Exploiting Papers’ Reference’s Section for Multi-Label Computer Science Research Papers’ Classification

Naseer Ahmed Sajid (), Munir Ahmad (), Muhammad Tanvir Afzal () and Atta-ur-Rahman ()
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Naseer Ahmed Sajid: Barani Institute of Information Technology (BIIT), PMAS-AA University, Rawalpindi, Pakistan
Munir Ahmad: Barani Institute of Information Technology (BIIT), PMAS-AA University, Rawalpindi, Pakistan
Muhammad Tanvir Afzal: #x2020;Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan
Atta-ur-Rahman: #x2021;Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia

Journal of Information & Knowledge Management (JIKM), 2021, vol. 20, issue 01, 1-21

Abstract: The profusion of documents production at an exponential rate over the web has made it difficult for the scientific community to retrieve most relevant information against the query. The research community is busy in proposing innovative mechanisms to ensure the document retrieval in a flexible manner. The document classification is a core concept of information retrieval that classifies the documents into predefined categories. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as information retrieval, finding experts, recommender systems, etc. In Computer Science, the Association for Computing Machinery (ACM) categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In this paper, we exploit the reference section of a research paper to predict the topics of the paper. We have proposed a framework called Category-Based Category Identification (CBCI) for multi-label research papers classification. The proposed approach extracted references from training dataset and grouped them in a Topic-Reference (TR) pair such as TR {Topic, Reference}. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The approach collects the corresponding list of topics matched with the references in the said pair. We have evaluated our technique for two datasets that is Journal of Universal Computer Science (JUCS) and ACM. The proposed approach is able to predict the first node in the ACM topic (topic A to K) with 74% accuracy for both JUCS and ACM dataset for multi-label classification.

Keywords: Multi-label classification; data mining; scientific documents; citations (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219649221500040

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