Authors:
Hua Li
;
Daniel J. T. Powell
;
Mark Clark
;
Tifani O'Brien
and
Rafael Alonso
Affiliation:
Leidos Inc., United States
Keyword(s):
User Modeling, Expertise Modeling, Resume, Profile, Skill.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Enterprise Information Systems
;
Intelligent Information Systems
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Organizational Memories
;
Symbolic Systems
;
Tools and Technology for Knowledge Management
Abstract:
Job applicants describe their skills and expertise in resumes and curriculum vitaes (CVs). These biographic
data are often evaluated by human resource personnel or a search committee. This manual approach works
well when the number of resumes is small. However, in this information age, the volume of available
resumes can be overwhelming and there is a need for automatic evaluation of applicant skills and expertise.
In this paper, we describe a user modeling algorithm to quantitatively identify skills and expertise from
biographic data. This algorithm is called REMA (Resume Expertise Modeling Algorithm). REMA takes
data from a resume document as input and produces an expertise model. The expertise model details the
expertise topics for which the resume owner has claimed competency. Each topic carries a weight indicating
the level of competency. There are two key insights for this algorithm. First, one’s expertise is the
cumulative result of the various “learning events” in one’s caree
r. These learning events are mentioned in
various sections of the resume, such as earning a degree, writing a paper, or getting a patent. Second, one’s
knowledge and skills can become outdated or forgotten over time if not reinforced by learning. We have
developed a prototype resume evaluation system based on REMA and are in the process of evaluating
REMA’s performance.
(More)