**6. Conclusion**

220 Security Enhanced Applications for Information Systems

The main steps in the process of navigating and querying of a semantic model can be

3. Filter the entities using a semantic relation e.g. *rdf:type* in order to retrieve all instances

4. Filter the entities with the faceted navigation filter e.g. retrieve the personal data for a

5. After reviewing the results, the user may wish to continue navigating the information space by following relations between instances e.g. *foaf:PrimaryTopic-1* can be selected to link the instance *Janev* with its personal profile document *1526-PPD* (see Fig. 5)*.* Links to the MPI document base that stores the publications and other documents created in the

1. Select a knowledge base e.g. Organization and Personal profiles;

MPI working process are framed in red (Janev et al., 2010).

Fig. 5. Personal profile document of instance Janev-PPD.

Once available on the Web, expert profiles can be searched with Semantic search engines,

**5.2 Searching Web of Data using Sig.Ma** 

e.g. Sig.Ma.

summarized as follows.

of type *foaf:Person*;

2. Select a semantic concept e.g. *foaf:Agent*;

person with a surname *Janev;*

Taking into account the new trends in the design and implementation of enterprise information systems (based on adaptable, flexible, and open IT architecture, using open standards and emerging technologies), this Chapter introduced new insight into expertise management and proposed the Semantic Web-based approach to HR data representation, integration and retrieval.

**Ontology-based approach to competency management**: The proposed ontology-based approach to competency management includes establishment of a modular knowledge base of expert profiles and population of the knowledge base with information extracted from different HR related sources. The proposed approach based on emerging technologies and tools does not complement the existing information - integration approach (e.g. integrating the expert data in a form of a database) or the content management approach (e.g. integrating the experts' documents in a form of a document base), but it rather extends, enhances and integrates them with the aim to obtain a complete picture of the available resources.

**Explicit, standard format of expert profile that facilitates data interoperability and expert search:** As the interoperability between different knowledge organization schemas is one of the major Linked Open Data issues, the design of the semantic knowledge model in this Chapter was based on public vocabularies such as FOAF, DOAC, SIOC, DOAC, BibTeX, as

Building Expert Profiles Models Applying Semantic Web Technologies 223

Bordea, G., (2010). Concept Extraction Applied to the Task of Expert Finding. *The Semantic* 

Bordea, G., & Buitelaar, P. (2010). Expertise Mining. In *Proceedings of the 21st National Conference on Artificial Intelligence and Cognitive Science,* Galway, Ireland, 2010 Bizer, C., Heese, R., Mochol, M., Oldakowski, R, Tolksdorf, R, Eckstein, R. (2005). The

Draganidis, F., Chamopoulou, P., & Mentzas, G. (2006). An ontology-based tool for

Draganidis F., & Mentzas, G. (2006). Competency based management: a review of systems and approaches. *Information Management and Computer Security 14(1)*: 51 – 64 Fang, H., & Xiang Zhai, C. (2007). Probabilistic models for expert finding. In *Advances in* 

Gruber, T.R. (1993). A translation approach to portable ontology specification. *Knowledge* 

Houtzagers, G. (1999). Empowerment, using skills and competence management.

Janev, V., & Vraneš, S. (2011a). *Semantic Web Tools and Technologies for Competence* 

Janev, V., & Vraneš, S. (2011b). Applicability assessment of Semantic Web technologies. *Information Processing & Management*, 47:507–517, doi:10.1016/j.ipm.2010.11.002 Janev, V., Mijović, V., & Vraneš, S. (2010). Automatic extraction of ICT competences from

Jung, H., Lee, M., Kang, I.-S., Lee, S.-W., & Sung, W.-K. (2007). Finding topic-centric

Müller-Riedlhuber, H. (2009). The European Dictionary of Skills and Competences (DISCO):

Paquette, G. (2007). An Ontology and a Software Framework for Competency Modelling

Petkova, D., & Bruce Croft, W. (2006). Hierarchical language models for expert finding in

*SEMANTICS '09, 2-4 September 2009, Graz, Austria* (pp. 467 – 479)

and Management. *Educational Technology & Society 10* (3): 1-21

*with Artificial Intelligence* (pp. 599 – 608). IEEE Computer Society

*Participation & Empowerment: An International Journal (2):*27-32

Publishing GmbH & Co. KG, 2011. ISBN: 978-3-8454-4166-5

CCIS 110 (pp. 391-400). Berlin / Heidelberg: Springer

6089/2010, 451-456, DOI: 10.1007/978-3-642-13489-0\_42

*Conference Wirtschaftsinformatik (WI 2005), Bamberg, Germany*

*Learning, 6th September 2006*, Graz , Austria

Berlin / Heidelberg: Springer

*acquisition*, *5(2):* 199-220

*Psychologist 20*:321-33

*Web: Research and Applications, Lecture Notes in Computer Science*, 2010, Volume

Impact of Semantic Web technologies on job recruitment processes. *International* 

competency management and learning paths. In *Proc. I-KNOW '06, 6th International Conference on Knowledge Management*, *Special track on Integrating Working and* 

*Information Retrieval*, *Lecture Notes in Computer Science, Vol. 4425* (pp. 418-430).

*Management: The Case Study of R&D Organization*. LAP LAMBERT Academic

unstructured sources. In J.E. Quintela Varajăo et al. (Eds.), *Proceedings of the CENTERIS 2010 - International Conference on ENTERprise Information Systems*, Part II,

identified experts based on full text analysis. In A.V. Zhdanova, L.J.B. Nixon, M. Mochol, J. G. Breslin (Eds.), *Finding Experts on the Web with Semantics 2007, Proceedings of the 2nd Intl. ISWC+ASWC ExpertFinder Workshop (FEWS'07), Busan, Korea, November, 2007*. Retrieved August 5, 2008 from CEUR-WS.org/Vol-290/ McClelland, D. (1973). Testing for competence rather than for intelligence. *American* 

an example of usage scenarios for ontologies. In *Proceedings of I-KNOW '09 and I-*

enterprise corpora. In *Proceedings of the 18th IEEE International Conference on Tools* 

well as common vocabularies for modelling case study specific data and relations such as DC, RDF, RDFS, and OWL.

**Enhancing self-declared expertise with competences automatically and objectively extracted using text analysis:** Taking into consideration that self declared expertise cannot be an exhaustive description of the person's expertise areas, the use of text analysis tools for updating the semantic expert profiles with uncovered *latent* knowledge should be considered.

**Meaningful search and retrieval of expertise:** Recently, a new search approach has emerged. It has been named faceted search that combines the navigational search paradigm and the direct, keyword search paradigm. Faceted search methods augment and improve traditional search results by using not just words, but concepts and logical relationships that are components of an ontology. Faceted navigation techniques and semantic relations shorten the search time, improve the relevance of search results, and deliver high-quality search services. This Chapter demonstrates the use of these methods in practice.
