Document (#40233)

Author

Blank, I.
Rokach, L.
Shani, G.

Title

Leveraging metadata to recommend keywords for academic papers

Source

Journal of the Association for Information Science and Technology. 67(2016) no.12, S.3073-3091

Year

2016

Abstract

Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions.

Content

Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23571/full.

Theme

Automatisches Indexieren

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4. Hu, K.; Luo, Q.; Qi, K.; Yang, S.; Mao, J.; Fu, X.; Zheng, J.; Wu, H.; Guo, Y.; Zhu, Q.: Understanding the topic evolution of scientific literatures like an evolving city : using Google Word2Vec model and spatial autocorrelation analysis (2019) 0.34

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5. Su, H.-N.: Visualization of global science and technology policy research structure (2012) 0.28

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