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

Boselli, Roberto; Cesarini, Mirko; Marrara, Stefania; Mercorio, Fabio; Mezzanzanica, Mario; Pasi, Gabriella; Viviani, Marco

doi:10.1007/s10844-017-0488-x

Cited by 66 publications

(36 citation statements)

References 38 publications

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“…5) Ontology: Capturing labor market trends from the content of online job advertisements is an important aspect. In this paper, we identified primary studies which utilize existing labor market ontologies such as ESCO [101], [107] or O*NET [49] to map job titles from collected advertisements with existing titles in these ontologies. However, a new challenge is the creation of a new ontology which describes the whole digital labor content.…”

Section: ) Adoption By Policy Makers (Stakeholder: Policymentioning

confidence: 99%

Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study

et al. 2019

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Software engineering is a continuously evolving sector and the demands of the related labor market result in a wide variety of job openings, ranging from developers to customer service positions. Thus, there is a need to continuously monitor labor market trends using data and analytics. Both employers and employees can benefit by capturing emerging trends which can facilitate continuous learning and training in new technologies, support of better matching between a job offer and the ideal candidate and expertise detection. To fulfill these needs, the results of labor market analytics need to reach the stakeholders timely and accurately. However, often delays occur, which stem from time-consuming approaches based on collecting data from traditional sources, such as questionnaires or interviews. Recently, researchers started leveraging content from digital sources, which are easily accessed and contain a wealth of information. This paper presents the results of a Systematic Mapping Study on digital sources that can be used to address the data analytics needs of the labor market. It provides a multifaceted categorization of the issues involved in the analysis of digital sources of the software engineering labor market. It aims to identify digital labor market sources for data retrieval which are appropriate for employers and employees analytics. Additionally, it aims to connect different skill types, needs and goals of labor market with the utilization of digital sources and data analysis methods. In total 86 primary studies were selected and each one was evaluated and classified aiming to identify the: (a) digital sources that are used for labor market analytics; (b) type of skills they examine; (c) methods which are used to utilize the raw digital content; (d) goals for which every primary study is conducted; (e) beneficiaries (stakeholder) of the results; and (f) time trends for all the above.

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Section: ) Adoption By Policy Makers (Stakeholder: Policymentioning

confidence: 99%

Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study

et al. 2019

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“…Here, a key role is played by big data relating to the labor market (e.g., job advertisements, CVs posted on the Web, etc.). These data need to be collected, organized, and manipulated to permit real-time monitoring and analysis of labor market dynamics and trends (see, for example, [13,14,15]).…”

Section: Background On Lmimentioning

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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“…In academic research, online job portals may provide researchers with precious information to study the labour market in real time [1].…”

Section: Application Context and Datas Stmentioning

confidence: 99%

“…An interesting application context is offered by the continuously-growing diffusion of Web portals for the labour market (like job portals, employment Web sites and so on); the on-line availability of ads concerning job vacancies enables new research activities for analyzing the labour market and its dynamics [1]. However, web portals for publishing ads are just an example that shows how the Web has become a valuable source of data concerning any aspect of day-by-day life.…”

Section: Introductionmentioning

confidence: 99%

Blind Queries Applied to JSON Document Stores

Marrara

Pelucchi²,

Psaila

2019

Information

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Social Media, Web Portals and, in general, information systems offer their own Application Programming Interfaces (APIs), used to provide large data sets concerning every aspect of day-by-day life. APIs usually provide data sets as collections of JSON documents. The heterogeneous structure of JSON documents returned by different APIs constitutes a barrier to effectively query and analyze these data sets. The adoption of NoSQL document stores, such as MongoDB, is useful for gathering these data sets, but does not solve the problem of querying the final heterogeneous repository. The aim of this paper is to provide analysts with a tool, named HammerJDB, that allows for blind querying collections of JSON documents within a NoSQL document database. The idea below is that users may know the application domain but it may be that they are not aware of the real structures of the documents stored in the database—the tool for blind querying tries to bridge the gap, by adopting a query rewriting mechanism. This paper is an evolution of a technique for blind querying Open Data portals and of its implementation within the Hammer framework, presented in some previous work. In this paper, we evolve that approach in order to query a NoSQL document database by evolving the Hammer framework into the HammerJDB framework, which is able to work on MongoDB databases. The effectiveness of the new approach is evaluated on a data set (derived from a real-life one), containing job-vacancy ads collected from European job portals.

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

Cited by 66 publications

References 38 publications

Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study

Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study

Towards Labour Market Intelligence through Topic Modelling

Blind Queries Applied to JSON Document Stores

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