Abstract:
Online job search and talent procurement have given rise to challenging match and search problems in the e-recruitment domain. Existing systems perform direct keyword mat...Show MoreMetadata
Abstract:
Online job search and talent procurement have given rise to challenging match and search problems in the e-recruitment domain. Existing systems perform direct keyword matching of technical skills which misses out a closely matching candidate on account of it not having the exact skills. This results in substandard results which ignores the relationships between technical skills. In an attempt to improve relevancy, this paper proposes a semantic similarity measure between IT skills using a knowledge based approach. The approach builds an ontology using DBpedia and uses it to derive a similarity score using feature based similarity measures. The proposed approach performs better than the Resumatcher system in finding the similarity between skills.
Published in: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Date of Conference: 28-31 August 2018
Date Added to IEEE Xplore: 25 October 2018
ISBN Information: