An empirical framework on adaptive keyword query searching in Linked Databases

Keyword Search is an emerging field for searching in database. It is an alternate way of dealing with traditional SQL querying in relational database with larger datasets. Now what happen in traditional databases is one need to know the attributes and the schema of databases. For the end-user to retrieve the data is very difficult because to fire the query he/she must know the SQL language. Researches done in this area mostly deal with keyword search in single database then what for when the user is dealing with more than one database, to overcome this problem we are introducing an algorithm of SIL(Searching in Linked Databases). The other challenge in keyword processing is storing the keyword in the table i.e. via Inverted Index, so we introduce a novel technique to DeINIX(Density INverted IndeX) which reduce the memory storage space henceforth the pre-processing time is also reduced and the answer displayed will be First-10 answers. Our empirical result shows that this method answers queries more precisely, takes lesser time and memory space to retrieve a quality answer.