Semantic-Aware Expert Partitioning

Boeva, Veselka; Boneva, Liliana; Tsiporkova, Elena

doi:10.1007/978-3-319-10554-3_2

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…The applied approach uses the semantic similarity between keywords to identify the main research/application subjects covered by the survey. It is based on the idea published in [ 52 ]. In order to be able to apply this approach we have manually associated each keyword (from the most frequent ones) with its synonym keywords.…”

Section: Methodsmentioning

confidence: 99%

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

Al-Saedi

Boeva

Casalicchio

et al. 2022

Sensors

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Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

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Section: Methodsmentioning

confidence: 99%

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

Al-Saedi

Boeva

Casalicchio

et al. 2022

Sensors

Self Cite

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“…Our IR-based models adopt an intermediate approach by generating multiple subprofiles for each expert using clustering methods, aiming to ensure that each subprofile represents a specific topic or a homogeneous group of topics. Some related works also use clustering methods, such as [46,47], but they typically cluster people based on similar expertise, whereas our approach clusters documents to separate different kinds of expertise for each researcher. Our approach also shares similarities with the research in [43], which employed the author-persona-topic (APT) model to suggest suitable reviewers for submitted papers.…”

Section: Related Workmentioning

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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“…ones developed in (Boeva et al 2014, Boeva et al 2016. In the considered context each cluster of experts can itself be thought as the expertise area of any expert (supervisor) assigned to the cluster.…”

Section: Expert Finding 17mentioning

confidence: 99%

“…In the recent years research on identifying experts from online data sources, such as the DBLP library, Microsoft Academic Search, Google Scholar Citation, LinkedIn, PubMed etc., has been gradually gaining interest (Abramowicz et al 2011, Balog and de Rijke 2007, Bozzon et al 2013, Boeva et al 2014, Boeva et al 2016, Hristoskova et al 2013, Jung et al 2007, Singh et al 2013, Stankovic et al 2011, Tsiporkova and Tourwé 2011, Zhang et al 2007). An example is a Web-based biomedical expert finding system, proposed in (Singh et al 2013), which can be applied to identify subject experts and subjects associated with an expert.…”

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confidence: 99%