Intelligent information processing for building university knowledge base

Koperwas, Jakub; Skonieczny, Łukasz; Kozłowski, Marek; Andruszkiewicz, Piotr; Rybiński, Henryk; Struk, Wacław

doi:10.1007/s10844-015-0393-0

Cited by 14 publications

(7 citation statements)

References 33 publications

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“…In the recent literature, text classification (TC) has proven to give good results in extracting knowledge from many real-life Web-based data such as, for instance, those gathered by institutional scientific information platforms [28], or microblogs and other social media platforms [2,9,26,60], in many different research areas such as opinion spam detection [23,55] and sentiment analysis [4,41,43,54]. However, text classifiers have not been applied yet to the classification into the ISCO taxonomy of job vacancies written in natural language.…”

Section: Text Classificationmentioning

confidence: 99%

WoLMIS: a labor market intelligence system for classifying web job vacancies

et al. 2017

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In the last decades, an increasing number of employers and job seekers have been relying on Web resources to get in touch and to find a job. If appropriately retrieved and analyzed, the huge number of job vacancies available today on on-line job portals can provide detailed and valuable information about the Web Labor Market dynamics and trends. In particular, this information can be useful to all actors, public and private, who play a role in the European Labor Market. This paper presents WoLMIS, a system aimed at collecting and automatically classifying multilingual Web job vacancies with respect to a standard taxonomy of occupations. The proposed system has been developed for the Cedefop European agency, which supports the development of European Vocational Education and Training (VET) policies and contributes to their implementation. In particular, WoLMIS allows analysts and Labor Market specialists to make sense of Labor Market dynamics and trends of several countries in Europe, by overcoming linguistic boundaries across national borders. A detailed experimental evaluation analysis is also provided for a set of about 2 million job vacancies, collected from a set of UK and Irish Web job sites from June to September 2015.

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Section: Text Classificationmentioning

confidence: 99%

WoLMIS: a labor market intelligence system for classifying web job vacancies

et al. 2017

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“…Text Classification. In the recent literature, text classification (TC) has been shown to give good results in extracting knowledge from much real-life web-based data, for instance, data collected from institutional scientific information platforms [20], or microblogs and other social media platforms [21,22], in many different research areas such as opinion spam detection [23,24] and sentiment analysis [25,26], and, recently, job vacancies and labor market information in general [1,3]. Specifically, text classification has been an active research topic since the early 1990s.…”

Section: Related Workmentioning

confidence: 99%

Towards Labour Market Intelligence through Topic Modelling

Colace¹,

Santo²,

Lombardi³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Nowadays, the number of people and companies using the Web to search for and advertise job opportunities is growing apace, making data related to the Web labor market a rich source of information for understanding labor market dynamics and trends. In this paper, the emerging term labor market intelligence (LMI) refers to the definition of AI algorithms and frameworks that derive useful knowledge for labor market-related activities, by putting AI into the labor market. At the same time, another branch of AI is developing known as Explainable AI (XAI), whose goal is to obtain interpretable models from current (and future) AI algorithms, given that most of them actually act like black boxes, providing no interpretable explanations of their behavior, as in the case of machine learning. In this paper we connect these two approaches, using a graph model obtained through an NLP-based (Natural Language Processing) methodology for classifying job vacancies. We compare the results obtained with those from a European Project in LMI that employs machine learning for the classification task, to show that our approach is effective and promising.

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“…Universities provide a platform for academics to articulate their ideas and insights and a key function of university knowledge management is to serve as a knowledge repository that members within the academic community can access. This repository can be used as a diagnostic tool for enabling universities to identify any gaps in skills or knowledge within their institution (Koperwas et al, 2017) and can act as a source of competitive advantage for universities to enable scholars to advance knowledge and to make the institution stand out in the academic marketplace (Basu & Sengupta, 2007). There is a pressing need for universities to manage their intellectual capital and knowledge management processes due to increasing scrutiny on the use of public money and social accountability, and growing competition between academic institutions due to reduced levels of funding (Secundo et al, 2015 1.4.…”

Section: Understanding the Nature Of Competitive Advantage In Academiamentioning

confidence: 99%

Gaining competitive advantage through intellectual capital and knowledge management: an exploration of inhibitors and enablers in Jordanian Universities

Abu-Rumman

2018

Problems and Perspectives in Management

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This small-scale study aimed to explore the different factors that help or hinder the achievement of competitive advantage through the possession and development of intellectual capital and the delivery of effective knowledge management in a sample of Jordanian universities. Using a quantitative methodology, underpinned by a pragmatist theoretical approach, an electronic survey was conducted with staff working within three established universities based in Jordan. The concepts of intellectual capital, knowledge management and competitive advantage within an academic setting are first explored before focusing more specifically on investigating how different factors influence these and impact on competitive advantage. Overall, the study identified a range of inhibitors and enablers relating to intellectual capital and knowledge management and identified areas where universities need to develop in order to increase future competitive advantage. The study therefore makes a valuable contribution in adding to the body of evidence within this field.

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Intelligent information processing for building university knowledge base

Cited by 14 publications

References 33 publications

WoLMIS: a labor market intelligence system for classifying web job vacancies

WoLMIS: a labor market intelligence system for classifying web job vacancies

Towards Labour Market Intelligence through Topic Modelling

Gaining competitive advantage through intellectual capital and knowledge management: an exploration of inhibitors and enablers in Jordanian Universities

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