An ontology-based intelligent system for recruitment

García‐Sánchez, Francisco; Martı́nez-Béjar, Rodrigo; Contreras, Leonardo; Fernández-Breis, Jesualdo Tomás; Castellanos–Nieves, Dagoberto

doi:10.1016/j.eswa.2005.09.023

Cited by 68 publications

(16 citation statements)

References 14 publications

(10 reference statements)

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“…On the other hand, the authors of [4][5][6] have employed knowledge represented by semantic resources to automate the recruitment process. The used semantic resources in their systems have been built based on integrated classifications and standards.…”

Section: Related Workmentioning

confidence: 99%

“…Some of these systems provide users (employers and job seekers) with the ability to upload their resumes as unstructured documents using different formats such as .doc, .pdf, .rtf [9,10], while others restrict this process by only allowing users to upload their job requirements / qualification information according to specific forms [4][5][6]. For example, PROSPECT system [11] allows recruiters to list and rank applicants' resumes based on a combination between statistical and information retrieval techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Accordingly, many approaches have been proposed to accomplish this task. Examples are: Boolean retrieval based approaches [2], relevance based models [3] and most recently approaches that rely on exiting semantic resources [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MatchingSem: Online recruitment system based on multiple semantic resources

Kmail

Maree

Belkhatir

2015

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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The growth of online recruitment has spurred the need for more effective automated systems. On the one hand, traditional approaches based on keyword-based matching techniques suffer from low precision, i.e. a large fraction of the systems' suggestions are irrelevant. On the other hand, the newer semantics-based approaches are penalized by limitations of the exploited semantic resources, namely semantic knowledge incompleteness and limited domain coverage. In this paper, we present an automatic semantics-based online recruitment system that reuses knowledge captured in multiple existing semantic resources to match between candidate resumes and job posts. In addition, we use statistical-based concept-relatedness measures to alleviate the problem of semantic knowledge incompleteness in the exploited resources. An experimental instantiation of the proposed system has been installed to validate its effectiveness in matching job applicants to job posts.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

MatchingSem: Online recruitment system based on multiple semantic resources

Kmail

Maree

Belkhatir

2015

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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show abstract

“…Some document detection methods made use of ontology and utilized the structured information in Wikipedia to enhance the performance (Grineva et al 2009). García-Sánchez et al (2006) proposed an ontology-based recruitment system to provide intelligent matching between employer advertisements and the curriculum vitae of the candidates. Moreover, Lee et al (2009) used ontology to construct the knowledge of travel information in Tainan City, and further integrated fuzzy inference with ant colony optimization to recommend to the tourist a personalized travel route to enjoy Tainan City according to the tourist's requirements effectively.…”

Section: Related Workmentioning

confidence: 99%

A semantic frame-based intelligent agent for topic detection

et al. 2015

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Detecting the topic of documents can help readers construct the background of the topic and facilitate document comprehension. In this paper, we propose a semantic frame-based topic detection (SFTD) that simulates such process in human perception. We take advantage of multiple knowledge sources and extracted discriminative patterns from documents through a highly automated, knowledgesupported frame generation and matching mechanisms. Using a Chinese news corpus containing over 111,000 news articles, we provide a comprehensive performance evaluation which demonstrates that our novel approach can effectively detect the topic of a document by exploiting the syntactic structures, semantic association, and the context within the text. Experimental results show that SFTD is comparable to other well-known topic detection methods.

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“…Another good example of e-employment ontology is García-Sánchez, Martínez-Béjar, Contreras, Fernández-Breis, and Castellanos-Nieves (2006) where the ontology supports a search engine that provides intelligent matches between job offers and candidates curricula in a job portal bounded to a rural area. Nevertheless, the development of this ontology does not rely on any widespread standard or classification.…”

Section: Related Workmentioning

confidence: 99%

A network of ontology networks for building e-employment advanced systems

Villazón-Terrazas

Ramírez

Suárez-Figueroa

et al. 2011

Expert Systems with Applications

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a b s t r a c tThis paper presents the development of a network of ontology networks that enables data mediation between the Employment Services (ESs) participating in a semantic interoperability platform for the exchange of Curricula Vitae (CVs) and job offers in different languages. Such network is formed by (1) a set of local ontology networks that are language dependent, in which each network represents the local and particular view that each ES has of the employment market; and (2) a reference ontology network developed in English that represents a standardized and agreed upon terminology of the European employment market. In this network each local ontology network is aligned with the reference ontology network so that search queries, CVs, and job offers can be mediated through these alignments from any ES. The development of the ontologies has followed the methodological guidelines issued by the NeOn Methodology and is focused mainly on scenarios that involve reusing and re-engineering knowledge resources already agreed upon by employment experts and standardization bodies. This paper explains how these methodological guidelines have been applied for building e-employment ontologies. In addition, it shows that the approach to building ontologies by reusing and re-engineering agreed upon non-ontological resources speeds the ontology development, reduces development costs, and retrieves knowledge already agreed upon by a community of people in a more formal representation.

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An ontology-based intelligent system for recruitment

Cited by 68 publications

References 14 publications

MatchingSem: Online recruitment system based on multiple semantic resources

MatchingSem: Online recruitment system based on multiple semantic resources

A semantic frame-based intelligent agent for topic detection

A network of ontology networks for building e-employment advanced systems

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