Automated Mapping of Scholarly Domains: A Case Study on the arXiv Repository

Title: Automated Mapping of Scholarly Domains: A Case Study on the arXiv Repository

Abstract: This study explores the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature, using the arXiv repository as a case study. The primary goal is to illustrate the automated detection of emerging research areas within a larger-scale resource, which can be beneficial in disentangling other sub-networks and associated sub-communities from the global network. The study employs a support vector machine text classifier to extract an emerging research area from the corpus, demonstrating the potential of machine learning methods in enhancing the management and understanding of scholarly domains.

Main Research Question: How can machine learning techniques be used to automatically detect and map emerging research areas within a large online corpus of academic literature?

Methodology: The study utilizes the arXiv repository, which contains over 250,000 full-text research articles in physics and related disciplines. The authors focus on training a support vector machine text classifier to extract an emerging research area from the larger corpus. They represent document content using vector representations and treat each category label as a separate binary classification problem.

Results: The study successfully demonstrates the automated detection of emerging research areas within the arXiv repository. The support vector machine text classifier accurately classifies new documents, proving the effectiveness of the machine learning approach in generating text classification rules automatically from examples.

Implications: The results of this study have significant implications for the management and understanding of scholarly domains. By automatically detecting and mapping emerging research areas, researchers can more effectively navigate and contribute to their respective fields. Additionally, the study highlights the potential of machine learning techniques in enhancing the efficiency and accuracy of information retrieval and management systems within academia.

Link to Article: https://arxiv.org/abs/0312018v1 Authors: arXiv ID: 0312018v1