Development and Evaluation of Authority Data based Academic Paper Retrieval System

Lee, Seok-Hyoung; Kwak, Seung-Jin

doi:10.4275/kslis.2012.46.2.133

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yet, to the best of our knowledge, there is a lack of systems for visually displaying multifaceted inter-data relationships. Retrieval efficiency and system usability can be improved compared with currently available general search engines by revealing such variegated inter linkages among scientific data and analyzing them based on the attributes of respective extracted data [4,5]. This paper presents a visualization system based on the Solr system for the purpose of expanding the usability of scientific data.…”

Section: Introductionmentioning

confidence: 99%

A Data Visualization System for Considering Relationships Among Scientific Data

Gim

Jang

Chae³

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. With the recent explosive increase in the amount of web-based scientific data in big data environments, various researcher support systems have been developed to help discover desired scientific data and search insights. Scientific and researcher-related data are also applied to social networking services, thus promoting inter-researcher networking. However, much time and effort is put into big data mining to extract information customized to researchers' specific needs. Moreover, systems that facilitate information extraction by schematizing various inter-data relationships are absent. In this paper, we propose a system that facilitates relevant information extraction from scientific data and provides intuitive data visualization. Such data visualization allows efficient relationship expression between scientific data (relationships between researchers, acronyms and technical terms, and synonyms of a technology name), and provides an author disambiguation interface for authors with the same name. As a result, researchers can extract relevant information from big data with scientific data, and obtain significant information based on cleansed and disambiguated data.

show abstract

Section: Introductionmentioning

confidence: 99%