Linked data is a promising way to publish media data as resources on the Web and interlink them with other resources. While significant amounts of image, audio and video fragments have been tagged and exposed as linked data, searching and explaining the unexpected query results have been rarely studied. To improve the functionality and usability of SPARQL-based multimedia search engines, we focus on explaining missing items in the query results, or the so-called why-not question in this paper. We first formalize why-not questions on multimedia SPARQL queries and then propose a novel explanation model to answer why-not questions. Our model adopts a to generate logical explanations at the basic graph pattern level, the filter constraint level, or the multimedia function level, respectively, which helps users refine their initial queries. Extensive experimental results on two real-world RDF datasets show that the proposed model and algorithms can provide high-quality explanations both in terms of effectiveness and efficiency.