**4.1 Creating a new ontological model**

Unlike conventional object-oriented conceptual models like UML where attributes are bound to a specific class, classes and properties (as main entities of ontology) are equally important for ontology building. Therefore, prior to making a decision about the knowledge model design and structure, one have to enumerate the important terms that will be used, e.g. for the HR domain these are *Person*, *Organization*, *Document*, *Project*, *Publication*, *Author*, *Competence*, *Experience*, etc. After that, separate generalization hierarchies for classes and properties are designed. There are several possible approaches in developing a class hierarchy: the top-down, the bottom-up and the combination development process. The top-down development process starts with the definition of the most general concepts in the domain and subsequent specialization of the concepts. The bottom-up development process starts with the definition of the most specific classes, the leaves of the hierarchy, with subsequent grouping of these classes into more general concepts. The combination development process is a combination of the top-down and bottom-up approaches.

Unlike conventional object-oriented conceptual models like UML where attributes are bound to a specific class, classes and properties (as main entities of ontology) are equally important for ontology building. Therefore, prior to making a decision about the knowledge model design and structure, one have to enumerate the important terms that will be used, e.g. for the HR domain these are *Person*, *Organization*, *Document*, *Project*, *Publication*, *Author*, *Competence*, *Experience*, etc. After that, separate generalization hierarchies for classes and properties are designed. There are several possible approaches in developing a class hierarchy: the top-down, the bottom-up and the combination development process. The top-down development process starts with the definition of the most general concepts in the domain and subsequent specialization of the concepts. The bottom-up development process starts with the definition of the most specific classes, the leaves of the hierarchy, with subsequent grouping of these classes into more general concepts. The combination development process is a combination of the top-down and

Fig. 2. UML representation of the concept Expert.

**4.1 Creating a new ontological model** 

bottom-up approaches.

In a top-down manner, we can define the most general concepts/properties as subclasses / subproperties of entities from the public vocabularies FOAF, DOAC, and BibTeX (Aleman-Meza et al., 2007)and assign them meaning identical with the existing commonly used classes in the Semantic Web (see Table 2). In that way, the main "components" are defined as subclasses of the public concepts (*foaf:Person*, *foaf:Organisation*, *foaf:Document*, *foaf:PersonalProfileDocument*, *doac:Education*, *doac:Skill*, *doac:Experience, bibtex:Entry*), while links/relations between the components are defined as sub-properties of *foaf:interest*, *foaf:made/maker*, *foaf:topic*, *foaf:primaryTopic*, *foaf:homepage*, etc. Additional classes and properties specific to the domain of interest (e.g. in the ICT domain) can be defined manually with elements from the RDF Schema (www.w3.org/TR/rdf-schema/) or defined automatically in bottom-up manner e.g. using D2RQ server, http://www4.wiwiss.fuberlin.de/bizer/d2r-server.


Table 2. RDF models

For example, the Mihajlo Pupin Institute ontology (MPI) uses concepts from the DOAC+FOAF vocabulary and extends them with new concepts and properties defined in the *imp* and *skills* namespace as follows:

general description of an expert (*imp:Person rdfs:subClassOf foaf:Person*);

Building Expert Profiles Models Applying Semantic Web Technologies 217

IT-0001 Organizational Assignment *imp:inOrganization* (*imp:Organization*)

IT-0021 Family / Related Person *imp:hasFamilyMember* (*imp:Person*) IT-0022 Education *doac:education* (*doac:Education*)

IT-0023 Other/Previous Employers *imp:PartTimeEmployment, imp:referer* IT-0024 Qualifications *imp:Skill, imp:LanguageSkill, imp:refer*

*foaf:phone* 

details *doac:education* (*doac:Education*)

of Expertise *imp:keyQualification xsd:string*

Table 3. Establishing correspondence between implicit and explicit data representation

Figure 3 represent a screenshoot of mapping the facts from RDBMS tables to instances explicitly represented in the Institute "Mihajlo Pupin" knowledge store (Janev & Vraneš,

organizations *imp:isMemberOf* (*foaf:Organization*)

*foaf:holdsAccount* (*foaf:OnlineAccount*)

*imp:hasScientificRecord* (*imp: ScientificExperience*)

*foaf:homepage* (*foaf:Document*)

*imp:graduationTitle xsd:string*

*imp:responsibilitiesOnProjects* (*imp:ProjectReference*)

IT-0034 Corporate Function *imp:EmploymentType*

IT-0016 Contract Elements *imp:Document*

IT-0002 Personal Data *imp:Person* IT-0006 Addresses *imp:Address* IT-0009 Bank Details *imp:Bank*

IT-0185 Personal ID *imp:globalID*

Postgraduates studies -

Key qualifications and Areas

IT-9150 Awards, Appreciations *imp:hasAward*

IT-9170 References *imp:ScientificPaper*

Memberships in scientific

P010 Organization *imp:Organization* P013 Position *imp:JobPosition*

SAP HCM - Personnel Administration

IT-0105 Communication

IT-9110 MPI scientific titles

The Researcher file

IT-9160 Projects

IT-9120

IT-9130

IT-9140

2011a).

SAP HCM - Organizational Management

Organizational Data

Personal data


#### **4.2 From implicit to explicit data representation**

After the ontological knowledge base is designed, the next step is to populate the ontology i.e. import data into the ontology and create instances. Manually creating of ontologies is a time consuming task. Semantic Web community has delivered many high-quality opensource tools that can be used for automatic or semiautomatic ontology population i.e. to convert the facts trapped in the legacy systems or business documents into information understandable both for machines and people.

Professional HRM systems, e.g. the SAP Human Capital Management solution, cover the whole life-circle of an employee from her/his recruitment, training, development, and deployment to retirement. They enable tracking of employee movements and adequate tracking of changes in organizational structure. Furthermore, standard SAP HCM processes support skill management and give managers and HR professionals reporting and analysis options that provide a real-time insight into employee qualifications. As a result, the underlying (implicit) data base model is highly normalized and quite complex. Customizing the predefined SAP HCM functionalities or extending them with new client tailored functionalities require SAP consultancy efforts. Therefore, extracting the HR data in explicit format and enriching them with semantic information will make the data easily accessable and processable in other business applications. Table 3 gives an example how specific groups of data, called "infotypes" in SAP terminology, can be mapped to public or in-house defined domain classes.


#### SAP HCM - Personnel Administration

216 Security Enhanced Applications for Information Systems

general description of an organization (*imp:Organization rdfs:subClassOf foaf:* 

*imp:PersonalProfileDocument*, based on *foaf:PersonalProfileDocument* for expertise data

*imp:RnDProfile*, a disjoint concept of the *foaf:PersonalProfileDocument* for MPI core

 description of education (*doac:Education*) and skills (*skills: ComputerSkill, skills: LanguageSkill, skills: EngineeringSkills, skills: OrganizationalSkill, doac: SocialSkill*);

various kinds of experience (*imp:WorkingExperience, imp:ScientificExperience* 

 relations between a person and his/her profile documents and expertise (*foaf:primaryTopic, foaf:topic, imp:topic\_interest\_project, imp:topic\_interest\_reference,* 

relations between a organization and its profile document (*foaf:primaryTopic*, *foaf:topic*,

relations between a person and his/her expertise (*imp:degree, imp:graduationTitle,* 

relations between a person and the document base (*foaf:workInfoHomepage,* 

After the ontological knowledge base is designed, the next step is to populate the ontology i.e. import data into the ontology and create instances. Manually creating of ontologies is a time consuming task. Semantic Web community has delivered many high-quality opensource tools that can be used for automatic or semiautomatic ontology population i.e. to convert the facts trapped in the legacy systems or business documents into information

Professional HRM systems, e.g. the SAP Human Capital Management solution, cover the whole life-circle of an employee from her/his recruitment, training, development, and deployment to retirement. They enable tracking of employee movements and adequate tracking of changes in organizational structure. Furthermore, standard SAP HCM processes support skill management and give managers and HR professionals reporting and analysis options that provide a real-time insight into employee qualifications. As a result, the underlying (implicit) data base model is highly normalized and quite complex. Customizing the predefined SAP HCM functionalities or extending them with new client tailored functionalities require SAP consultancy efforts. Therefore, extracting the HR data in explicit format and enriching them with semantic information will make the data easily accessable and processable in other business applications. Table 3 gives an example how specific groups of data, called "infotypes" in SAP terminology, can be mapped to public or in-house

*imp:keyQualifications, imp:responsibilities OnProjects, imp:hasScientificRecord*);

*imp:useDBMS, imp:useModellingTool, imp:useProgrammingLanguage*);

*Organization*) and a community (*foaf:Group*);

competences integration on organizational level;

 general description of a document (*foaf:Document*); personal profile document (*imp:PersonalProfileDocument*);

R&D profile document (*imp:RnDProfile*);

*rdfs:subClassOf doac:Experience*)

*foaf:workplaceHomepage*); etc.

**4.2 From implicit to explicit data representation** 

understandable both for machines and people.

*foaf:homepage*);

defined domain classes.

integration on employee level;

Table 3. Establishing correspondence between implicit and explicit data representation

Figure 3 represent a screenshoot of mapping the facts from RDBMS tables to instances explicitly represented in the Institute "Mihajlo Pupin" knowledge store (Janev & Vraneš, 2011a).

Building Expert Profiles Models Applying Semantic Web Technologies 219

 Semantically Enhanced Full-text Search (see the "Search" panel in the upper left corner); A semantic search has significant advantages compared to conventional fulltext searches. By detecting classes and properties that contain the matched keywords, the semantic search delivers important feedback to the user how the search may be

Browsing using semantic relations (see the "Navigation:Classes" panel in the lower left

 Searching using faceted navigation method (see the "Filter" panel in the right most side). *OntoWiki* enables users to select objects according to certain facets i.e. all property values (facets) of a set of selected instances. If for a certain property the instances have only a limited set of values, those values are offered to restrict the instance selection further. Hence, this way of navigation through data will never lead

Once a selection is made, the main content section will arrange matching content in a list view linking to individual views for individual instances. The right sidebar offers tools and

Selection opportunities include (see Fig.4):

successfully refined;

to empty results;

Fig. 4. Expertise search with OntoWiki.

complementary information specific to the selected content.

corner);

Fig. 3. Defining mapping rules with TopBraid SPINMap SPARQL-based language.
