Search Engines like Google, Bing and Yahoo provide users with autocomplete search suggestions based on a partially entered search queries. These suggestions are generated based on the following.
- Users' previous search histories
- What other people are searching for, including Trending stories
- The terms being typed.
The drawback of this approach is that the suggested completions are often ambiguous in nature. They do not provide any support for disambiguating homonyms i.e term with two or more meaning for the search term. Our goal in this project is to provide better search suggestion by disambiguating homonyms and providing semantically relevant information with the help of building an ontology supported web search (OSWS). The project has been inspired from the work of the reference research paper called Google Knows Who Is Famous Today [research paper] in which an ontology based web search is constructed using DBPedia and YAGO data sources. A big shortcoming of the project developed in the research paper is the use of DBpedia and YAGO data sources which may be outdated since these data sources are updated only once in two years. Another shortcoming is that the YAGO and DBPedia end points have a very slow response times. The main idea of our project is to address the shortcoming of the project developed in the research paper by using a new data source called Babelnet - which provides very fast API response times and contains latest up to date information.
