Abstract

Vast amounts of graph data are generated every day from a mixture of sources as\nwell as social networks, ecommerce, semantic web (RDF data) and biochemical networks. We\nsurvey methodologies that achieve keyword search over weighted graph data. Keyword search\ngives a simple but user-friendly interface to retrieve information from complicated data\nstructures. In view of the fact that many real time datasets are represented via trees and graphs,\nkeyword search has turn into an appealing mechanism for information of a number of types. In\nthis survey, we thrash out the strategies of keyword search on graphs, that are summary\nillustration for graph databases and relational data and strategies of keyword search on\nduplication-free graphs. In our discussion, we concentrate on three most important issues of\nkeyword search on graphs. The primary issue is, what qualifies as an solution to a keyword\nsearch; the secondary issue is, a way to rank the answers; and the third issue is, a way to carry\nout keyword search efficiently. We additionally discuss a few unresolved issues and suggest a\nfew new research guidelines in this topic.