Using Web-Mining for Academic Measurement and Scholar Recommendation in Expert Finding System

Wu, Chi-Jen; Chung, Jen-Ming; Lu, Cheng-Yu; Lee, Hahn-Ming; Ho, Jan-Ming

doi:10.1109/wi-iat.2011.137

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…Zeng et al (2010) try to derive experts from co-authorship networks. Wu et al (2011) propose the p-index, inspired by the well-known h-index, to measure the "quality" of a researcher in a given domain. Moreira and Wichert (2013) combine multiple estimators of expertise employing the Dempster-Shafer theory of evidence and Shannon entropy.…”

Section: Related Workmentioning

confidence: 99%

Intelligent information processing for building university knowledge base

et al. 2016

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There are many ready-to-use software solutions for building institutional scientific information platforms, most of which have functionality well suited to repository needs. However, there have already been discussions about various problems with institutional digital libraries. As a remedy, an approach that is researcher-centric (rather than documentcentric) has been proposed recently in some systems. This paper is devoted to research aimed at tools for building knowledge bases for university research. We focus on the AI methods that have been elaborated and applied practically within our platform for building such knowledge bases. In particular we present a novel approach to data acquisition and the semantic enrichment of the acquired data. In addition, we present the algorithms applied in the real life system for experts profiling and retrieval.

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Section: Related Workmentioning

confidence: 99%

Intelligent information processing for building university knowledge base

et al. 2016

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“…This theory was used to combine different expertise sources. Also, the authors of references [5,51] used web mining methods to propose multifaceted web mining heuristic tool to find people of desired expertise and to recommend scholars, respectively. Also, the authors of references [62,63] used facility location analysis to propose an optimization framework to find an optimal group of experts for a given multi-aspect task/project.…”

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confidence: 99%

Expert Finding Systems: A Systematic Review

et al. 2019

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The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

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Scholarly recommendation systems: a literature survey

Zhang,

Patra,

Yaseen

et al. 2023

Knowl Inf Syst

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A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.

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Using Web-Mining for Academic Measurement and Scholar Recommendation in Expert Finding System

Cited by 10 publications

References 16 publications

Intelligent information processing for building university knowledge base

Intelligent information processing for building university knowledge base

Expert Finding Systems: A Systematic Review

Scholarly recommendation systems: a literature survey

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