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Comparison of Text-based and Linked-based Metrics in Terms of Estimating the Similarity of Articles by Marzieh Goltaji; Javad Abbaspour; Abdolrasool Jowkar; Seyed Mostafa Fakhrahmad is a Social Sciences article available to read on EtoBox.

What is Comparison of Text-based and Linked-based Metrics in Terms of Estimating the Similarity of Articles about?

The aim of this study is to identify the power of text-based metrics (Cosine and Lucene similarity) and linked-based (Co-citation, bibliographic coupling, Amsler, PageRank, and HITS) and their combination in estimating the similarity of articles with each other. The experiments were conducted on a test collection of 26,262 articles in the PubMed Central Open Access Subset (PMC OAS) of CITREC that, in addition to having linked-based metrics, their full text was available for calculating text-based metrics. Thirty articles were selected as primary articles, and articles related to each of them were retrieved based on the mesh similarity metric. Then, the similarity of the retrieved documents based on text-based and linked-based metrics was also extracted. In the next stage, text-based, linked-based, and hybrid metrics were entered into the generalized regression model to estimate the similarity of the articles to determine their power; finally, the performance of the models was compared based on the mean squared error and correlation. The results showed that the model included Cosine and Lucene similarity metrics in text-based metrics. In linked-based metrics, HITS (Hub), HITS (autho

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Author

Marzieh Goltaji; Javad Abbaspour; Abdolrasool Jowkar; Seyed Mostafa Fakhrahmad

Publisher

SAGE Publications

Published

2023

Language

EN

Field

Social Sciences