Semantic Matchmaking for Job Recruitment: An Ontology-Based Hybrid Approach

Transcription

1 Semantc Matchmang for Job Recrutment: An Ontology-Based Hybrd Approach Maryam Fazel-Zarand 1, Mar S. Fox 2 1 epartment of Computer Scence, Unversty of Toronto, Canada 2 epartment of Mechancal and Industral Engneerng, Unversty of Toronto, Canada mfazel@cs.toronto.edu, msf@el.utoronto.ca Abstract. Our am n ths wor s to propose an ontology-based hybrd approach to effectvely match ob seeers and ob advertsements. The approach uses a deductve model to determne the nd of match between a ob seeer and an advertsement, and apples a smlarty-based approach to ran applcants. Keywords: Semantc Matchng, Recrutment, Sll Ontology 1 Introducton In human resource management t s often necessary to locate and match ndvduals and postons. Examples of such tass nclude human resource recrutng, selectng ndvduals for teams based on dfferent slls and qualfcatons, and fndng the rght expert to acqure nformaton or to learn from wthn an organzaton. Currently, for human resource recrutng, the Internet s beng manly used to place onlne ob advertsements, to perform resume search, and to acqure nformaton about slls and competences of ndvduals [4]. In order to augment and assst ths process, the study and development of totally or partally automated technques and tools have receved the attenton of both researchers and organzatons. To effectvely locate and match ndvduals and postons, wthn or from outsde an organzaton, t s mportant to use semantc technology [3][10]. The use of semantc descrptons of ob offers and applcant profles allows for qualtatve and quanttatve reasonng about matchngs between avalable and requred slls and competences whch s needed to mprove the process of decdng who to hre and assgnng ndvduals to tass and teams [1]. Furthermore, semantc descrptons of applcant profles wthn an organzaton help mprove the management of ndvdual slls and competences of avalable human resources, and provde a global vew of the slls avalable at the organzatonal level. In ths paper, dfferent nds of matchmang strateges are combned to mprove the recrutment process. We formally defne ob seeers and ob advertsements usng a sll ontology, use a deductve model to determne the nd of match between a ob seeer and an advertsement, and fnally use a smlarty based approach to ran applcants. Thus, a descrpton logc-based classfcaton s performed to determne

2 the type of match between a ob seeer and an advertsement, and then ranng of applcants wth partal match s done based on ther smlarty degree. Related to ths wor are [2] and [10] whch present a scenaro for supportng the recrutment process wth semantc web technologes for the German Government. Ther approach uses [12] s smlarty measure to evaluate the degree of match between ob offers and applcants. Our approach s dfferent from thers n that n addton to usng dfferent smlarty measures, t uses logc deductve facltes whch present hgh precson and recall. Also, desred slls and competences are not consdered n ther wor. The organzaton of ths paper s as follows: Secton 2 presents the proposed ontologcal framewor for modelng ob seeers and ob descrptons. Secton 3 descrbes the matchmang model, whle Secton 4 presents the ranng algorthm. Fnally, Secton 5 concludes the paper wth a dscusson of contrbutons made and areas of future wor. 2 Ontologcal Framewor In human resource recrutng, two perspectves are dstngushed: A ob seeer creates an applcaton by specfyng her slls, level of competence and some sort of proof for each sll usng the atomc concepts defned n an OWL-L ontology. In other words, a ob seeer s consdered to be equvalent to a set of sll statements. Slls are semantcally organzed n a sll ontology SllOnt 1. A proof for a sll can ether be a degree and/or prevous wor experence. A ob advertsement s a set of requrements n terms of ob related descrptons and constrants on slls, competency levels, and/or proofs. The requrements can ether be must-have constrants or desred (nce-to-have) slls or degrees. In case of desres, a numerc value s also assgned ndcatng the mportance of havng that sll or degree accordng to the recruter. escrpton logcs (L) are used to formally represent applcatons and ob advertsements. As mentoned, a ob seeer s a person havng a set of sll statements: JobSeeer Person 1 hassllstatement.sllstatement where, the concept SllStatement s represented as: SllStatement =1 hassll.sll =1 hascompetencylevel hasproof.proof 1 We consder techncal, socal, and organzatonal slls n a specfc doman of nterest.

3 where, hascompetencylevel can tae a value n the range {1,5}. A proof can ether be a degree and/or prevous wor experence: egree Proof =1 hasttle.ttle hasfeld.studyfeld =1 from.instuton =1 startate.ate =1 endate.ate WorExperence Proof =1 hasposton.ttle =1 hasorganzaton.company =1 startate.ate =1 endate.ate =1 hasuraton.ate hasfuncton.jobfuncton hassupervsor.person When a new ob advertsement s provded, a new concept representng ths advertsement s added n a OWL-L ob ontology. Ths concept s represented usng the L formalsm as the conuncton of: A concept n the form hasescrpton.jobescrpton, where Jobescrpton =1 hasttle.ttle =1 forcompany.company hasindustry.industry hasfuncton.jobfuncton =1 hastype.(parttme FullTme) One or more concepts n the form hasrequrement.requrement, where Requrement can ether be a degree or sll requrement. egreerequrement Requrement =1 requresegree.ttle =1 requresfeld.studyfeld SllRequrement Requrement =1 requressll.sll =1 requrescompetencylevel 0 requresexperence 0 requresegree Zero or more concepts n the form hasesre.esre, where 3

4 esre Requrement =1 hasesrelevel where, hasesrelevel can tae a value n the range {1, 10}. Job advertsements are further categorzed based on ndustry, functon, and poston ttle. Ths s partcularly useful for searchng for obs n terms of ob related descrptons. These expressons can be represented n OWL-L, correspondng to the SHOIN() famly of descrpton logcs. 3 Matchng When searchng for obs (or applcants), a ob seeer (or recruter) can as for all ob advertsements (or applcatons) that match her applcaton. In addton, a ob seeer can also express her requrements n terms of desred ob related descrptons. For matchng ob seeers to ob descrptons, only must-have requrements are consdered. esres are used later for ranng. Let be a ob advertsement wth a set of requrements { d _ req, s _ req }, where d _ req s the -th degree requrement, and s _ req s the -th sll requrement of. A Qualfed match denotes that a ob seeer satsfes all the requrements of. In order to determne a qualfed match, we create a new concept C as a conuncton of the followng terms: For each d _ req, requrng degree d n feld f, For each where, f term = hassllstatement.(hasproof.(egree hasttle.d hasfeld.f )) s _ req, requrng sll s wth competency level l, term = hassllstatement.(hassll.s hascompetencylevel.l extra) s _ req requres a mnmum amount of experence ex, then extra = hasproof.(worexperence hasuraton.ex ) f s _ req requres a certan degree d n feld f, then extra = hasproof.(egree hasttle.d hasfeld.f ) otherwse, extra s.

5 All nstances of C are qualfed matches for. In real world stuatons, however, t rarely happens that applcatons match all the requrements specfed n a ob advertsement. For ths, n addton to the qualfed match, we defne two types of Under-Qualfed matches. In the frst case, call t Under-Qualfed-Type-1, an applcaton s consdered to be under-qualfed for the ob advertsement f and only f 2 : 1) the competency level s less than the requred competency level for a specfc sll n ; or 2) n case of requred experence, the number of years s not satsfed. Note that when determnng a match we are assumng that the sll specfed n a requrement exsts n the applcaton. In other words, the cases n whch one or more of the sll requrements are not satsfed at all are not consdered. To determne such a match, the same approach for determnng a qualfed match s done wth the unsatsfed constrants replaced by varables. The Under-Qualfed-Type-2 match taes nto account the fact that t s not always the case that all the requred slls are present n an applcaton. If consderng all the requrements specfed n a ob advertsement results n fndng no matchng applcatons (qualfed or under-qualfed), t would be possble to terate through all the requrements that are not satsfed, replace a sll at a tme wth ts parent (whch s a more general sll) and perform the search agan untl a matchng applcaton s found. 4 Ranng In order to ran the applcatons matched to a ob descrpton, we need to consder three scenaros. The frst scenaro nvolves fndng the most sutable applcatons n the set of all qualfed applcatons (those that satsfy the qualfed match crtera) for a ob advertsement. In ths scenaro, desred slls or degrees are used to evaluate the match degree. The second scenaro nvolves ranng the set of applcatons satsfyng the under-qualfed matchng crtera. In ths case some sort of a smlarty measure needs to be taen nto consderaton. The thrd scenaro nvolves the cases n whch one or more of the sll requrements are not satsfed at all. Consderng the frst scenaro, we tae nto account the desre level values, u(ds ), assgned to each desre by the recruter and normalze them to 1 (.e., u(ds ) = 1). We can wrte the global match degree as the sum of the desre levels of the satsfed desred slls or degrees: Sm x u ds ) where, x s the Boolean varable ndcatng whether or not desre s satsfed for each A n the set of all qualfed applcatons. To calculate x, for each desre a term smlar to term or term s created and then nstance checng s done to see f A s an nstance of ths term. ( 2 For now we consder all degree requrements to be hard constrants. 5

6 To ran applcatons n the second scenaro, we defne two dssmlarty measures: one based on the competency levels, and another one based on the requred experence. Cssm ( l l )[( l l )] ( l l ) where, l s the requred competency level of sll requrement of, and l s the competency level of applcaton A for the matchng sll. The term n the power exsts so that f the dfference n the competency levels s greater, then the two are more dssmlar. For example, f an applcaton has a dfference of competency level 4 n only one sll, and another applcaton has a dfference of competency level 2 n two slls, then the frst applcaton s more dssmlar to the ob advertsement than the second one. Smlarly, we defne the dssmlarty measure based on the requred experence: EXssm ( ex ex )[( ex ex )] ( ex ex ) where, ex s the requred experence of sll requrement of, and ex s the experence applcaton A has for the matchng sll. Havng these two measures, t s possble to ran the applcatons based num c + num ex (or maybe a weghted sum n the case where one crtera s more mportant than the other) and choose the ones that are less dssmlar. If the ob advertsement also ncludes some desres, then t s possble to use the technque used n the frst scenaro to further ran the applcatons that have equal dssmlarty values. The ranng of applcatons n the thrd scenaro s somewhat dfferent from the prevous two scenaros. For ths we need to consder a node-based semantc smlarty measure [12][11][6]. One such measure s the smlarty measure ntroduced n [12] whch s based on two assumptons: 1) dstance between sblngs s greater than the dstance between parent and chld, and 2) semantc dstance between upper level sblng concepts s greater than between sblng concepts on lower levels: SemSm( a, b) 1 d ( a, b) where, d c (a,b) s the dstance between the two concepts a and b: d c d ( a, b) d ( a, cpp) d ( b, cpp) c c ( x, cpp) mlestone( cpp) mlestone( x) where, cpp s the closest comment parent of a and b, and mlestone s the value assgned to each concept n the sll ontology SllOnt and s calculated usng the followng formula: c c

7 mlestone 1 2 ( x) l( x) where, s the rate the mlestone decreases, and l(x) s the herarchy level of concept x. Usng such a smlarty measure, then, t s possble to evaluate the match degree based on the smlarty between the sll that was requred and the sll that the applcant actually has. 5 Concluson and Future rectons Ths paper presents an approach to matchng ob seeers and ob advertsements that combnes a deductve matchmang model based on descrpton logcs and a smlarty based ranng model. Currently we are n the process of testng our approach wth real data to compare the dfferent matchng and ranng crtera. In addton to satsfyng advertsed ob requrements, other factors such as ob seeers and recruters preferences, cultural ft, ablty to adapt to the company s maretplace and ablty to grow wth the organzaton play an mportant part n selectng employees. Furthermore, when consderng ndvduals for teams, complextes arse when consderng the ftness between an ndvdual and other team members [9]. It would be nterestng to see how these complextes can be supported by automated technques. Ths approach can also be used to mprove Slls Management Systems or Expertse Fndng Systems wthn an organzaton. Currently the approach reles on self declaratons of competences and experences whch can be naccurate or nsuffcent. It would be nterestng to use mechansms to automatcally dscover upto-date expertse nformaton from secondary sources such as codes, documents, and forums. For ths the doman ontology can be used to automatcally annotate exstng nformaton resources and to perform automated reasonng to mprove the detecton and extracton of ndcators of expertse [5]. Another useful ontology n ths regard s the organzaton ontology [7] whch formalzes the organzatonal structure and can be used to nfer slls and expertse based on the roles that the agents play and the communcatons that occur among them. The nowledge provenance and trust ontologes presented n [8] are other examples of ontologes whch can prove to be useful n ths context. These ontologes can be used to formally defne the semantcs of nformaton sources, nformaton dependences, relatonshps between nformaton sources and experts, and trust relatonshps to mprove expertse recognton and extracton. References 1. Badca, C.; Popescu, E.; Fracowa, G.; Ganzha, M.; Paprzyc, M.; Szymcza, M.; Par, M.W.: On Human Resource Adaptablty n an Agent-Based Vrtual Organzaton; Studes n Computatonal Intellgence, Vol. 134, pp (2008). 7

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