In a traditional recruitment process, large amounts of resumes and job postings are often handled manually, which is very time-consuming. Existing machine learning techniques for automatic resume ranking lack accuracy in accessing relevant information in job offers, which is crucially needed in order to ensure the pertinence of resumes. We present a context-driven possibilistic framework for extracting such information from job postings, in the form of relevant terms. In our process, after considering the recruiters’ specific organizational context, we analyze their term relevance evaluation strategies in job advertisements. By interviewing a group of recruiters and analyzing their behavior, we have derived a first set of textual relevance markers. Existing term-extraction methods from the literature were also applied to extract such textual relevance markers. We have evaluated all markers using cognitive uncertainty measures and we have integrated them into an ontology-based Belief-Desire-Intention architecture. Doing this, we have improved the F1 score and recall measures of existing state-of-the-art term extraction approaches by 20% and 29% respectively. Besides, our framework is open-ended: it is possible to add new textual markers at any time as nodes of a fuzzy decision tree, the calculation of which depends on the context and domain of job offers.