Web search engines often federate many user queries to relevant structured databases. For example, a recruitment-related query might be federated to a jobseekers-and-employers database containing their resumes and skills. The relevant structured data items are then returned to the user along with web search results. Though each structured database is searched in isolation, the search often produces empty / incomplete results as the database may not contain the required information to answer the query. Starting from our Applicant Tracking System (ATS), we have 16 development databases of over 650,000 profile documents of resumes / cover letters / skills. There are on average 238 keywords per document. In fact, per minute there can be up to 200,000 transactions within all these databases. Our existing traditional database search technique (by full-text keyword PostgreSQL search) can be frozen or taking very long to respond unless if we cut off / only search from top profiles, we will not have the search results ready by thirty seconds, but this cut-off limitation returned incorrect results; for example for a query "Jet fuel Thermal Oxidation" to request information about job seekers whose resumes contain skills in Oil and Gas industry, in the top ten results there was a conflict in relevance ranking. In order to research a more suitable full-text keyword search technique better than the existing database search, we considered employment of semantic search models. Our semantic search technique has 88% -91.22% accuracy with very much quicker queries that can help users to make a search of 4 keywords of skills completed from 1 second to 28 seconds. Furthermore, the semantic search engine becomes very strong that users can search by entering a whole text paragraph. We designed a combination of semantic search that look for web pages per search and database search.

No abstract