FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data

• Published in: PeerJ. Computer science Vol. 7; p. e389
• Main Authors: Tahir, Noman, Asif, Muhammad, Ahmad, Shahbaz, Malik, Muhammad Sheraz Arshad, Aljuaid, Hanan, Butt, Muhammad Arif, Rehman, Mobashar
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 11.03.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Analysis; Data mining; Datasets; Domains; Emerging Technologies; Evaluation; Graph-based keyword extraction; Information retrieval; Keyword extraction; Keywords; Literature reviews; Machine learning; Metadata; Methods; Natural Language and Speech; Programming; Programming Languages; Scientific Computing and Simulation; Semantics; Statistical methods; Subject specialists; Textile research; Training; Programming; Keyword extraction; Graph-based keyword extraction
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.389

Keyword extraction is essential in determining influenced keywords from huge documents as the research repositories are becoming massive in volume day by day. The research community is drowning in data and starving for information. The keywords are the words that describe the theme of the whole document in a precise way by consisting of just a few words. Furthermore, many state-of-the-art approaches are available for keyword extraction from a huge collection of documents and are classified into three types, the statistical approaches, machine learning, and graph-based methods. The machine learning approaches require a large training dataset that needs to be developed manually by domain experts, which sometimes is difficult to produce while determining influenced keywords. However, this research focused on enhancing state-of-the-art graph-based methods to extract keywords when the training dataset is unavailable. This research first converted the handcrafted dataset, collected from impact factor journals into n -grams combinations, ranging from unigram to pentagram and also enhanced traditional graph-based approaches. The experiment was conducted on a handcrafted dataset, and all methods were applied on it. Domain experts performed the user study to evaluate the results. The results were observed from every method and were evaluated with the user study using precision, recall and f-measure as evaluation matrices. The results showed that the proposed method (FNG-IE) performed well and scored near the machine learning approaches score.