Knowledge graphs (KGs), with their flexible and expressive data model, are frequently used for management of large-scale data and knowledge in data-intensive domains, including business, healthcare, and biomedicine. In particular, the KG data representation enables extraction from KGs of various knowledge and insights, with a number of applications to date. One type of knowledge that can be extracted from KGs is relational knowledge, which is expressed as paths between pairs of KG nodes and can provide real-world explanations for domain connections between the entities or concepts of interest. In this paper we focus on the problem of ranking path-based explanations for KG queries in ways that would rank higher the paths that make more sense in the user-provided semantic context, effectively and efficiently on large-scale KGs.Toward addressing the problem, we introduce an approach called Semantics-Aware Path Ranking Algorithm (SAPRA). The SAPRA approach is designed to scale to very large KGs. It is broadly applicable to KG data and queries in a range of domains, leveraging the properties of entities and relationships within the given KG to recommend paths that most closely align with the user-provided semantic context. To further enhance the accuracy of the semantic interpretation of the user queries, SAPRA can adapt its behavior based on feedback from domain experts. SAPRA accepts as inputs KG queries and the associated semantic context in their purely syntactic form, which makes the approach domain agnostic. We report the results of an experimental evaluation of our implementation of SAPRA on the biomedical KGs ROBOKOP and DRKG. The results show promise for better path-ranking effectiveness and efficiency of the proposed approach against the state of the art on large-scale KGs, in the biomedical domain and potentially beyond.