The publication of linked data has become a growing phenomenon. This can be explained by the development of web semantic technologies and by regulatory constraints that require from companies and institutions the publication of some of their data of interest. The resulting linked open data sources offer huge opportunities for the development of novel applications. But at the same time, the quality of the data provided by these sources can be poor, making their exploitation and usage difficult, sometimes even risky. Quality issues have to be dealt with in this context. In this paper, we present a state of the art of the approaches proposed in the literature for the management of linked open data quality. The scope of our study covers the dimensions and the metrics, the frameworks, the softwares, and the use cases for quality management of linked open data proposed in literature. Based on this study, we identify some open research problems and perspectives.