Evaluation of Semantic Similarity across MeSH Ontology: A Cairo University Thesis Mining Case Study

Abstract

Knowledge exaction and text representation are considered as the main concepts concerning organizations nowadays. The estimation of the semantic similarity between words provides a valuable method to enable the understanding of texts. In the field of biomedical domains, using Ontologies have been very effective due to their scalability and efficiency. The problem of extracting knowledge from huge amount of data is recorded as an issue in the medical sector. In this paper, we aim to improve knowledge representation by using MeSH Ontology on medical theses data by analyzing the similarity between the keywords within the theses data and keywords after using the MeSH ontology. As a result, we are able to better discover the commonalities between theses data and hence, improve the accuracy of the similarity estimation which in return improves the scientific research sector. Then, K-means cluster algorithm was applied to get the nearest departments that can work together based on medical ontology. Experimental evaluations using 4, 878 theses data set in the medical sector at Cairo University indicate that the proposed approach yields results that correlate more closely with human assessments than other by using the standard ontology (MeSH). Results show that using ontology correlates better, compared to related works, with the similarity assessments provided by experts in biomedicine.