Potential of Semantic Web Technologies to Support Knowledge
Transfer in Forest Management
Alfred Radl and Harald Vacik
Institute of Silviculture, University of Natural Resources and Life Sciences, Peter Jordan-Straße 82, Vienna, Austria
Keywords: Biodiversity, Common Vocabulary, Ecosystem Services, Knowledge Transfer, Web Portal, Thesaurus.
Abstract: We introduce a Knowledge Transfer Portal (KTP) which supports knowledge transfer among researchers
and forest managers. The KTP will be used for supporting transfer of knowledge generated in the
FunDivEUROPE (FUNctional significance of forest bioDIVersity in EUROPE) after project life. It uses
semantic web technologies to achieve a common understanding throughout a knowledge representation
based on an expert elicitation process. Knowledge transfer tools (KTTs) take use of knowledge elements
within the knowledge base and implement various knowledge transfer functionalities. The knowledge base
shows interactions of biodiversity effects on the sustainable provision of ecosystem services. In this
contribution we focus on the ongoing knowledge base engineering process and show first results that were
based on a series of workshops with domain experts to generate a common understanding about terms,
definitions and their relations. Relations were generated upon FunDivEUROPE project hypothesis with
respect to project results and expert beliefs. We use a web-based, collaborative knowledge base engineering
cycle and create a thesaurus which was initiated with terms from these expert workshops.
1 INTRODUCTION
Semantic web technologies constitute an important
step in sharing, integrating and re-using information
whereas knowledge management becomes one of
the key drivers in semantic web research. Although,
few examples, like Rosset (2013), show the
potentials of semantic technologies in forest
management, semantic web techniques have been
hardly adopted in the forestry domain in the past.
Even though functional trait approaches, for
example the TRY-database (Kattge et al., 2011) has
a large potential to better the understanding of
ecosystem changes, they often fail in transferring
long term observed knowledge to a broader
(nonscientific) community.
At the moment there are large efforts to structure
ecology data in common vocabularies. ThesauForm
(Laporte and Garnier, 2012; Laporte et al., 2013) or
the LTER Controlled Vocabulary (Porter, 2009) are
some examples to show how data records can be
organized in a standardized way.
Knowledge transfer in FunDivEUROPE
(Functional significance of forest biodiversity) aims
to support an understanding about the role of
biodiversity in securing ecosystem services in forest
ecosystems. The identified, produced and evaluated
project knowledge will be transferred to politicians,
forest managers and other interested user groups.
Besides the common shared vocabulary, the major
challenge in knowledge transfer between scientists
and non-science are the various perspectives on the
problem domain. Questions like “Does species
mixture matter in improving timber production or
enhancing water quality?” need to be answered to
serve stakeholders information demands. Therefore
the need in knowledge transfer arises to link the
research findings of the science community with the
practical forest management problems of the
stakeholders.
2 LITERATURE SURVEY
Basically, knowledge transfer is a form of
communication between two individuals where each
takes on the role of a sender or a recipient. The
questioner communicates his knowledge needs to a
sender who acts as knowledge resource and answers
the questions of the recipient (Lind and Persborn,
2000). Knowledge Transfer is often labeled time
387
Radl A. and Vacik H..
Potential of Semantic Web Technologies to Support Knowledge Transfer in Forest Management.
DOI: 10.5220/0004626403870392
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2013), pages 387-392
ISBN: 978-989-8565-81-5
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
consuming with a large need of expert involvement
associated with huge costs and many difficulties that
hinder a successful competition of knowledge
transfer. For example, different background
expertise may lead to misunderstanding between the
transfer partners in understanding of questioning or
answering. These and other difficulties should be
seen as characteristics of every knowledge transfer
(Szulanski, 2000). Additional to the roles of partners
involved in the knowledge transfer also the
knowledge itself varies in its type. Nonaka and
Takeuchi (1995) differentiate between tacit and
explicit knowledge and describe the way how one
type of knowledge is transformed into another by
various means. Tacit knowledge is hard to be
communicated because it states the implicit
knowledge bounded to individuals. It has to be made
“explicit” in order to make “transferable”. On the
other hand explicit knowledge can be classified as
structured or unstructured and characterized as
procedural (“knowing how”) or declarative
knowledge (“knowing that”) that needs to be
processed in a different way. In scientific literature
transfer of knowledge is often related to transfer
between organizations or within an
organization/community among its members.
In this contribution we focus on knowledge
transfer between forestry experts and various
FunDivEUROPE stakeholders. Expert’s implicit
knowledge needs to be elicited to generate a
common understanding of the project domain.
3 KNOWLEDGE TRANSFER IN
FunDivEUROPE
Key issue of the Knowledge Transfer Platform
(KTP) is to facilitate knowledge transfer between
researchers and interested end users within the
FunDivEUROPE project. It is an easy accessible,
modular, web-based platform, which supports
knowledge transfer with a set of Knowledge
Transfer Tools (KTTs). KTTs add functionalities to
allow searching, communicating and exploring
knowledge elements. In the context of
FunDivEUROPE the KTP should give a frame for
researchers and other stakeholders (e.g. forest
managers) to interact and exchange various
knowledge elements upon a common understanding.
Knowledge elements describe semantically enriched
content objects enhanced by metadata tags used in
the common understanding.
Figure 1 shows the interaction of users with a
shared understanding by using, creating or tagging
knowledge elements.
Figure 1: Knowledge Transfer Portal interaction.
3.1 Extracts from the KTP
Architecture
The architecture of the Knowledge Transfer Portal
(KTP), shown in in Figure 2, comprises four main
components: Web Content Management System,
Toolbox with Knowledge Transfer Tools, Content
Crawler and a Semantic Engine. To foster the
common understanding of experts and practitioners
the KTP uses semantic web technologies to support
access of and communication with different
knowledge transfer tools. The knowledge base
describes a common understanding of the
knowledge domain with terms and relationships
between knowledge elements.
Figure 2: Conceptual Architecture of the KTP.
Each KTT, which aids knowledge elicitation and
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holds functionalities for decision support, relies on
the same knowledge base. In the first prototype we
integrate KTTs to search and create project relevant
knowledge elements. Additional, a set of advanced
tools should be available to explore project findings
interactively. For instance a tool to create Frequently
Asked Questions (FAQs) on forest biodiversity
related issues is implemented in FunDivEUROPE.
Another advanced tool, which uses interactive maps,
allows comparing the influence of different tree
species mixtures on ecosystem services in different
European regions.
Figure 2 shows the conceptual architecture of the
FunDivEUROPE Knowledge Transfer Platform. The
links between the components indicate the
interaction of architecture components. With
reference to Figure 1 the KTP is placed in between
the two communication partners and aid knowledge
transfer.
The KTP is designed as web content
management system (WCMS) enhanced by a
semantic engine for knowledge transfer purposes.
The knowledge base holds the content repository of
the WCMS and integrates metadata from the
enhancement engine. The KTP uses Drupal
(Corlosquet et al., 2009) as WCMS and Apache
Stanbol (Damjanovic et al., 2011) to support
metadata enhancement. The KTTs use the WCMS as
presentation layer and rely on the repositories of the
semantic engine and the WCMS. Additional a
crawler component is responsible for automated
retrival of external resources to extend the
knowledge base.
4 KNOWLEDGE BASE
ENGINEERING PROCESS
We use a development process based on expert
elicitation originating from a well-accepted
methodology in scientific literature. Instead of
developing an ontology right from the edge we use a
thesaurus form to draft the first prototype of the
FunDivEUROPE knowledge base. The thesaurus is
described with SKOS (Simple Knowledge
Organization System) to use its specifications within
the Semantic Web framework. SKOS uses RDF
(Resource Description Framework) to allow sharing
the knowledge representation on the web and a
common understanding of various data sources.
4.1 Methodology of the Development
Process
We rely on the METHONTOLOGY approach
(Fernández-López et al., 1997) to guide the
development of the FunDivEUROPE knowledge
base used in the KTP.
Figure 3: METHONTOLOGY Life cycle.
PotentialofSemanticWebTechnologiestoSupportKnowledgeTransferinForestManagement
389
Figure 3, based on Corcho, et al. (2003) shows the
management, development and support activities of
METHONTOLOGY. We added shortcuts for each
activity to reference our activities of the text below
to activities in the methodology.
Development Activities of METHONTOLOGY
are used by the means of a spiral model to allow
prototyping in the development phase of the KTP.
We also utilize the recommendations of the
guidelines for the construction, format, and
management of monolingual controlled vocabularies
(ANSI/NISO Z39.19, 2005).
4.2 FunDivEUROPE Thesaurus
Prototype Specification and
Conceptualization
After we defined the scope of the FunDivEUROPE
thesaurus (D1), we started with a glossary of terms
(D2) and a basic network of relations between them.
These terms and relations were generated from a
series of workshops.
The first workshop was held with a small group
of experts and served as a first draft of the glossary
of terms relating the FunDivEUROPE project
domain. The major challenge was to find a set of
terms that not only represents the terms used to
formulate research findings but also links issues of
interested stakeholders.
Conceptualizations, definitions and relations of
the well-known, accepted Millennium Ecosystem
Assessment framework (Millennium Ecosystem
Assessment, 2005) were applied and extended by
findings of Chapin, et al. (2000), Haines-Young and
Potschin (2010), Martín-López, et al. (2009), The
Economic of Ecosystem and Biodiversity (TEEB,
2010) and Hooper, et al. (2005).
Figure 4: Top-Level interactions.
Figure 4 shows the first Top Level design of the
FunDivEUROPE thesaurus, which was a result of
intensive literature review on forest biodiversity
research (S1, S2). The Top-Level design links
ecosystem processes with factors of human well-
being and builds a bridge between concepts of
interest for different groups of stakeholders.
We assigned terms of the glossary to elements in
the framework and used it to confront project
experts. Terms and framework were revised in a
continuative workshop (S3) as part of the annual
project meeting with a larger group of researchers.
In this setting experts were asked to formulate their
project hypothesis regarding the terms in the
glossary. The question included elicitation of
relationships between terms for each cause – effect
chain belonging to their hypothesis. A project
hypothesis “Different tree species mixtures
improving timber production” leads to a chain of
relevant and related terms e.g. species mixture (a
measure of forest biodiversity) influence
competition (belonging to species interactions) and
cause changes in plant growth rate.
The workshops ended with a revised glossary of
terms including relations between these terms and a
set of documentation done for each activity (S5).
4.3 FunDivEUROPE Thesaurus
Prototype Formalization and
Implementation
We use Tematres (Gonzales-Aguilar et al., 2012), a
web based collaborative approach in thesaurus
development, to implement a first version of the
FunDivEUROPE thesaurus (D4). Tematres supports
a thesaurus definition (D3) to formalize the terms of
the previous workshops.
Figure 5 shows a complete structure of the first
conceptualization of terms. Afterwards researchers
and experts of the project were informed to assist in
further thesaurus prototyping.
Figure 5: Hierarchy of the FunDivEurope Thesaurus.
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5 INTEGRATION OF THE
THESAURUS INTO THE
KNOWLEDGE
TRANSFERPLATFORM
In this section we focus on integration of the shared
vocabulary with respect to components of the
conceptual architecture that shares an interface with
the knowledge base.
Figure 2 introduces the interface of the
knowledge base to the semantic engine and the
WCMS.
We use Apache Stanbol to allow semantic
enhanced content management. Stanbol feature a
RESTful webservice for easy integration into
various content management systems. Furthermore,
the VIE (Vienna IKS Editables) JavaScript library
was used to provide a set of semantic user interface
widgets (Grünwald and Bergius, 2012). These
widgets are integrated into the KTPs, Drupal based,
WCMS. We use a SKOS definition (Miles and
Bechhofer, 2009) of the FunDivEUROPE thesaurus
to translate thesaurus definition into RDF (Van
Assem et al., 2006). This format allows extending
the existing definitions within the Apache stanbol
server, which uses dbpedia for metadata
enhancement.
6 CONCLUSIONS AND FUTURE
WORK
We demonstrate how the Knowledge Transfer Tool
is used to facilitate knowledge transfer between
researchers and other stakeholders. Transfer tools
share the same common understanding. A prototype
of a thesaurus was developed which represents the
domain of the FunDivEUROPE project. We use a
prototyping approach and developed a first
initializing version of the FunDivEUROPE
thesaurus. The thesaurus was integrated within a
web content management system via a semantic
engine.
Knowledge engineering as we did allows experts
to generate a knowledge base which enables to
formulate cause-effect relationships on forestry
objects (e.g. species traits) and entities known by the
stakeholder (e.g. ecosystem goods and services).
This effort enables forest managers or politicians to
raise general system understanding and increases
their sense for influences effecting biodiversity in
forests of Europe.
Further work contains constant improvement and
extension of the FunDivEUROPE thesaurus and
improvements and customizations of user interface
integration into the WCMS.
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