The traditional Web is evolving into the Web of Data which consists of huge collections of structured data over poorly controlled distributed data sources. Live queries are needed to get current information out of this global data space. In live query processing, source selection deserves attention since it allows us to identify the sources which might likely contain the relevant content. Another important criterion for selecting sources is the quality of data they contain, which can be assessed according to several dimensions. Data quality, however, often is not absolute; rather it is context-dependent. The aim of this project is to investigate context-dependent source selection approaches combining relevance with data quality according to multiple dimensions, with the aim of selecting not only the most relevant but also the highest quality sources. We are relying on SKOS for context modelling and on index-based approaches, based on data summaries, for source selection.