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 Department of Computer Scence, Unversty of Toronto 10 Kng's College Road, Toronto, ON, M5S 3G4, CANADA Emal: Mar S. Fox Department of Mechancal and Industral Engneerng, Unversty of Toronto Emal:

2 Abstract Human Resources Management (HRM) s the strategc management of the employees, who ndvdually and collectvely contrbute to the achevement of the strategc goals of an organzaton. Many HRM tass are based on locatng and matchng ndvduals to postons. In ths paper we present an ontology-based hybrd approach to effectvely match job seeers and job postngs. The approach uses a deductve model to determne the nd of match between a job seeer and a postng, and apples a smlarty-based approach to ran applcants. Keywords. Semantc Hybrd Matchng, Recrutment, Job Search

3 Semantc Matchmang for Job Recrutment: An Ontology-Based Hybrd Approach In today s compettve busness envronment, companes need to accurately grasp the competency of ther human resources n order to be successful. Ths s partcularly mportant for organzatons that engage wth multple and changng clents such as consultng frms and software development companes snce these organzatons need to be able to flexbly respond to nternal and external demands for slls and competences. As such, t s often necessary to reason about slls and competences of ndvduals. Ths s the case for human resource recrutng, selectng ndvduals for teams based on dfferent slls and qualfcatons, determnng who to tran and what tranng program to offer, and recommendng the rght expert to ndvduals for acqurng nformaton or learnng from wthn the organzaton. In order to facltate the management of avalable human resources competences, provde a global vew of competences avalable at the organzatonal level, and perform qualtatve and quanttatve reasonng about avalable and requred slls and competences, the development of totally or partally automated technques has receved the attenton of both researchers and organzatons (e.g., Colucc et al, 2003; Bzer et al, 2005; Malnows et al 2006). In addton, the Internet has also been ncreasngly used for HRM purposes n recent years. For human resource recrutng, for example, the Internet s currently beng manly used to place onlne job advertsements, to perform resume search, and to acqure nformaton about slls and competences of ndvduals (Dafoulas et al, 2003). The Internatonal Assocaton of Employment Web Stes 2 reports that there are more than 40,000 employment stes servng job seeers, employers and recruters worldwde. The man reasons for the use of onlne resources are the opportunty to reach and attract a larger number of ndvduals and the ablty to process and trac a larger number of applcatons faster and more costeffectvely (Laumer and Echardt, 2009). In ths wor, we focus on locatng and matchng ndvduals and postons, a process mportant for hrng and team staffng. Dfferent matchmang approaches exst n the lterature whch can be used for matchng ndvduals to job requrements. For example, typcal text-based nformaton retreval technques such as database queryng and smlarty between weghted vectors of terms have been used n prevous wors (Vet et al, 2006). Technques for ontology-based sll-profle matchng have also been consdered. (Lau and Sure, 2002) proposes an ontology-based sll management system for elctng employee slls and searchng for experts wthn an nsurance company. (Lu and Dew, 2004) presents a system whch ntegrates the accuracy of concept search wth the flexblty of eyword search to match expertse wthn academa. (Colucc et al, 2003) proposes a semantc based approach to the problem of slls fndng n an ontology supported framewor. They use descrpton logc nferences to handle the bacground nowledge and deal wth ncomplete nowledge whle fndng the best ndvdual for a gven tas or project, based on profle descrptons sharng a common 2

4 ontology. Approaches for calculatng the structural smlarty between nstances on the bass of ontologes have also been consdered. (Bzer et al, 2005) and (Mochol et al, 2007), for example, present a scenaro for supportng the recrutment process wth semantc web technologes wthn the German Government whch uses (Zhong et al, 2002) s smlarty measure to evaluate the degree of match between job offers and applcants. In general, matchmang strateges that are based on purely logc deductve facltes present hgh precson 4 and recall 5, but are often characterzed by low flexblty (Banchn et al, 2007). Smlarty-based approaches, on the other hand, are characterzed by hgh flexblty, but lmted precson and recall (Banchn et al, 2007). Flexblty refers to the ablty to recognze the degree of smlarty when an exact match does not exst. Havng flexble matchmaers s of fundamental mportance partcularly n the context of human resources recrutment snce n real world stuatons t s rarely the case that ndvduals match all the requred competences for a job. Although some scholars (e.g., Bzer et al 2005) have proposed usng taxonomc smlarty to ran applcants, the usefulness of ths technque n dfferent contexts and envronments s not clear. There may be some cases, for example, where the s-a relaton s not suffcent to express the relaton between dfferent slls. Let us gve a smple example. Assume we need someone wth object-orented programmng slls. If an employee nows Smalltal programmng then we can conclude that ths person qualfes, snce Smalltal s a pure object-orented programmng language and as such Smalltal programmng s a specalzaton of object-orented programmng. However, f we have a C++ programmer, we cannot mae such a strong concluson snce although C++ supports object-orented programmng one does not have to program n such way n C++. To mprove the matchng process and provde an adaptve, flexble and effcent job offerng and dscovery envronment, we combne dfferent matchmang strateges. We propose to frst use a deductve model to determne the nd of match between an ndvdual and a job postng, and then based on the nd of match determne the smlarty measure to use n order to ran the applcants wth partal match. The remander of ths paper s organzed as follows: Secton 1 presents the underlyng ontology. Secton 2 descrbes the matchmang model, and Secton 3 presents the ranng algorthm. Fnally, Secton 4 concludes the paper wth a dscusson of contrbutons made and areas of future wor. Ontologcal Framewor In human resource recrutng, two perspectves are dstngushed. A job seeer creates an applcaton by specfyng hs/her academc bacground, prevous wor experence, and set of 4 Precson s a measure of exactness or fdelty. In nformaton retreval, t s the number of relevant documents retreved by a search dvded by the total number of documents retreved by that search. 5 Recall s a measure of completeness. In nformaton retreval, t s the number of relevant documents retreved by a search dvded by the total number of exstng relevant documents.

5 competences. A recruter, on the other hand, creates a job postng n the form of a set of requrements n terms of job related descrptons and constrants on slls, profcency levels, and/or degrees. We use descrpton logcs (DL) wth rules to represent and reason about applcatons and job postngs. The expressons can be represented n OWL-DL, correspondng to the SHOIN(D) famly of descrpton logcs. For smplcty when wrtng rules we use p to denote slled person, c denote competence, s denote sll, j denote job postng, fl denote formal learnng actvty, nfl denote formal learnng actvty, d denote degree program learnng actvty, and e denote experence. Sll There are several defntons of competency present n the lterature (De Co et al, 2007). The defnton we assume s the one gven by the HR-XML Consortum wor group 6 : a specfc, dentfable, defnable, and measurable nowledge, sll, ablty and/or other deployment-related characterstc (e.g. atttude, behavor, physcal ablty) whch a human resource may posses and whch s necessary for, or materal to, the performance of an actvty wthn a specfc busness context. We adopt ths defnton for ts emphass on measurable nowledge and slls and the connecton between competences and actvty performance. Hereafter, we focus on measurable slls possessed by human resources and may use sll nstead of competency 7. We use the term competence to refer to a sll along wth a level of profcency. We assume slls n a specfc doman of nterest. For our reference ontology, slls are semantcally organzed n a sll taxonomy (.e., sll specalzaton/generalzaton wth an s-a semantcs). We do not nclude slls related to specfc tools and technologes n ths taxonomy. Ths s due to the fact that tools and technologes used may provde dfferent set of functonaltes and capabltes. The Smalltal-C++ example gven n the ntroducton falls nto ths category. As another example, consder the sll of worng wth Mcrosoft Offce Excel. Ths may suggest competency n worng wth spreadsheets, plottng graphs, and/or macro programmng. To nclude slls related to specfc tools and technology, we extend our smple sll taxonomy wth the part-of relaton. For example, n the above stuaton object-orented programmng would be defned as part-of C++ programmng. In addton to s-a and part-of relatons, we defne the symmetrc alternatve-for relaton between two tools or technologes. Two slls related to tools and technologes can be thought of as alternatves f at least one sll exsts that s part-of both of them. For example, worng wth Java Servlet s an alternatve-for worng wth JSP, or programmng n Java s an alternatve-for worng wth C The reader should note that sll s not a synonym for competency, as t only covers part of ts scope.

6 Competence In our model, competences are general descrptons, ndependent of specfc ndvduals or job descrptons. A competence statement refers to a sll along wth a profcency level. Dfferent quanttatve and qualtatve measurement scales exst for evaluatng an ndvdual aganst a sll. Examples nclude Ratng scales, Behavorally Anchored Ratng scales, and Threshold scales (Moyer, 2001). Ratng scales are the most popular and typcally consst of a numerc scale wth a bref descrpton of each number s correspondng meanng. The dsadvantage of these scales, however, les n ther nconsstent nterpretatons across users of a scale (Moyer, 2001). To overcome the dsadvantage of ratng scales, we defne a profcency level n terms of the requred level of nowledge and years of experence. We dstngush between four levels of nowledge: basc, ntermedate, advanced, and expert nowledge (expert subsumes advanced whch n turn subsumes ntermedate whch n turn subsumes basc). The years of experence s specfed as the mnmum number of years requred. Competence =1 refers-to =1 has-nowledge-level =1 has-years-of-experence (D-1) Usng the HR-XML defnton, havng a partcular sll becomes tghtly bound to the evdence that suggests one has the certan sll at a partcular level of profcency. The evdence also helps n understandng how a sll can be acheved, whch s especally useful for arrangng tranng programs. In ths regard, we dstngush between learnng actvty and demonstraton of a sll. A learnng actvty 8 (LA) s an actvty that has one or more learnng outcomes assocated wth t and occurs wthn a partcular context 9 (Gránne and Fll, 2005). A learnng outcome s what the learners should now or be able to do after completng the LA. Demonstraton of a sll, on the other hand, ndcates the experence one has n performng the tass that requre the partcular sll. A demonstraton of a sll s represented by the concept: WorExperence =1 hasposton =1 atorganzaton =1 has-start-date =1 has-end-date requres.competence (D-2) A learnng actvty can ether be formal or non-formal. Fgure 2 llustrates the learnng actvty taxonomy. Formal learnng occurs as a result of nstructor-led programs wthn the currcula of educatonal nsttutons or the courses or worshops offered by dfferent agences (Schugurensy, 2000). Non-formal learnng, on the other hand, nvolves the pursut of nowledge or slls outsde such settngs, for example, learnng acheved through readng boos, engagng n self-study programs, or collaboratng wth communtes of practce. 8 The defnton of a learnng actvty can be extended to nclude learnng and teachng approaches adopted and the tass undertaen. 5 The valdty and relablty of the assessments of the outcome of the learnng actvtes are outsde the scope of ths paper.

7 Learnng Actvty Formal Learnng Non-Formal Learnng Self-Drected Socalzaton Degree Program Worshop Communtes of Practce Fgure 2. Learnng actvty classfcaton Formal learnng actvtes can have a set of competences as requred precondtons (hasprecondton), but must have at least one competence statement as outcome (has-outcome). For nonformal learnng actvtes the set of precondtons and outcomes may not be so clear. For these actvtes we defne the relaton covers ndcatng that a non-formal learnng actvty covers topcs related to a certan sll. For brevty we only nclude the defnton for degree program wth wll be used later for slls-requrements matchng: DegreeProgram FormalLearnng =1 has-degree-ttle =1 has-study-feld =1 from-nstuton =1 has-start-date =1 has-end-date.date (D-3) Havng these defntons, we can defne a slled person as a person who has taen some learnng actvtes, has some wor experences, and has a set of competence statements: SlledPerson Person 0 has-taen 0 has-experence 1 has-competence (D-4) Consderng learnng actvtes, we can nfer that an ndvdual has a sll at a level of profcency f the ndvdual has completed a formal learnng actvty and the sll s ether a precondton or outcome of the learnng actvty: has-taen(p,fl) has-precondton(fl,c) has-competence(p,c) (R-1) has-taen(p,fl) has-outcome(fl,c) has-competence(p,c) (R-2) If, however, the ndvdual has partcpated n a non-formal learnng actvty, then t can only be suggested that the ndvdual may have the desred sll: has-taen(p,nfl) covers(nfl,s) may-have-sll(p,s) (R-3) Consderng demonstraton of a sll, we can nfer that an ndvdual has a competence f s/he has an experence whch requres the related sll at a partcular level of profcency: has-experence(p,e) requres(e,c) has-competence(p,c) (R-4)

8 If the experence requres a sll related to the use of a tool or technology, then the use of the tool can suggest havng the slls that are part of t: has-experence(p,e) requres(e,c) refers-to(e,s) part-of(s,s) may-have-sll(p,s) (R-5) Job Postng We defne a job postng as a set of requrements n terms of job related descrptons and constrants on competences. Every job postng s represented usng the DL formalsm as the conjuncton of: A concept n the form has-descrpton.jobdescrpton, where JobDescrpton =1 has-poston-ttle =1 has-bref-descrpton =1 has-category =1 at-company has-functon.jobfuncton (D-5) Example categores nclude admnstratve, engneerng, and customer care. One or more concepts n the form has-requrement.competence representng the set of requred competences for the job. Zero or more concepts n the form has-degree-requrement.degreerequrement representng requred degree program learnng actvtes; DegreeRequrement =1 requresdegree =1 requresfeld (D-6) Zero or more concepts n the form has-nce-to-have-requrement.desre, where Desre Competence =1 hasdesrelevel (D-7) where, hasdesrelevel can tae an nteger value n the range [1, 10]. Slls-Requrements Matchmang When searchng for jobs (or applcants), a job seeer (or recruter) can as for all job postngs (or applcatons) that match her applcaton. In slls-requrements matchmang, we are nterested n determnng whether or not an ndvdual satsfes a set of requrements. We dstngush between must-have and nce-to-have requrements when matchng. Must-have requrements are hard constrants whereas nce-to-have requrements are soft constrants (or preferences) that are taen nto account when ranng. We propose to frst use a deductve model to determne the nd of match between an ndvdual and a job descrpton, and then based on the nd of match determne the smlarty measure to use n order to ran the applcants wth partal match.

9 Logc-Based Matchng Let P be a job postng wth a set of requrements { d _ req P, -th degree requrement, and conjuncton of the followng terms: }, where d _ req P s the s the -th competence requrement of P. Let D be the For each d _ req P, requrng degree d n feld f, term = has-taen.(has-degree-ttle.d has-study-feld.f ) A qualfed match denotes that an ndvdual satsfes all the requred competence and degree requrements of P. In order to determne a qualfed match, we create a new concept C 1 as a conjuncton of D and the followng terms: For each, term = has-competence. All nstances of C 1 are qualfed matches for P. c _ req P In real world stuatons, however, t rarely happens that applcatons match all the requrements specfed n a job postng. A gap between the set of requrements and the set of competences of an ndvdual may exst for dfferent reasons. It mght be the case that an ndvdual s not profcent enough n a specfc sll or n worst case does not satsfy a competence requrement at all. For ths, n addton to the qualfed match, we consder dfferent types of under-qualfed matches. For the frst type of under-qualfed match, we relax the requred profcency level constrants. In ths case, an applcaton s consdered to be profcency-under-qualfed match for job postng P f and only f 11 the requred profcency level for one or more slls s not satsfed. To determne such a match, we create a new concept C 2 as a conjuncton of D and the followng terms: For each referrng to sll s, term = has-competence. All nstances of C 2 are profcency-under-qualfed matches for P. has-competence.(refers-to.s ) The second type of under-qualfed match, competency-under-qualfed match, taes nto account the fact that t s not always the case that all the requred slls are present n an applcaton. For ths type of match we frst consder ndvduals who may have the mssng sll(s). To determne such a match, we create a new concept C 3 as a conjuncton of D and the followng terms: For each referrng to sll s, term = has-competence. may-have-sll.s All nstances of C 3 are competency-under-qualfed-case-1 matches for P. 11 For now we consder all degree requrements to be hard constrants.

10 Fnally, we consder all ndvduals who satsfy a subset of the requred competences. In order to reduce the search space, we frst fnd all ndvduals who satsfy at least one of the requred competences. Next, for each applcaton found we solve a Concept Abducton Problem (CAP) (Colucc et al, 2007) to fnd the mssng slls. The soluton of a CAP can be nterpreted as what has to be hypotheszed n an applcaton A j and added to t n order to mae t a match for P. To do ths, we solve a CAP for each A j and a new concept C 5 whch s a conjuncton of the followng terms: For each referrng to sll s, term = has-competence.(refers-to.s ) Havng the soluton to the CAP for each A j, we can consder only those that have fewer mssng slls. To acheve a better match t s 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 chec to see f A j satsfes ths new requrement. Smlarty-Based Ranng In order to ran the applcatons matched to a job descrpton, we need to consder two scenaros. The frst scenaro nvolves ranng the set of under-qualfed applcatons. The second scenaro nvolves consderng nce-to-have requrements or preferences for fndng the most sutable applcants n the set of all applcatons. Ranng Under-qualfed Applcants To ran applcatons that are profcency-under-qualfed, we defne a dssmlarty measure and ran applcants accordngly. dssmlarty( P, j) [( P j )( P )] j 2 [( e P e j )( e P e )] j 2 where, ( e ) s the normalzed requred nowledge level (experence) of sll requrement of P, P P and ( e ) s the normalzed nowledge level (experence) of applcaton A j for the matchng sll. j j In case one crteron s more mportant than the other, t s possble to consder a weghted sum of nowledge level and experence. To ran applcatons that are competency-under-qualfed-case-1, we smply count the number of may-have slls and ran applcants accordngly. To ran applcants that are mssng one or more slls, we consder the sze of the set of ther mssng slls. We then use the dssmlarty measure to ran those applcants that have the same number of mssng slls. Consderng Nce-To-Have Requrements for Ranng Applcants To fnd the most sutable applcatons n the set of all matched applcatons (both qualfed and under-qualfed), we tae nto account the desre level values, u(ds ), assgned to each nce-to-have

11 requrement (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: sm P, j) x u( ds ) ( j where, x j s the Boolean varable ndcatng whether desre s satsfed by applcant A j n the set of all qualfed applcatons. To calculate x j, for each desre a term smlar to term s created and then nstance checng s done to see f A j s an nstance of ths term. Note that ths functon s used to ran applcatons that are consdered equally good wth respect to the prevous measures. Emprcal Results We have collected data on ndvdual s slls from an e-retal company and tested our approach wth ths data to compare the dfferent matchng and ranng crtera. We wll send you the results once they are fnalzed. Conclusons and Future Drectons Ths paper presented an approach to matchng job seeers and job postngs whch taes advantage of the benefts of both logc-based and smlarty-based matchng. In other words, ths hybrd approach presents hgh precson and recall whle beng flexble. The approach frst uses a deductve model to determne the nd of match between an ndvdual and a job postng, and then based on the nd of match determnes the smlarty measure to use n order to ran the applcants wth partal match. In addton to satsfyng advertsed job requrements, other factors such as recommendatons, 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 may arse due to ftness between an ndvdual and other team members. It would be nterestng to see how these complextes can be supported by automated technques. The bass for HRM s the accurate grasp of the competency of human resources. Currently the approach reles on self declaratons of learnng actvtes and experences whch can be naccurate or nsuffcent. In addton, assessments need to be vald and relable. Assessment results often lac valdty and relablty due cogntve bases and nablty to adequately gather human resource growth nformaton among other thngs (Seta et al, 2005). It would be nterestng to use mechansms to automatcally dscover up-to-date competency 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 (Fazel-Zarand and Yu, 2008). Another useful ontology n ths regard s the organzaton ontology (Fox et al, 1997) 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 (Huang, 2008) are other

12 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 competency recognton and extracton and reduce fluctuaton n competency evaluaton. Acnowledgements Ths research s supported, n part, by the Natural Scence and Engneerng Research Councl of Canada. References Banchn, D.; De Antonells, V.; Melchor, M. (2007). Flexble Semantc-Based Servce Matchmang and Dscovery, World Wde Web Journal. Badca, C.; Popescu, E.; Fracowa, G.; Ganzha, M.; Paprzyc, M.; Szymcza, M.; Par, M.W. (2008) On Human Resource Adaptablty n an Agent-Based Vrtual Organzaton; Studes n Computatonal Intellgence, Vol. 134, pp Besals, E.; Abecer, A. (2005) Human Resource Management wth Ontologes, Wssensmanagement. Professonal Knowledge Management, Thrd Bennal Conference, LNAI Vol. 3782, Bzer, C., Heese R., Mochol, M., Oldaows, R., Tolsdorf, R., Ecsten, R. (2005) The Impact of Semantc Web Technologes on Job Recrutment Processes; n Proceedngs of the 7th Internatonal Conference Wrtschaftsnformat. Colucc, S.; D Noa, T.; D Scasco, E.; Donn, F.M.; Mongello, M.; Mottola, M. (2003) A Formal Approach to Ontology-Based Semantc Match for Slls Descrptons, Journal of Unversal Computer Scence, Vol. 9/12, Colucc, S.; D Noa, T.; D Scasco, E.; Donn, F.M.; Ragone, A. (2007). Semantc-based Sll Management for Automated Tas Assgnment and Courseware Composton, Journal of Unversal Computer Scence, Vol. 13/9, De Co, J.; Herder, E.; Koeslng, A.; Lof, C.; Olmedlla, D.; Papatreou, O.; Sbers, W. (2007) A Model for Competence Gap Analyss, n Proceedngs of 3rd Internatonal Conference on Web Informaton Systems and Technologes (WEBIST). Dttmann, L. (2003) Towards Ontology-based Sll Management, Projetbercht zum Verbundprojet KOWIEN, Unverstät Dusburg-Essen. Dorn, J.; Naz, T.; Pchlmar, M. (2007) Ontology Development for Human Resource Management, n Proceedngs of the 3 rd Internatonal Conference on Knowledge Managements. Fazel-Zarand, M.; Yu, E. (2008) Ontology-Based Expertse Fndng, n Proceedngs of the 7 th Internatonal Conference on Practcal Aspects of Knowledge Management (PAKM 2008), Lecture Notes n Computer Scence, Vol, 5345, pp

13 Fox, M.S., Barbuceanu, M., Grunnger, M., Ln, J. (1997) An Organzaton Ontology for Enterprse Modellng. In: Pretula, M., Carley, K., Gasser, L. (eds.) Smulatng Organzatons: Computatonal Models of Insttutons and Groups, AAAI/MIT Press, Menlo Par, pp Gomez-Perez, A.; Ramrez, J.; Vllazon-Terrazas, B. (2007) Reusng Human Resources Management Standards for Employment Servces, n Proceedngs of the Worshop on the Frst Industral Results of Semantc Technologes (FIRST 2007) at ISWC/ASWC, pp Gránne, C.; Fll, K. (2005) A Learnng Desgn Toolt to Create Pedagogcally Effectve Learnng Actvtes, Journal of Interactve Meda n Educaton, Vol. 8. Huang, J. (2008) Knowledge Provenance, An Approach to Modelng and Mantanng the Evoluton and Valdty of Knowledge, PhD Thess, Department of Mechancal and Industral Engneerng, Unversty of Toronto, Canada. Lau, T.; Sure, Y. (2002) Introducng Ontology-Based Slls Management at a Large Insurance Company, n Proceedngs of the Modellerung, Lu, P.; Dew, P. (2004) Usng Semantc Web Technologes to Improve expertse Matchng wthn Academa, n Proceedngs of I-KNOW 04, Mochol, M.; Wache, H.; Nxon, L. (2007) Improvng the Accuracy of Job Search wth Semantc Technques, n Proceedngs of the 10th Internatonal Conference on Busness Informaton Systems (BIS 2007), Lecture Notes n Computer Scence, Vol. 4439, pp Moyer, R. (2001) Defnng and Measurng Competences: An Overvew of Approaches, Clc2learn Inc. Razmerta, L.; Angehrn, A.; Maedche, A. (2003) Ontology-based User Modelng for Knowledge Management Systems; n User Modelng, Pennsylvana, Rech, J.R.; Brochausen, P.; Lau, T.; Remer, U. (2002) Ontology-Based Slls Management: Goals, Opportuntes and Challenges, Journal of Unversal Computer Scence, Vol. 8/5, Sap, H.B.; Lucches, C. (1993). Matchng Algorthms for Bpartte Graph, Techncal Report DCC- 0393, Unversdade Estudal de Campnas. Schmdt, A.; Kunzmann, C. (2006) Towards a Human Resource Development Ontology for Combnng Competence Management and Technology-Enhanced Worplace Learnng, n Lecture Notes n Computer Scence Vol. 4278, Sprnger, Schugurensy, D. (2000) The Forms of Informal Learnng: Towards a Conceptualzaton of the Feld, NALL Worng Paper No. 19. Seta, K.; Ieda, M.; Hrata, K.; Hayash, Y.; Kuryama, K. (2005) A Human Resource Model and Evdence Based Evaluaton--Ontology for IT Sll Standards--. In Proceedng of the 2005 Conference on Towards Sustanable and Scalable Educatonal Innovatons Informed by the Learnng Scences,

14 Vet, D.; Muller, J.; Schnder, M; Fehn, B. (2001). Matchmang for Autonomous Agents n Electronc Maretplaces. In Proceedngs of the Internatonal Conference on Autonomous Agents 01, Zhong, J.; Zhu, H.; L, J.; Yu, Y. (2002). Conceptual Graph Matchng for Semantc Search. In Proceedngs of the 10 th Internatonal Conference on Conceptual Structures,