We conducted a semantic similarity study of semantic concepts in the context of the Holy Book Quran. Semantic similarity examines the degree of likeness and shared common properties of two concepts. For example, the Quranic concept of Allah and God will result in a high score of semantic similarity, whereas hell and paradise will yield in a low score because of its extremely different attributes and semantic features. Apart from that, we also delivered the Quranic concept semantic similarity standard dataset which consists of some pairs of Quranic concept along with its similarity score, which was manually annotated by human raters. This dataset resulted in the score of inter-annotator agreement 0.63, not far from the the ones yielded by some well-known datasets such as WordSim and Simlex. Furthermore, to measure the semantic similarity score, we chose the knowledge-based approach by utilizing lexical database properties such as the length and depth of a synonym set (synset). We then applied it to Yuhua Li equation, which has been considered to be the baseline among researchers within the problem of semantic similarity. In terms of the result, our system gained Pearson's correlation 0.33 and Spearman's 0.19. By considering inter-annotator agreement 0.63 that our Quranic standard dataset has as the upper bound score, there are still quite large room for improvement to better mimicking Muslim's intuition to measure the degree of similarity of concepts within the domain of Quran.