Computing semantic relatedness between textual labels representing biological and medical concepts is a crucial task in many automated knowledge extraction and processing applications relevant to the biomedical domain, specifically due to the huge amount of new findings being published each year. Most methods benefit from making use of highly specific resources, thus reducing their usability in many real world scenarios that differ from the original assumptions. In this paper we present a simple resource-efficient method for calculating semantic relatedness in a knowledge-poor environment. The method obtains results comparable to state-of-the-art methods, while being more generic and flexible. The solution being presented here was designed to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base, thus it does not assume a closed problem.