Data-driven Techniques for Expert Finding

Boeva, Veselka; Angelova, Milena; Tsiporkova, Elena

doi:10.5220/0006195105350542

Cited by 5 publications

(6 citation statements)

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“…Boeva et al proposed a data driven expert finding technique. Their technique also weighs experts based on their expertise (Boeva, Angelova, & Tsiporkova, 2017). Further search into the literature would highlight other technical methods for expert finding.…”

Section: Brief Overview Of Expert Finding Researchmentioning

confidence: 99%

“…Partially inspired by the general BI solution, and its uses for academic research introduced by Chaudhuri et al (2011), Sherman (2014 and Dadkhah and Lagzian (2018) the process of experts finding is schematically presented in Figure 1. Similar to the work by Boeva et al, the process herein uses a keyword-based search to identify experts (Boeva, Angelova, & Tsiporkova, 2017). A basic requirement of a BI process is data.…”

Section: Process Of Finding Experts Using Bi Toolsmentioning

confidence: 99%

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The potential of business intelligence tools for expert finding

Dadkhah¹,

Lagziana²,

Rahim-niaa³

et al. 2019

JISIB

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Finding the right experts for data gathering through interview serves as a key for particular research works. However, most expert finding methods in the literature require great deals of technical knowledge, making them somewhat impracticable for business researchers without deep technical knowledge. Accordingly, there is a need for an expert finding solution for researchers without a deep technical background. As business researchers may have knowledge about business intelligence and its tools, the use of business intelligence tools can be used to solve such issue. The present paper discusses the process of using business intelligence tools to find potential experts for example topics. Subsequently, based on a literature review, criteria are presented for distinguishing different experts. Finally, the analytic hierarchy process is discussed for assigning weights to both selection criteria and potential experts. The audience of this paper is researchers who are familiar with business intelligence tools or would like to learn how to work with them

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Section: Brief Overview Of Expert Finding Researchmentioning

confidence: 99%

Section: Process Of Finding Experts Using Bi Toolsmentioning

confidence: 99%

The potential of business intelligence tools for expert finding

Dadkhah¹,

Lagziana²,

Rahim-niaa³

et al. 2019

JISIB

View full text Add to dashboard Cite

show abstract

Section: Brief Overview Of Expert Finding Researchmentioning

confidence: 99%

Section: Process Of Finding Experts Using Bi Toolsmentioning

confidence: 99%

“…identified by looking into the literature. Accordingly, papers presenting criteria for expert selection were identified (Afzal, Kulathuramaiyer, & Maurer, 2008;Afzal & Maurer, 2011;Benner, Tanner, & Chesla, 1992;Boeva et al, 2017;Cameron, Aleman-Meza, Decker, & Arpinar, 2007;Hirsch, 2005;Naeem et al, 2013;Quatrini Carvalho Passos Guimarães, Pena, Lopes, Lopes, & Bottura Leite de Barros, 2016); (Academia Europaea as cited in Naeem et al, 2013; Pakistan Academy of Sciences as cited in Naeem et al, 2013; Fehring as cited in Quatrini Carvalho Passos Guimarães, Pena, Lopes, Lopes, & Bottura Leite de Barros, 2016). As some of these papers were subject-oriented, respective criteria were generalized and used as a feature for expert identification and ranking (Table 3).…”

Section: Ranking Experts Based On the Research Topicmentioning

confidence: 99%

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2019

JISIB

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The journal includes articles within areas such as Competitive Intelligence, Business Intelligence, Market Intelligence, Scientific and Technical Intelligence and Geo-economics. This means that the journal has a managerial as well as an applied technical side (Information Systems), as these are now well integrated in real life Business Intelligence solutions. By focusing on business applications, this journal does not compete directly with the journals that deal with library sciences or state and military intelligence studies. Topics within the selected study areas should show clear practical implications.

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Expert finding in community question answering: a review

et al. 2019

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The rapid development recently of Community Question Answering (CQA) satisfies users quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. Sparse data and new features violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify all the existing solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the field of expert finding in CQA. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance. Further, we compare the performance of different models on different types of matching tasks, including text vs. text, graph vs. text, audio vs. text and video vs. text. The results can help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.

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Data-driven Techniques for Expert Finding

Cited by 5 publications

References 0 publications

The potential of business intelligence tools for expert finding

The potential of business intelligence tools for expert finding

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Expert finding in community question answering: a review

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