The Semantic web offers the intelligent algorithms that could logical analyze scholarly data and retrieve accurate results in scientific research queries. It is based on the generation of ontologies that describe particular knowledge domains. The building of a new ontology is a challenging and demanding approach. Herein, we enable the Hirsch index (h-index) to define the critical terms needed for the description of the cardiology domain. To this end we generated a master vocabulary to describe cardiology derived from relative textbooks by allowing duplicates. More than 56,000 unique terms were collected. The frequency of appearances of each term was used as the sole criterion for the evaluation of its importance in the cardiology domain description. The power regression (log-log) model best fits to these data compared to different nonlinear regression models. Therefore, we apply the h-index function to define the sufficient number of the multiple appeared cardiology terms that could describe this particular scientific field. We found that the hindex for the cardiology terms is 68, indicating the number of terms appearing equally or more than 68 times in the corpus of cardiology textbooks. The definite integral of the power function between the terms and their repeats for the 68 terms was found to represent 70% of the total area under curve. Thus, approximately 1.5‰ of the unique terms indexed in the Cardiology textbooks may be used as the core for the development of a cardiology ontology. We propose that this methodology may serve as a road map in similar librarian applications.