OAEI 2006

Matching: Human Resource Ontologies

To improve job portal functionality we utilise Semantic Web technologies to semantically annotate job postings and applicants’ profiles in order to increase market transparency together with avoiding the bottleneck of a central database. In a Semantic Web-based recruitment application the data exchange between employers, job applicants and job portals is based on a set of shared vocabularies describing domain relevant terms: occupations, industrial sectors and skills. These commonly used vocabularies have been formally defined by means of a so-called Human Resource ontology (HR-ontology). The implementation of the HR-ontology was realized by translating several semi-structured input formalisms and coding text-based classification standards to OWL.

The Data

In order to support common industry practice and to maximize the integration of job seeker profiles and job postings from different organizations the ontology underlying the Semantic Web job portal had to be aligned to established domain-specific standards and classifications. We reused some of the most relevant classifications being deployed by national and international agencies:

Our HR ontology is modelled using OWL and descriptions of job postings and applicants’ profiles are stored in RDF using the vocabulary defined by the HR ontology.

Task & Results

In the HR scenario, the domain specific knowledge is represented in the form of various concept hierarchies (skills, occupation classification, industry sectors, etc.) and can be used to determine the semantic similarity between concepts. The algorithm should allow us to compare job descriptions and applicants’ profiles based on their semantics. We compare one job description including skills, occupation types and industry sectors with all applicants’ profiles which also contain skills, occupation type and industry sector (as well as another way round: compare one job seeker description with all available openings). Results are ranked according to semantic similarity in a range 0:1. Hence the provided algorithms must support1:n matching. The ranking will be made according to our weightings.

Current system

The current application (cf. black parts in the Fig. 1) uses an algorithm which is based on the similarity between two concepts determined by the distance between them at the same time reflecting the respective positions of the concepts in the concept hierarchy.

Modified system

The modified application (cf. red parts in the Fig. 1) uses ontology matcher for automatic generation of a job matcher (Fig. 1 - red matching engine) depending on the ontologies. The generated matcher is to be used in place of the original one (Fig. 1 - black matchig engine).

Fig. 1 The job-application matching cases: existing application (in black) + modified application (in red) Fig. 1 The job-application matching case: existing (in black) + modified (in red) application


The matching alogrithms as well as their parameters should be provided to the organizers (Jerome.Euzenat(at)inrialpes.fr) which will run the algorithms on the ontologies and obtain the alignment. Furthermore the generated job matcher will be used to compare a set of about 250 job ofersag with about 250 applicants' profiles. The current approach will be used as the baseline to compare submissions. Submissions can use any matching approach desired, including the use of additional, external sources. However these sources may only be accessed during the matchings and must not be manually pre-tuned to the given ontologies.