SIMILARITY MEASURES FOR SKILL-PROFILE MATCHING IN

ENTERPRISE KNOWLEDGE MANAGEMENT

Ernst Biesalski

DaimlerChrysler AG, Plant Wörth, Daimlerstrasse 1, D-76742 Wörth, Germany

Andreas Abecker

FZI Research Center for Information Technologies at the University of Karlsruhe, 76131 Karlsruhe, Germany

Keywords: Knowledge Management, Ontology, Skill Management.

Abstract: At DaimlerChrysler’s truck plant in Wörth, we are currently implementing a comprehensive IT solution for

integrated and synergistic processes in personnel development. In this paper, we sketch some ontology-

based software modules – as well as their interdependencies and synergies – which support streamlined and

integrated, comprehensive personnel-development processes. A central element in the software architecture

is ontology-based similarity assessment for skill-profile matching which is exemplarily discussed for

software-supported project staffing.

1 MOTIVATION

In the coming 10 years, Human Resource

Management (HRM) departments in large

companies in Germany and most parts of Europe

will face radically new challenges and tasks.

Already today, demographic studies and prognoses

show clearly, that in the long-term, the number of

young people will significantly decrease (Federal

Statistical Office Germany, 2003). Even with a

realistically estimated amount of immigration, the

share of citizen under 20 years will reduce from 20%

(2001) to 16% (2050) of the overall population

while the share of people above 60 years increases

from about 25% to about 33%. Consequently, the

working population will run through a continuous

aging process (in the average), and from ca. 2015

on, the number of persons available for employment

will more and more run short. Under such

conditions, a coordinated, long-term personnel

development strategy gains increasing importance.

Such a strategic personnel development must be part

of a comprehensive HRM strategy which should in

turn be embedded into an overall, holistic

Knowledge Management (KM) approach (Biesalski;

Abecker, 2005).

From the IT point of view, HRM departments

mainly use IT applications for the management of

personnel data (standing data, performance reviews),

for junior employee development, or for training

planning, seldom also for assessment of training

needs. Real-world system landscapes are often

characterized by manifold heterogeneous systems,

evolved over time, showing pretty non-uniform

features – which hinders interoperability of those

applications. Further problems come from massively

redundant data storage, as well as complex

interfaces between systems. Since such system

landscapes are typically a combination of standard

software and proprietary developments of the HR

software department, they seldom support an

integrated personnel development approach, i.e., a

coordinated behaviour of different applications.

Modern views on knowledge and skills of

employees are normally not realized. The purpose of

such systems is to manage the single employee, not

to model and manage an integrated view on

employee, tasks and organizational context.

At DaimlerChrysler’s truck plant in Wörth, we

are currently developing such an integrated system

and process landscape. In Section 2, we sketch the

respective software architecture. In Section 3, we

focus on one module of the system, designed for

supporting project staffing. At the hand of this

example, we discuss in more detail the ontology-

based matching of skill profiles – which is a central

functionality also for the other modules. Finally, in

Biesalski E. and Abecker A. (2006).

SIMILARITY MEASURES FOR SKILL-PROFILE MATCHING IN ENTERPRISE KNOWLEDGE MANAGEMENT.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - AIDSS, pages 11-16

DOI: 10.5220/0002462700110016

 SciTePress

Section 4, we briefly summarize, sketch some

related work and report on the current

implementation status of the system.

2 AN APPLICATION

FRAME-WORK FOR

PERSONNEL DEVELOPMENT

We propose an integrated software and process

framework for personnel development (PD

Framework) as depicted in Figure 1. It contains

business processes for personnel development

(Biesalski; Abecker, 2005a), a Human Resource

Data Warehouse (HR DWh) which integrates data

from different legacy systems, an ontology-based

employee-skill database, as well as different new

application modules.

In this article, we focus on the application

module for project staffing. A core idea of this and

all the other modules is the ontology-based

modelling of employees’ skill profiles. This is based

upon an ontology which formalizes the former skill

catalogue that describes all different personal skills

occurring or required in the company (cp. Figure 2).

Each software module employs an ontology-based

matching procedure which is able to compare skill

profiles (i.e. bundles of skills which characterize an

employees’ knowledge, skills, and qualifications, or,

the competences required for a specific job,

respectively). For the “Succession Planning”

module, this means to compare the to-be skill profile

of an open position with the as-is profiles of a

number of employees – in order to find out the most

suited candidate. To this end, we compare bundles

of ontology instances, as indicated in Figure 3.

In the same manner, in the module “Detection of

training needs / Training planning”, we compare

competence profile of jobs with the as-is profile of

employee in order to identify qualification gaps.

Such a comparison can also regard strategic

directions, e.g. of the department, thus leading to a

training planning and behaviour which is closer to

the company’s overall strategic needs. When having

identified strategic training gaps, they can be

compared with training offers, and suggestions for

filling gaps can be made, also regarding time

restrictions of employees, budget restrictions, etc.

Such suggestions can then be offered by the

department manager to the employees. In the

module “Project and job staffing”, the matching

procedure searches best available employees for

vacancies in project teams, based upon matching

between to-be (project / job requirements) and as-is

(personal profile) comparisons.

3 THE MODULE: PROJECT

STAFFING

In industrial practice, open jobs are seldom staffed

along a structured procedure. Personal networks and

preferences often play the major role. Of course, this

guarantees neither a fast nor an optimized result, in

particular when staffing a large project team with a

number of heterogeneous required skills and

competencies. Since large enterprise usually have

electronic data about the competences and

experience of their employees, a (partial) automation

seems feasible and desirable.

In order to support project staffing, we need on

one hand position skill requirements and on the

other hand employees’ skill profiles. We want to

find – with a minimum staffing effort – the best

Figure 1: Overall Perspective - Personnel Development Software Framework.

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

suited employees who fulfil the position skill

requirements as well as other constraints, such as

availability. For integrating project staffing into a

comprehensive HRM approach, we particularly need

the standardized skill catalogue which allows for a

unified semantic description of position skill

requirements and employee skill profiles, and a

skill-matching procedure. For addressing these

issues, we adopted the approach of (Hefke;

Stojanović, 2004), introduced a comprehensive

ontology-based skill catalogue at DaimlerChrysler

plant Wörth / Rhine, as well as ontology-based

similarity measures for profile matching.

Table 1: Some Central Concepts of the Skill Ontology.

Concept Description

Skill The ability to produce solutions in some

problem domain. In Enterprise Skill

Management, the identified, named, trainable

competence of some employee, often

required to perform a specific organizational

task (well), to fill a position, or to enact an

organizational role.

Weight Achieved or required level of expertise

which indicates to which extent the given

competence is or shall be mastered.

Skill

instance

A skill together with a level of expertise

(e.g., “expert in C++ programming”,

“beginner in English”). As a unit of reference

only needed for internal technical reasons.

Skill

Profile

A list of skill instances. As an employee skill

profile describing as-is situation regarding an

individual’s personal qualifications, as

position skill requirements describing

necessary qualifications to do a job

successfully.

3.1 Ontology Based Skill Modelling

In general, a skill catalogue contains skills relevant

for the company. In a structured skill catalogue, the

skills can be equipped with a weight, describing the

achieved level of expertise. In the DaimlerChrysler

Wörth case, about 700 single skills have been

modeled. For an employee, a personal skill profile

lists his or her actual skills, together with weights for

the achieved level of expertise. A profile for position

skill requirements is a list of weighted skills which

are important for successfully doing the required

tasks in a given position (here the weight expresses

the relevance of having achieved the specified level

of expertise). Both profiles refer to the same, unified

vocabulary specified in the skill catalogue (cp.

Figure 2 and Table 1).

3.2 Project Staffing with Ontology

Based Similarity Measures

As an efficient and expressive data structure for

processing skill profiles, we use an ontology-based

approach (Staab; Studer, 2004) which stores,

manages, and compares profiles with the help of the

KAON (Mädche; Motik; Stojanović, 2003) ontology

management framework. For supporting the

selection of qualified employees, our „project

staffing“ module is a web-based tool which allows

to define project-specific position skill requirements

and – based upon those – gives dedicated project

staffing advice. To this end, decision-supporting

information is taken into account from sources such

as employee-skill profiles, job catalog, time

recording system, etc. Since a multitude of

perspectives must be fed into the complex employee

selection process, the matching procedure which

compares job profiles and potential candidates’

profiles should be capable of semantically assessing

the similarity of ontology instances. For realizing

such a candidate selection procedure, we employed

the similarity framework introduced in (Ehrig;

Haase; Stojanović; Hefke, 2004). However, while

(Ehrig; Haase; Stojanović; Hefke, 2004) focus on

text-dominated application areas (comparison of

vocabularies and terminologies), the comparison of

skill profiles requires more advanced combination

and expression means for similarity measures. So,

we extended the framework such that the user can be

provided with different metrics for assessing a

candidates’ suitability for a given job profile. Our

requirements analysis and analysis of existing HRM

systems showed that different similarity metrics for

Figure 2: Top-Level Structure of Skill Ontology

(simplified).

SIMILARITY MEASURES FOR SKILL-PROFILE MATCHING IN ENTERPRISE KNOWLEDGE MANAGEMENT

profile comparison should be used to express

different aspects relevant for different tasks. For

project staffing, an aggregated metric is used which

combines the following four aspects:

• Direct skill comparison: we require an

exact match of as-is and to-be. So we can

specify K.O. criteria for the central

requirements, especially in strategically

important jobs.

• Proportional similarity: we identify also

partially fulfilled requirements. This is also

important if we can plan for additional

teaching and qualification measures or for

“training on the project”.

• Compensatory similarity: we identify not

only partially fulfilled requirements, but also

overqualifications; so, additional expertise

on one hand may compensate deficiencies on

the other hand. If several employees fulfil the

K.O. criteria, this can be useful to find the

most suited one.

• Taxonomic similarity: the taxonomic

structure of the skill ontology is taken into

account to find “close matches” in the case

that no employee has exactly the required

qualifications. Also usable for deciding

between several candidates, and for refining

profile specifications.

Figure 3: Example: Part of Ontology-based Employee-

Competence Database.

Let us now discuss these similarity measures in

some more detail.

3.3 Similarity Measures for Skill

Profiles

The basis of our skill-profile matching is the

mapping of all competence metrics to a four-level

scale (beginner, advanced, expert, teacher).

Comparison of skill profiles is reduced to the

comparison of skill instances. Let:

• R be a profile for some position-skill

requirements consisting of a non-empty list

of skill instances r with skill name rs and

expertise level rl, and

• E be an employee-skill profile consisting of a

list of skill instances e with skill name es and

expertise level el

()

⎩

⎨

⎧

else ,0

esrs if , 1

name skill

ersim

()

⎩

⎨

⎧

else0,

elrl if 1,

level skill

ersim

3.3.1 Direct Skill Comparison

Often we want to specify special skill instances as

K.O. criteria. This requires an extension of our skill

modelling. Then we can define the direct skill

comparison metrics for a position skill requirements

profile R and an employee skill profile E as follows:

()

∑

∈

∈∈

∗

EeRr

ameskilskilllevel

rweight

ersimersimrweight

ERsim

)(

),(*),()(

comparisondirect

3.3.2 Proportional Similarity

The idea of direct comparison leads to the effect that

each underfulfillment of a skill requirement finally

results in a complete disqualification of the

respective employee. It does not allow to assess the

possibly differing extent to which the requirements

where not fulfilled (only marginal deficiencies

versus complete misqualification). In reality, project

leaders need a metrics which is suited to assess

partial fulfillment of requirements in an appropriate

manner. To this end, we define the proportional

similarity:

()

∑

∈

∈∈

−

∗

EeRr

levelalproportion

rweight

ersimersimrweight

ERsim

)(

),(*),()(

name skill

similarity-alproportion

with:

()

else ,

elrl-1

el rl if 1

level-alproportion

⎪

⎩

⎪

⎨

⎧

∗−

≤

=ersim

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

3.3.3 Compensatory Similarity

Proportional similarity is an extension of the

compensatory similarity which addresses not only

under-, but also overqualifications. These can be

especially valuable when several candidates have

fully satisfied the requirements of the other

similarity measures and cannot yet further be

distinguished.

()

∑

∈

∈∈

−

∗

EeRr

levelrycompensato

sweight

ersimersimrweight

ERsim

)(

),(*),()(

name skill

similarity-rycompensato

with:

() ( )

el rl-1:,

level-rycompensato

∗−=ersim

This metrics must be interpreted differently from

the other two presented before. In contrast to the

situation with an exact match or a proportional

similarity, a “1” is here not anymore an indicator

that all requirements are fulfilled completely.

Instead, overqualifications in some skill-profile

facets may compensate for underqualifications in

other facets.

3.3.4 Taxonomic Similarity

It is often difficult to find employees which fit

relatively exact into a given profile specification.

Figure 4: Example Taxonomic Similarity.

This comes also from the fact that it is not always

trivial to specify the expected requirements

unequivocally if there are different possible opinions

how to characterize the required profile in terms of a

complex skill catalogue which might provide many,

slightly different, but related skills in a certain

competence area. For instance, knowledge about

„Spreadsheet software“ might also be proven by a

certificate about using „Microsoft Excel“.

Depending on the perspective, profile models may

differ, both when employees describe themselves,

and when project leaders define a required skill

profile.

Taxonomic similarity can be derived from

semantic cotopy of two ontology instances. Figure 4

gives an example as a small excerpt from a

hypothetical skill catalogue: the skills „Object

oriented programming“ and „Procedural

programming“ are closely related since the have the

same parent concept. While “Java programming”

and “C++ programming” are very similar, “Pascal

programming” is still related, but far more loosely.

Such sophisticated comparisons of profiles based on

the taxonomic skill catalogue as background

knowledge, allows far-reaching detailed assessments

of whole project team staffs, if required. In

particular, they allow for fine-granular ranking of

candidates. Due to space limitations, we don’t go

into details about the computation of taxonomic

similarity, here. For more information, see, e.g.

(Ehrig; Haase; Stojanović; Hefke, 2004).

4 SUMMARY AND CONCLUSION

The idea of detailed ontology-based modelling of

personal skills is not new (Stader, Macintosh, 1999;

Liao; Hinkelmann; Abecker; Sintek, 1999), but has

found just recently more practical interest. Our own

work within DaimlerChrysler, but also for instance

(Hefke; Stojanović, 2004; Lau; Sure, 2002;

Dittmann, 2003; Hiermann; Höfferer, 2005) show

that such an approach – if appropriately supported

by organizational processes – can lead to more

efficient and more effective project staffing in real-

world, large-scale industrial application scenarios.

As sketched in Section 2, even more impact can be

achieved by designing a comprehensive ontology-

based skill-management infrastructure, joining up

existing systems, adding new functionalities, and

designing suitable support processes. The focus of

this paper was the use of ontology-based similarity

measures for skill-profile matching. Many academic

approaches for using advanced reasoning for skill

matching (like: Colucci; Di Noia; Di Sciascio;

Donini; Mongiello; Mottola, 2003) apparently have

not yet been applied in large-scale real-world

scenarios. Similarity-based approaches seem not yet

very widespread in this area, but they have proven

their practical usefulness in a vast amount of Case-

Based Reasoning (CBR) applications (see, e.g.,

Watson, 1997). CBR tools have also been

SIMILARITY MEASURES FOR SKILL-PROFILE MATCHING IN ENTERPRISE KNOWLEDGE MANAGEMENT

successfully applied in Expert Finder systems which

can be seen as a very specific partial instance of a

skill management system (Vivacqua; Lieberman,

2000). The specific contribution of the work

presented in this paper is to settle the CBR ideas

upon a state-of-the art ontology infrastructure – thus

combining the advantages of similarity based search

(good retrieval results in vaguely specified query

situations and complex domains) with those of

ontology-based systems (clear semantics, good

application potential for integration of different

legacy systems).

When writing this paper, the software framework

is fully specified, the HR Data Warehouse and the

employee-skill database are already implemented,

and the three application modules are under

implementation.

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ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS