Ontology-Based Skills Management: Goals, Opportunities and Challenges.

Abstract

Establishing electronically accessible repositories of people's capabilities, experiences, and key knowledge areas is key in setting up Enterprise Knowledge Man- agement. A skills repository can be used for e.g. finding people, staffing, skills gap analysis, and professional development. The ontology based skills management system developed at Swiss Life uses RDF schema for storing ontologies. Its query interface is based on a combined RQL and HTML query engine.

Full text

Ontology-Based Skills Management:
Goals, Opportunities and Challenges
Jacqueline R. Reich
(Rentenanstalt/Swiss Life, Switzerland
jacqueline renee.reich@roche.com)
Peter Brockhausen
(Rentenanstalt/Swiss Life, Switzerland
peter.brockhausen@swisslife.ch)
Thorsten Lau
(Rentenanstalt/Swiss Life, Switzerland
thorsten.lau@gmx.net)
Ulrich Reimer
(Rentenanstalt/Swiss Life, Switzerland
ulrich.reimer@acm.org)
Abstract: Establishing electronically accessible repositories of people’s capabilities,
experiences, and key knowledge areas is key in setting up Enterprise Knowledge Man-
agement. A skills repository can be used for e.g. finding people, staffing, skills gap
analysis, and professional development. The ontology based skills management system
developed at Swiss Life uses RDF schema for storing ontologies. Its query interface is
based on a combined RQL and HTML query engine.
Key Words: skills management, ontologies, RDF
Category: H.3, H.4, K.6.1, K.4.3, I.2.4
1 Introduction
The tacit knowledge, personal competencies, and skills of its employees are the
most important resources of a company for solving knowledge-intensive tasks
such as decision-making, strategic planning, or creative design. They are the
real substance of the company’s success [Taubner and Br¨ossler 2000]. Therefore,
establishing an electronically accessible repository of people’s capabilities, expe-
riences, and key knowledge areas is one of the major building blocks in setting
up Enterprise Knowledge Management. Such a skills repository forms the basis
for a Skills Management System, which can be used to expose skill gaps and
competency levels, to enable the search for people with specific skills, and can
influence the requirements for training, education and learning opportunities as
part of team building and career planning processes [Ackerman et al. 1999].
Journal of Universal Computer Science, vol. 8, no. 5 (2002), 506-515
submitted: 4/2/02, accepted: 3/5/02, appeared: 28/5/02  J.UCS

By making employees’ experiences, knowledge and skills explicit, it is easier
to find out what people know or to direct people to others who can be of help.
This sharing of information improves the organisational productivity as well as
the individual performance. It supports staffing and enables the planning of pro-
fessional development [Auer 2000, Sure et al. 2000] – or, as Younker phrased it,
“Skills management is a robust and systematic approach to forecasting, identi-
fying, classifying, evaluating and analysing the work force skills, competencies
and gaps that enterprises face” [Younker 1998].
Implementing a Skills Management system is a threefold effort. One has to
address the technical, the content, and the cultural dimension. The technical di-
mension deals with providing the necessary functionality. The content dimension
encompasses the set up of organisational and automatic processes for keeping
the contents up-to-date. The concern of the cultural dimension is to ensure a
climate of trust and openness so that employees are motivated to make their
skills known – to their own and to the company’s benefit. Skills Management
may offer the means to affect a cultural change and instill real change into the
organisational mind-set and value-set [Deiters et al. 2000, Liao et al. 1999].
The outline of the paper is as follows. We start with a description of Swiss
Life’s architecture of a Skills Management application [Section 2]. Section 3
is devoted to the ontology development process. In section 4 we give a brief
description of the querying facilities before we conclude with an outlook on
future work [Section 5].
2 Skills Management at Swiss Life
At Swiss Life we developed a Skills Management system (SkiM) that in its
first version aims at finding people with a certain skills profile. This can either
be used for staffing new projects, or for identifying experts who might help to
solve a certain problem. Employees describe their skills themselves. They are
totally self-responsible in this. However, as the skills are publicly visible within
the company social pressure will work as a corrective, causing employees to be
honest in describing their skills.
Furthermore, participation in SkiM is completely voluntary. Instead of mak-
ing it obligatory we rely on the motivation of employees to become more visible
within the company and thus to increase their career opportunities. SkiM can be
seen as providing an internal job fair. An employee specifies his or her skills by
selecting concepts from a terminology [see Section 3.1] and by indicating a level
for each selected skill. Skills levels range over four steps from “elementary knowl-
edge” to “expert”. Although the skills are visible to every other employee, the
actual skills levels are not, guaranteeing some privacy. However, this is subject to
discussion, among others to better enforce the social pressure mentioned above.
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Figure 1: A personal home page in SkiM (top part)
Besides the skills more details can be given, like education, former affiliation,
special interests, projects participated in, etc. Finally, from all statements given
a personal home page is generated on the intranet, which can then be searched
[see Fig. 1].
Whenever an employee wants to register with a skill or education type that
is not part of the existing catalogue, she or he can easily extend the catalogue
by forwarding the suggested new term and its supposed place in the hierarchy to
the SkiM administrator who will care for its correct integration. The new term
will be visible to all users as soon as the integration will be completed.
2.1 Architecture of the SkiM System
The SkiM system comprises several components [see Fig. 2]. The Ontology Editor
OntoEdit allows an administrator to edit the ontologies for skills, education, and
508 Reich J.R., Brockhausen P., Lau T., Reimer U.: Ontology-Based Skills ...

Figure 2: Architecture of the SkiM System
job functions. OntoEdit enables inspecting, browsing, codifying and modifying
ontologies and therefore supports the ontology development and maintenance
tasks. The ontologies are modelled at a conceptual level and independently of
the formalism of the final representation language. OntoEdit offers views on
conceptual structures, such as concepts, concept hierarchy,relations,oraxioms
[Sure and Studer 2001]. For the early phases of ontology development the tool
MindManager was used to edit the ontologies because it better supports brain-
storming processes [see Section 3.2].
The Web Application part of SkiM allows employees to build their person-
alised intranet home pages by filling in the information categories given by tem-
plates.
Sesame from Aidministrator is an RDF/RDF Schema Storage and Retrieval
system [Broekstra et al. 2001]. Within SkiM Sesame stores the skills ontology as
RDF Schema and the instances of the ontology concepts, namely the association
of skills to employees, as RDF facts. It also stores any additional RDF annota-
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tions of the home pages which serve to characterise the content of the free text
fields. Sesame supports expressive querying of RDF schema and RDF facts by
means of a query engine for the RQL query language.
The query interface employs RDFferret [Davies et al. 2002] to do a combined
ontology-based and free text retrieval [see Section 4]. RDFferret combines full
text searching with querying RDF facts, in our case the skills data for each em-
ployee stored in Sesame as well as the additional annotations. Full text searching
is provided to offer high recall and coverage of unannotated information, while
precise ontological queries result in a high precision. Of course, a combination
of both query modes is possible.
3 SkiM as an Ontology-Based Approach
3.1 The Underlying Ontology
Within SkiM, three ontologies are defined - for skills, education, and job function.
At the moment, these ontologies are just taxonomies but will be extended to
include structured concepts in order to allow a more advanced functionality of
SkiM [see Section 5]. SkiM forces every skill, education or job description of
employees to be formulated by terms selected from the corresponding ontology.
We thus make sure that the terms used for describing skills, education or jobs
will match with query terms when SkiM users search for information. This will
guarantee a high recall and precision of the result sets. Moreover, the application
of ontologies is a prerequisite for comparing skills descriptions, for generating
a classification of the organisation’s knowledge, and for doing a so-called gap
analysis which identifies skills not sufficiently present in the organisation but
needed.
The skills ontology consists of three rather independent branches which corre-
spond to the three organisational units that were selected for the pilot phase, i.e.
IT, Private Insurance, HR [see Fig. 3, Informatik, Versicherung, HR-Personal].
The ontologies for education and job function are not divided into sub-domains
as the skills ontology. Currently, the skills ontology consists of 700 concepts,
the education ontology consists of 180 concepts, and the job function ontology
comprises 130 concepts.
The concept hierarchies are only that part of the underlying ontology which
a SkiM user sees. The complete ontology additionally includes concepts and
attributes to allow the connection between employees and their descriptions. An
OIL fragment that gives an impression of the whole ontology is shown in Fig. 4.
3.2 Ontology building
The development and maintenance of appropriate ontologies are the main chal-
lenges in building a Skills Management system. The manual ontology develop-
510 Reich J.R., Brockhausen P., Lau T., Reimer U.: Ontology-Based Skills ...

Figure 3: Top levels of the Swiss Life skills ontology
ment can be characterised as an iterative, incremental and evaluative process.
In the beginning, we provided the domain experts with a simple top level ontol-
ogy to give a better understanding of the domain to be covered by the ontology.
According to an initial baseline methodology inspired by [Sure and Studer 2002]
we advised the experts to use simple but helpful design rules, such as reducing
the degree of branching by setting a maximum of 5 to 10 branches, or limiting
the maximum depth of the ontology. Then, domain experts independently filled
their specific domain area within this top level ontology.
Using the design rules resulted in an overall reduction of the concepts which
was a welcome side effect. In total, this step resulted in an ontology with more
than 1000 concepts, including many duplicates. We then discussed and freezed
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class-def Skills
slot-constraint HasSkillsLevel cardinality 1
slot-def HasSkills
domain Employee
range Skills
slot-def WorksInProject
domain Employee
range Project
inverse ProjectMembers
slot-def ManagementLevel
domain Employee
range one-of "member" "head-of-group" "head-of-dept" "CEO"
class-def Publishing
subclass-of Skills
class-def DocumentProcessing
subclass-of Skills
class-def DesktopPublishing
subclass-of Publishing and DocumentProcessing
instance-of GeorgeMiller Employee
related HasSkills GeorgeMiller DesktopPublishing3
instance-of DesktopPublishing3 DesktopPublishing
related hasSkillsLevel DesktopPublishing3 3
Figure 4: A glimpse of the whole ontology
the ontology layer by layer, thereby identifying and eliminating some semantic
duplicates in the ontology. Moreover, parts of the ontologies were restructured
and apparently missing concepts were added.
For the development process we chose the brainstorming and mind mapping
tool MindManager from Mindjet. We created concept hierarchies, reorganised
them using simple drag and drop mechanisms, and applied the export function
to make the ontology public on the web for review purposes. In addition, we
annotated ontology elements with symbols or short notes about decisions that
were made. For instance, a question mark denotes an open topic to be discussed,
while a tick stands for an approved part of the ontology [see Fig. 5]. For group
discussions we made large printouts of the ontologies and put them on the wall.
The group of developers could view the current state of the ontologies with the
meta data describing the state of the discussion. Then the unclarified points
of the ontology were discussed and the ontology was rearranged and completed
step by step. This approach to ontology development proved to be very successful
concerning the outcome, the time required and the satisfaction of the ontology
developers.
The iterative approach as sketched above makes it very difficult to get a
clear versioning of the ontologies. Since most of the decisions are an outcome of
a discussion, part of the changes never physically exist as a version of their own.
512 Reich J.R., Brockhausen P., Lau T., Reimer U.: Ontology-Based Skills ...

Figure 5: Meta data for Ontology development (screen shot from MindManager)
It is also very hard to record the arguments that led to a decision without making
a detailed protocol of the discussion. As this is more or less impossible due to the
dynamic nature of the discussions, we only documented the result of a discussion
and the main arguments for the decisions but left out any intermediate parts of
the decision process.
While MindManager is an excellent tool to develop hierarchies in a cooper-
ative brainstorming process, it does not offer real editing functionality. It does
not check for duplicates in the ontology, relations can not be restricted in any
way (e.g. range, cardinality), nor does it distinguish between the identifier for
a concept and its representation. Such a distinction is a prerequisite for the
construction and maintenance of multi-lingual ontologies which are a basic re-
quirement in an international company, such as Swiss Life. All these features are
supported by the ontology editor OntoEdit [Sure and Studer 2001]. Therefore,
a combination of both tools might be close to a perfect ontology development
tool: MindManager for the early development phase while using OntoEdit for
extensions, maintenance, and versioning.
4 Querying Facilities
Searching for employees with certain skills can be done via their skills only, or
can be combined with search terms that aim at the other information categories
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of a personal home page, like education, special interests, projects worked in,
etc. Query terms for skills are enforced to be from the ontology.
To make sure that search terms are only evaluated in the proper information
category RDF annotations [Brickley and Guha 2000] are introduced in the home
pages so that for each term it is known to which category it belongs. We em-
ploy the search engine RDFferret from British Telecom [Krohn and Davies 2001]
which is capable of combining an ontology-based search (by interpreting RDF
facts) with a free text search. It also allows to confine search terms to certain
information categories of a home page by interpreting RDF statements in a web
page.
In order to achieve a match between search terms for skills with an employee’s
skills description, an up- or down-posting along the concept hierarchy is done.
The results are ranked according to the skills levels specified and the overall
degree of matching between a home page and the query.
5 Evaluation and Outlook
We are currently evaluating the existing version of SkiM in a pilot phase with 150
users. We found them to be very open to such a system and very willing to publish
their skills, provided their skills descriptions are publicly visible in the company.
Most users said that they would not participate if their skills would only be
seen by a few managers and a small group of people in the HR department.
This confirms our hypothesis that employees will voluntarily participate in such
a system if their personal benefit is a higher visibility in the company.
Many users complained that browsing the skills ontology is too cumbersome.
Thus, we will have to look into how to make the ontology better searchable.
We are currently discussing to introduce skills management on the corporate
level, i.e. with a visibility across all subsidiaries and branches of Swiss Life. In
that case we would need a multi-lingual skills ontology because otherwise many
people would feel uncomfortable in using a system with English terms only.
An approach complementary to ours is to identify people with certain skills
by doing text mining on the documents in the intranet [Becerra-Fernandez 2000,
McDonald and Ackerman 1998]. Adding such text analysis functionality to our
system would be ideal for generating suggestions for each employee to extend
his or her skills descriptions, and thus to make sure that skills descriptions once
delivered stay up-to-date.
Acknowledgements
This work has been partially supported by the European Commission research
project On–To–Knowledge (IST-1999-10132), and by the Swiss Federal Office
514 Reich J.R., Brockhausen P., Lau T., Reimer U.: Ontology-Based Skills ...

for Education and Science (project number BBW 99.0174). The SkiM system
was initiated and brought into existence by our former colleague Bernd Novotny.
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