Better performing keywords cover search

Abstract

Finding relevant information is a big challenge in today's information retrieval domain. Various applications need to find objects closest to the mentioned location that has a set of keywords. In a spatial dataset, objects are linked with some keyword(s) which specify their features. Closest Keywords is a method to query objects, using keyword cover. Algorithm based on Closest Keywords Search which exhaustively combines objects from different query keywords for generating candidate keyword covers. The increasing importance of keyword rating in object evaluation helps for the better decision making. This triggers to generate Best Keyword Cover which mainly considers inter-objects distance as well as the keyword rating of objects. When the number of query keywords gets increases, the performance of the closest keyword cover search algorithm drops significantly as a result of huge candidate keyword covers generated. To overcome this drawback, much more scalable algorithm known as keyword nearest neighbor expansion (keyword-NNE) has been proposed. Keyword-NNE algorithm significantly reduces number of candidate keyword covers generated.