Abstract
Extending co-citation using sections of research articles
Highlights
The digital information over the World Wide Web is rapidly being increased [1]
We propose an extension of the co-citation-based approach that exploits in-text citation frequencies and the role of citation patterns in co-cited documents
The co-citation [6] and citation proximity analysis (CPA) [2] were applied on this dataset; two different sets of 17 ranked document(s) lists were obtained
Summary
The digital information over the World Wide Web is rapidly being increased [1]. This bulk of information hinders users in retrieving relevant information [2–4]. In this regard, various efforts have been made in the form of citation indexes systems, such as CiteSeer, Google Scholars, etc. Various efforts have been made in the form of citation indexes systems, such as CiteSeer, Google Scholars, etc These systems return a plethora of documents, requiring users to make cognitive efforts to ascertain the relevant documents. Such scenarios cause exasperation and most often users end up missing the most relevant documents.
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