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It is an emerging challenge for data mining is the problem of mining richly structures datasets, where the objects are linked in some way. Links among the objects may demonstrate certain patterns, which can be helpful for many data mining tasks and are usually hard to capture with traditional statistical models. Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type (eg. people connected by friendship links, or the WWW, a collection of linked web pages) or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. Commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. This is an exciting and rapidly expanding area. In this article we review some of the common emerging themes and discuss ongoing link mining challenges; open issues and suggest ideas that could be opportunities for solutions. The most conclusion of this article is that providing an idea to usage link mining techniques from link mining to help to construct the Semantic Web.