In the recent past, the attention has been paid for publishing the structured data on web, know as linked data. Linked data is simply about using web to create typed links between data from different sources. These may be as diverse as databases maintained by different organizations in distributed over different geographical locations, or heterogeneous within the same organization, which are not easily interoperated at the data level. Technically, linked data refers to data published on the web in such a way that it is machine-readable, its meaning is explicitly defined, it is linked to other external data sets, and can in turn be linked to from external data sets. A variety of linked data publishing tools has been developed. The tools shield publishers from dealing with technical detail such as content negotiation and ensure that data is published according to the linked data community best practices. In literature, we have found that the existing data publishing tools are either based on query reformulation or data translation. These two approaches suffer from the various drawbacks. In this work, we have combined these two approaches, and proposed a new architecture for data publishing tool for linked data. These tools are very helpful because data can published easily with the help of tools on semantic web.
