted is that the vast majority of the tools concentrate on low level and limited set of attributes. This focuses to the significance of an ontology based method which gather web investigation information from many wellsprings of web analytics tools, as a result a web analytics ontology(WAO) is made. This web analytics ontology considers large and complimented set of web metrics and attributes and there by making analytics much more meticulous. Fetching data from WAO is done by means of SPARQL query and that is a keyword based search which searches tremendous volume of data. It is quite difficult for a beginer to learn such a high level query language. Therefore to conquer the confinement of such a question answering system for web analytics, This paper proposes an enhanced approach for querying WAO. In the proposed approach user enters a natural language query and for that query meaningful concepts are extracted from web analytics ontology to form the SPARQL query and then it is fired on the knowledge base(ontology) that finds appropriate RDF triples. The proposed system converts the natural query to SPARQL query using Quepy framework.