An ontological model for managing professional expertise

Ruiz-Martínez, Juana María; Miñarro-Giménez, José Antonio; Martı́nez-Béjar, Rodrigo

doi:10.1057/kmrp.2015.3

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Also, reference [79] used enterprise resource planning (ERP) systems as a source of expertise evidence. UNESCO research projects data has been used by the authors of reference [78] to discover experts. The authors of references [26,31,39,42,55] used extracted information from university websites.…”

Section: Environment Year Yearmentioning

confidence: 99%

“…Also, a bug repository was used as expertise evidence by the authors in reference [36]. Reference [78] used enterprise terminologies in their system as expertise evidence, while reference [79] used transactional data. Additionally, relevance feedback and log repository have been used by authors in reference [74] and reference [35], respectively.…”

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confidence: 99%

“…This is a formal explicit description of classes in a particular area with set of properties to describe the various features and attributes. For example, the authors of references [50,78] developed ontological models for expert finding systems. Moreover, recommendation systems techniques have been used for expert finding.…”

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confidence: 99%

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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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Section: Environment Year Yearmentioning

confidence: 99%

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confidence: 99%

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Expert Finding Systems: A Systematic Review

et al. 2019

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“…Expert finding has found success in various applications, including in relation to enterprises [1,2], community question answering [3,4], social networks [5,6], online forums [7], medicine [8], and academia [9][10][11][12]. A strong indicator of the significance of and interest in expert finding is the abundance of publications on the topic, including a substantial number of survey papers [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Information Retrieval and Machine Learning Methods for Academic Expert Finding

de Campos,

Fernández-Luna,

Huete

et al. 2024

Algorithms

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In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.

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Toward a cybercrime classification ontology: A knowledge-based approach

Donalds

Osei-Bryson

2019

Computers in Human Behavior

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An ontological model for managing professional expertise

Cited by 4 publications

References 10 publications

Expert Finding Systems: A Systematic Review

Expert Finding Systems: A Systematic Review

Information Retrieval and Machine Learning Methods for Academic Expert Finding

Toward a cybercrime classification ontology: A knowledge-based approach

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