About: Enhancing the Literature Review Using Author-Topic Profiling

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Enhancing the Literature Review Using Author-Topic Profiling
Creator	Haruechaiyasak, Choochart Kongthon, Alisa Thaiprayoon, Santipong
source	PMC
abstract	In this paper, we utilize bibliographic data for identifying author-topic relations which can be used to enhance the traditional literature review. When writing a research paper, researchers often cite on the order of tens of references which do not provide the complete coverage of the research context especially when the targeted research is multidisciplinary. Author-topic profiling can help researchers discover a broader picture of their topic of interest including topical relationships and research community. We apply the Latent Dirichlet Allocation (LDA) to generate multinomial distributions over words and topics to discover author-topic relations from text collections. As an illustration, we apply the methodology to bibliographic abstracts related to Emerging Infectious Diseases (EIDs) research topic.
has issue date	2008-01-01(xsd:dateTime)
bibo:doi	10.1007/978-3-540-89533-6_38
has license	no-cc
sha1sum (hex)	c194e5935bbce634b23e07c3f644ecde7849804c
schema:url	https://doi.org/10.1007/978-3-540-89533-6_38
resource representing a document's title	Enhancing the Literature Review Using Author-Topic Profiling
has PubMed Central identifier	PMC7120163
schema:publication	Digital Libraries: Universal and Ubiquitous Access to Information
resource representing a document's body	covid:c194e5935bbce634b23e07c3f644ecde7849804c#body_text
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