**3. State-of-the-art analysis of HR standards and literature**

#### **3.1 Analysis of classical approaches to expertise management**

The actual HRM solutions mainly focus on the integration of the distributed legacy databases, typically in the form of the data warehouse where the fact data (i.e. employee data) is arranged in order to answer the analytical queries efficiently. Personal profiles here usually rely on the self declared expertise. Employees keep track of their areas of expertise manually by maintaining a list of keywords or phrases and this list of key qualifications is being defined in the HR sector. This approach is error-prone since users are typically subjective/biased and reluctant to update the file regularly. Also, manually created lists cannot be an exhaustive description of the person's expertise areas. In addition, content based approaches (Sim et al., 2006) to expertise extraction, profiling and finding have been introduced lately that focus on the automatical identification of the expertise entities in the semi-structured and unstructured documents containing the expertise information as well as on the annotation of the identified expertise entities with the semantic mark-up. The input documents are: (1) curricula vitae and résumé that have been published in formats such as text, PDF, DOC and HTML; (2) publications and other legacy documents (Balog et al., 2006; Balog & de Rijke, 2008); (3) e-mails, blog sites and other online social networking related context (Aleman-Meza et al., 2007; Schäfermeier & Paschke, 2011). The expertise extraction and profiling is based on the linguistic analysis, statistical and machine learning classification methods as well as on the inductive logic programming techniques to discover rules for extracting fields from documents (Fang & Xiang Zhai, 2007; Petkova & Bruce Croft, 2006; Jung et al., 2007). Inspired by different research fields such as expert finding, competency management, terminology extraction, keyword extraction and concept extraction (Bordea, 2010), Bordea and Buitelaar (2010) proposed a hybrid approach and the Saffron system for expert profiling and finding.

Building Expert Profiles Models Applying Semantic Web Technologies 213

project (Müller-Riedlhuber, 2009). Some of them have already introduced (e.g. Germany, Norway) or are at the moment working on improvements of their matching (vacancies and

HR-XML HR-XML Consortium Competencies Schema, http://ns.hr-xml.org/ SOC The 2010 Standard Occupational Classification (SOC, www.bls.gov/soc)

occupations, 97 minor groups, and 23 major groups. O\*NET The Occupational Information Network (O\*NET,

DISCO European Dictionary of Skills and Competencies, financed by EU

e-CF European e-Competence Framework, a reference framework of 32 ICT

ESCO The European Skills, Competences and Occupations taxonomy (under

To represent information on the Web and to ensure interoperability between applications that exchange machine-understandable information, the Semantic Web uses the Resource Description Framework (RDF) as a general-purpose language. RDF describes information in terms of objects ("resources") and the relations between them via the RDF Schema, which serves as a meta-language or vocabulary to define properties and classes of RDF resources. The next layer on top of the RDF/RDFS data model serves to formally define domain models as shared conceptualizations, also often called ontologies (Gruber, 1993). Ontologies are nowadays very often used for building integrated inter- and intraorganization business services, and to make the search and retrieval both efficient and meaningful. In this Section we will use the RDF and OWL languages to introduce the most important concepts and relations between concepts relevant for building an expert

competences, http://www.ecompetences.eu/.

system is used by Federal statistical agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of 840 detailed occupations according to their occupational definition. To facilitate classification, detailed occupations are combined to form 461 broad

http://www.onetcenter.org, based on SOC) is designed to be the nation's most comprehensive resource of occupational information, with a database system that includes 275 descriptors about each occupation.

Leonardo da Vinci programme & the Austrian Federal Ministry for Education, the Arts and Culture, http://www.skills-translator.net/

development). A partial classification is already in use in the European job mobility portal EURES (http://ec.europa.eu/eures/). It exists in 22 languages and currently contains around 6000 skill descriptions and 5000

job seekers) processes by shifting more emphasis to competences.

Acronym HR Initiative

job titles.

**4. Explicit representation of an HR knowledge store** 

Table 1. International HR initiatives

profile (see Figure 2).

Fig. 1. Linking enterprise resources to the LOD cloud.

#### **3.2 Review of standards and literature for ontology-based competence management**

European Union, through its chief instruments for funding research (FP5 - The Fifth, FP6 – The Sixth and FP7 – The Seventh Framework Programs), has financed several projects that focused on ontology-based competency management. As a result of these projects, several prototype systems have been developed (Bizer et al., 2005; Draganidis et al., 2006) and few ontologies were made publicly available. The developed HR ontologies (Bizer et al., 2005; Müller-Riedlhuber, 2009) are based on widespread used standards and classifications of job profiles and industry sectors such as SOC (Standard Occupational Classification System, www.bls.gov/soc/), NAICS (North American Industry Classification System, see http://www.census.gov/epcd/www/naics.html), NACE (Statistical Classification of Economic Activities in the European Community, see http: //ec. europa. eu/ eurostat/ ramon/), HR-XML (HR-XML Consortium, www.hr-xml.org) and other.

Schmidt & Kunzmann (2006) developed the Professional Learning Ontology that formalizes competencies as a bridge between human resource development, competence and knowledge management as well as technology-enhanced learning. In (Bizer et al., 2005), Bizer developed a HR ontology, an e-recruitment prototype and argued that using Semantic Web technologies in the domain of online recruitment could substantially increase market transparency, lower the transaction costs for employers, and change the business models of the intermediaries involved. In (Paquette, 2007), the author presented a competency ontology and the *TELOS Software Framework for Competency Modelling and Management*.

Furthermore, the research work in the competence management domain in the last decade had a positive impact on several European Public Employment Services, e.g. see DISCO

 Find / acquire expert Find partner organization

**3.2 Review of standards and literature for ontology-based competence management**  European Union, through its chief instruments for funding research (FP5 - The Fifth, FP6 – The Sixth and FP7 – The Seventh Framework Programs), has financed several projects that focused on ontology-based competency management. As a result of these projects, several prototype systems have been developed (Bizer et al., 2005; Draganidis et al., 2006) and few ontologies were made publicly available. The developed HR ontologies (Bizer et al., 2005; Müller-Riedlhuber, 2009) are based on widespread used standards and classifications of job profiles and industry sectors such as SOC (Standard Occupational Classification System, www.bls.gov/soc/), NAICS (North American Industry Classification System, see http://www.census.gov/epcd/www/naics.html), NACE (Statistical Classification of Economic Activities in the European Community, see http: //ec. europa. eu/ eurostat/

Schmidt & Kunzmann (2006) developed the Professional Learning Ontology that formalizes competencies as a bridge between human resource development, competence and knowledge management as well as technology-enhanced learning. In (Bizer et al., 2005), Bizer developed a HR ontology, an e-recruitment prototype and argued that using Semantic Web technologies in the domain of online recruitment could substantially increase market transparency, lower the transaction costs for employers, and change the business models of the intermediaries involved. In (Paquette, 2007), the author presented a competency ontology and the *TELOS Software Framework for Competency Modelling and* 

Furthermore, the research work in the competence management domain in the last decade had a positive impact on several European Public Employment Services, e.g. see DISCO

ramon/), HR-XML (HR-XML Consortium, www.hr-xml.org) and other.

LOD2 sourcesfor expertise search

Management

HR

Products

Enterprise Resources

Publications

Sales

Processes

Fig. 1. Linking enterprise resources to the LOD cloud.

increase visibility of knowhow by publishing and marketing on the LOD2

Research

Patents

People

*Management*.

project (Müller-Riedlhuber, 2009). Some of them have already introduced (e.g. Germany, Norway) or are at the moment working on improvements of their matching (vacancies and job seekers) processes by shifting more emphasis to competences.


Table 1. International HR initiatives
