Ranking plays important role in contemporary information search and retrieval systems. Among existing ranking algorithms, link analysis based algorithms have been proved to be effective for ranking documents retrieved from large-scale text repositories such as the current Web. Recent developments in semantic Web raise considerable interest in designing new ranking paradigms for various semantic search applications. While ranking methods in this context exist, they have not gained much popularity. In this article we introduce the idea of the “Rational Research” model which reflects search behaviour of a “rational” researcher in a scientific research environment, and propose the RareRank algorithm for ranking entities in semantic search systems, in particular, we focus on elaborating the rationale and implementation of the algorithm. Experiments are performed using the RareRank algorithm and the results are evaluated by domain experts using popular ranking performance measures. A comparison study with existing link-based ranking algorithms reveals the benefits of the proposed method.