JobLex: A Lexico-Semantic Knowledgebase of Occupational Information Descriptors
Reddy, Manikanta D.
De Choudhury, Munmun
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Technological advancements in several work sectors have influenced evolution of the landscape of work at an unprecedented speed, leading to the demand of continuous skill development [1,8]. In turn, this interests a number of stakeholders spanning across academia and industry in a number of disciplines including labor economics, who leverage large-scale data available from a variety of offline and online sources (e.g., resumes, job portals, professional social networking such as LinkedIn, search engine, job databases, etc.) [9,11,12]. On these data streams, describing job aspects and skills vary extensively, confounded by factors such as self-presentation, subjective perspectives on soft and hard skills, audience, and intrinsic traits such as personality and mindset [2,4,7,15,17]. Such data analyses require a taxonomy of keywords that are associated with skills per job description or type. However, most databases are only limited — they do not capture variants, typos, abbreviations, or internet slangs that are used on social media or in informal settings . To facilitate research in this space, our work builds on a well-validated dictionary of occupational descriptors (O*Net) to propose a method, and correspondingly a knowledgebase, JobLex of occupational descriptors that can be used in computational social science and organizational studies . We publish both our script and an example lexicon (for Twitter) for purposes of research and practical application. Item Description: This work publishes a methodology to build knowledgebase of occupational information descriptors. We also provide access to the codebase and example lexicon.