Similarity Identification of Large-scale Biomedical Documents using Cosine Similarity and Parallel Computing

Abstract

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification process that automatically calculates in the PubMed application. The execution time of mesh heading is 15.447 seconds, and the timely execution of abstract is 74.191 seconds. The execution time of mesh heading is higher than abstract because abstract contains more words than mesh heading. This study has successfully identified the similarity between large-scale biomedical documents of the PubMed documents that implemented a cosine similarity algorithm. The result has shown that the cosine similarity of the mesh heading texts is higher than the abstract text in the form of a graph and table shown in the PubMed application. The cosine similarity is useful to measure the similarity between documents based on the TF*IDF calculation result.