Combining social network and semantic concept analysis for personalized academic researcher recommendation

Xu, Yunhong; Guo, Xitong; Jiang, Hao; Ma, Jian; Lau, Raymond Y. K.; Xu, Wei

doi:10.1016/j.dss.2012.08.003

Cited by 66 publications

(39 citation statements)

References 32 publications

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“…With the development of research social network, richer information are generated in the platform, such as project information and self-claimed interests. These information has also been used to profile a researcher [32,39,40].…”

Section: Expert Findingmentioning

confidence: 99%

“…Expert finding systems are initially applied in organizations as knowledge bases to store the expertise and skills of employees for knowledge management [9]. Now it has been applied in 1) online question & answering community for problem answering [20,23,37], 2) research social network for collaborator seeking [6,32,39], 3) research management system for reviewer assignment [31] and 4) organizations for well-informed colleagues retrieval [27,41].…”

Section: Expert Findingmentioning

confidence: 99%

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Connecting Researchers with Companies for University-Industry Collaboration

Wang

Ma²,

Liao

et al. 2017

Proceedings of the Annual Hawaii International Conference on System Sciences

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Section: Expert Findingmentioning

confidence: 99%

Section: Expert Findingmentioning

confidence: 99%

Connecting Researchers with Companies for University-Industry Collaboration

Wang

Ma²,

Liao

et al. 2017

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

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“…accordingly, a number of recommendations of the study is presented to the user [12]. Ferrara et al (2014) provided an overview of data extraction from the web.…”

Section: Performed Tasksmentioning

confidence: 99%

The Extraction of Social Networks from Web Using Search Engines

Mohajer¹

2017

jaar

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In this paper, our purpose is to create a large collection of related vocabularies and concepts to the user's favorite field (articles, people, conferences, books, etc.) from the available information on the infinite and vast source of web which is expressed in the form of social network. In the other words, we introduced a way to help the researchers to be able to specify their favorite topic in a particular field and by this way, observe and extract the social network of the related concepts to that topic. In order to extract the nodes of this network, we used the sampling of web pages through the Google search engine, text processing techniques, and information retrieval. The topic of the extracted social network in this research is the scientific conferences in the field of computer sciences. In order to evaluate the effectiveness of this method, the extracted network from the results of the search engine is compared with the scientific conferences available in the DBLP 1 database. The obtained results from the social network analysis showed that the extracted network is of very high accuracy.

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“…In today's digital world, a web of learning resources surrounds every individual. It is an environment where each resource connects to others, creating an overall structure in which all learning takes place as a new learning ecosystem [11].…”

Section: Personal Learning Environments (Ples)mentioning

confidence: 99%

An Overview of Competency Management for Learning and Performance Support: A Canadian Perspective

Kondratova

Fourier²,

Molyneaux³

2017

Int. J. Adv. Corp. Learn.

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Abstract-Despite the turbulent economy, recent expenditures on workplace learning in North America have increased. Technology-based methods including tools that enable social learning are making significant gains and account for 39% of all training hours in 2012. A majority of companies are moving from static classroom training to workplace learning that is more interactive and driven by technology. Companies actively experiment with new methods such as personalized learning, performance support, and gamification to encourage employees' motivation to learn and promote continuous workplace learning, practice and application. However, the divide between the training and competencies people have and the training and competencies companies need still remains. The National Research Council Canada (NRC)'s Learning and Performance Support Systems (LPSS) program, by implementing adaptive and personalization strategies, develops software components for learning, training, performance support and enterprise workforce optimization. These technologies have the potential to facilitate lifelong learning, reduce learning and training costs, and reduce demands on physical infrastructure. Software components being developed for learning, training and performance support also enable streamlined and rapid skill development, as well as reduce time to competency, support informal, personal and personalized learning, increase learner engagement, address workforce optimization and sustainability, and increase operational performance and productivity. An overview of the LPSS system and capabilities is presented along with the results of our review of the current state of competency management in Canada and some challenges in this area, followed by recommendations for further work on competency functionality in the context of the LPSS program.

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Combining social network and semantic concept analysis for personalized academic researcher recommendation

Cited by 66 publications

References 32 publications

Connecting Researchers with Companies for University-Industry Collaboration

Connecting Researchers with Companies for University-Industry Collaboration

The Extraction of Social Networks from Web Using Search Engines

An Overview of Competency Management for Learning and Performance Support: A Canadian Perspective

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