Feature selection is an important pre-processing, data mining, and knowledge discovery tool for data analysis. By eliminating redundant and irrelevant features from high-dimensional data, feature selection diminishes the 'curse of dimensionality' to improve performance. Data are becoming increasingly complex; heterogeneous data may often be viewed as natural collections of linked objects. Linked data are structured data that are connected with other data sources through the use of semantic queries. It is increasingly prevalent in social media websites and biological networks. Many feature selection methods assume independent and identically distributed data (IID), a condition violated with linked data. In this paper, a review of current feature selection techniques for linked data is presented. Several approaches are examined in various contexts so that performance issues and ongoing challenges can be assessed. The major contribution of this paper is to underscore contemporary uses and limitations of linked data feature selection techniques with the purpose of informing existing capabilities and current potentials for key areas of future research and application.