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Citation: Alfred, R.; Harald, V.
Supporting Knowledge Transfer on
Functional Significance of Forest
Biodiversity. Information 2025,16, 37.
https://doi.org/10.3390/
info16010037
Copyright: © 2025 by the authors.
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Article
Supporting Knowledge Transfer on Functional Significance of
Forest Biodiversity
Radl Alfred and Vacik Harald *
Institute of Silviculture, Department of Forest and Soil Sciences, BOKU University, Peter Jordanstr. 82,
1190 Vienna, Austria
*Correspondence: harald.vacik@boku.ac.at
Abstract: The FunDivEurope (Functional Significance of Forest Biodiversity in Europe)
project aimed to quantify the role of forest biodiversity for ecosystem functioning and
the delivery of goods and services in major European forest types. Members of the re-
search community aimed to communicate the research findings related to the functional
significance of forest biodiversity to the wider public. Therefore, a web-based Knowledge
Transfer Platform (KTP) was designed to ensure project-generated knowledge is transferred
to targeted stakeholders and user groups. The paper shows a user experience-based ap-
proach in the development of the knowledge transfer platform, and provides insights into
the system architecture to show how semantic web-based technologies are able to target a
broader audience while keeping entry barriers as low as possible to support communities
of practice to grow.
Keywords: knowledge transfer; content management system; semantic web; forest
biodiversity; ecosystem services; stakeholder interaction
1. Introduction
1.1. Biodiversity Research and Forest Management
Forest ecosystems are characterized by a complex of various elementary factors: plant,
animal, and micro-organism communities, abiotic factors (climate, abiotic soil substance),
and humanity as an integral component of ecosystems. Biodiversity is defined as “the
variability among living organisms from all sources including, inter alia, terrestrial, marine,
and other aquatic ecosystems and the ecological complexes of which they are part; this
includes diversity within species, between species, and of ecosystems [
1
] CBD, 1992”.
Ideally, to assess the conditions and trends of biodiversity, it is necessary to measure
the abundance of all organisms over space and time. Taxonomy attributes such as the
number of species, functional traits like the ecological type (e.g., nitrogen-fixing plants)
and the interactions among species that affect their dynamics and function (e.g., predation,
parasitism, competition, pollination) affect ecosystems in different strengths. Biodiversity
represents the foundation of ecosystems that, through the services they provide, affect
human well-being. Biodiversity contributes directly (through provisioning, regulating,
and cultural ecosystem services) and indirectly (through supporting ecosystem services)
to many constituents of human well-being, including security, the basic material for a
good life and health [
2
,
3
]. The question arises, how much does biodiversity matter? How
is the provision of water and timber, or the regulation of climate, floods, and diseases,
or cultural services such as recreation and supporting services such as soil formation,
photosynthesis, and nutrient cycling influenced by the abundance of species (e.g., trees,
plants, fungi, insects)?
Information 2025,16, 37 https://doi.org/10.3390/info16010037
Information 2025,16, 37 2 of 19
Forest management affects various levels of biodiversity from the genes to the land-
scape [
4
]. The combination of direct and indirect actions of man on forests can contribute
to a decrease in intraspecific variability, species diversity, and ecosystem variety. Bearing
in mind that each operation (e.g., harvesting, road construction, the use of herbicides and
pesticides, doing meliorations and afforestation) and the impacts of climate change has
both positive and negative consequences for various living organisms and communities,
understanding the functional significance of forest biodiversity plays a key role for sustain-
able forest management. For some groups of organisms, the effects of land use are already
well studied, but for others, they are still insufficiently researched [
5
,
6
]. In particular, the
interaction of the diversities of individual groups with each other and the response of the
respective genetic diversities to land use changes is incomplete [
7
]. In this context, the
functional significance of biodiversity of natural systems is unclear, as various management
strategies overlap the effects. Within the FunDivEUROPE (Functional Significance of Forest
Biodiversity in Europe) project the role of forest biodiversity for ecosystem functioning
and the delivery of goods and services in major European forest types was explored [
8
,
9
].
The study addressed the role of tree species diversity and species identification in the
provision of ecosystem services. In this context, tree diversity experiments were set up and
more than 250 comparative plots in six European regions were observed for studying the
effects on functional diversity. In several follow-up projects, SoilForEUROPE, Dr.FOREST,
or BEsound the findings of the FunDivEUROPE project could be taken up and further
explored to establish a knowledge base about the importance of forest biodiversity for the
goods and services provided by European forests [
9
]. The projects revealed that diverse
forests tend to be more resilient to disturbances, sequester more carbon, and provide an
enhanced diversity of forest-associated taxa. The findings offer insights into forest manage-
ment and conservation practices, advocating for the preservation and restoration of diverse
forest ecosystems.
1.2. Challenges of Knowledge Transfer in Natural Resource and Forest Management
Forest practitioners are generally interested to know, how species richness is influenc-
ing the provision of ecosystem services and to what extent forest management can consider
the scientific findings [
10
]. A consolidated knowledge transfer process is therefore essential
in communicating the findings of researchers to concerned stakeholders. Davenport et al.
describe knowledge as a “fluid mix of framed experience, values, contextual information,
and expert insight that provides a framework for evaluating and incorporating new expe-
riences and information. It originates and is applied in the minds of knowers” [
11
]. The
knowledge transfer process is often characterized as an expensive and time-consuming
process which needs a large involvement of experts to overcome difficulties and barri-
ers. In the last decades, many different applications, which implemented expert system
methodologies, were used to represent knowledge in various manners [
12
]. Rauscher
demonstrated the functional difference between electronic hypertext and print-based meth-
ods by publishing “The Encyclopedia of AI Applications to Forest Science” in a scientific
journal as an insert for that issue [
13
]. Saarikko authored an early comprehensive summary
of forestry information resources available on the Internet [
14
]. Thompson was one of the
pioneers in forest ecology using web technologies to transfer information in a collaboration
process between the community of researchers and forest managers [
15
]. It became obvious
that knowledge management systems using web-based hypertext had an enormous com-
petitive advantage over stand-alone systems. Universally available access and inexpensive
updating appeared to be success factors in natural resource management for making digital
scientific content attractive to replace traditional, paper-based methods [
16
]. In addition,
the developments in semantic web technologies allowed information to be represented in
Information 2025,16, 37 3 of 19
structured graphs, reuse information, and generate new information from existing data
sources through inference tools [
17
]. However, different perceptions of the domain of the
involved partners and inadequate transfer knowledge methods can lead to an unsuccessful
knowledge transfer process [18–20].
Bridging the gap in forest management between researchers and practitioners is a chal-
lenging task for knowledge transfer [
21
,
22
]. Communicating biodiversity-related research
to forest managers is essential for maintaining the long-term resilience of ecosystems under
changing environmental conditions. The research community is most often concerned
about the approval of hypotheses and the publication of their findings in scientific journals.
The community of forest managers tends to ask questions about the relevance of research
findings and requests information which supports forest management in general. It is
therefore essential to know the needs and requirements of the involved communities [
23
].
Recent developments have clearly shown that the active integration of the needs of actors
into the knowledge transfer processes is important [
20
] Böcher et al., 2020). The commu-
nity of researchers is often heavily tied to the project organization and their domain of
expertise [
24
]. Forest managers are often not connected to one or more individuals in the
community of researchers, which hinders their interaction [
25
]. However, it is essential
to reframe knowledge into a vocabulary both partners are able to understand and allow
bridging the knowledge gap between the connected communities. Standards that allow
enhancing information to a richer, context-specific domain knowledge have been devel-
oped [
26
] but the systematic integration of semantic web technologies [
27
] is still a key
factor in facilitating knowledge transfer on the internet [
28
,
29
]. Uncertainties still arise
about how to fit new fundamental research findings into existing domain concepts. Often
new categorizations need to be defined and communicated to raise the understanding and
support knowledge transfer from research to practice.
1.3. Objectives
Two ways of communication are commonly described in environments that could
be facilitated by knowledge transfer solutions [
30
,
31
]: (i) to directly push knowledge
from science to the interested communities of practitioners and (ii) to raise knowledge
demands and stimulate managers to pull knowledge from the research community. With
the proposed focus on knowledge exchange, we want to decrease potential barriers in
communication using aspects of both ways.
In this contribution, we want to demonstrate how a knowledge representation of
the forest biodiversity domain can be used to bridge the gap between the requirements
of forest management and biodiversity research. Based on the identified dissemination
needs, the conceptual design and implementation of a web-based knowledge transfer
platform (KTP) to various stakeholders is presented. We describe the architectural design
of the KTP and demonstrate how different components help to solve the challenges in
knowledge transfer. We introduce tools and functionalities which allow the platform users
to explore, find, and communicate content based on a common understanding of the
forest biodiversity domain. A common vocabulary is chosen to support the interaction
between the platform components using semantic web technologies. We show how a user
experience-based methodology supports early user involvement and content creation on a
collaborative basis.
2. Knowledge Transfer Platform Concept
With the design of the knowledge transfer platform (KTP), critical challenges in
knowledge transfer are addressed. We followed Garrett’s user-centered design approach
to minimize identified barriers to knowledge transfer and involve the user perspectives
Information 2025,16, 37 4 of 19
of the communities right from the beginning [
32
]. The methodology allows for designing
and developing web applications and information-oriented websites with a focus on user
experience. In this context, KTP can be seen as an information-oriented website with a
strong focus on knowledge representation and transfer. In Figure 1, the user-centered
design approach to information-oriented websites is shown. The following chapters refer
to the proposed user-centered design and demonstrate how the KTP and the processes
related to it have been tailored to meet those principles.
Information 2025, 16, x FOR PEER REVIEW 4 of 19
of the communities right from the beginning [32]. The methodology allows for designing
and developing web applications and information-oriented websites with a focus on user
experience. In this context, KTP can be seen as an information-oriented website with a
strong focus on knowledge representation and transfer. In Figure 1, the user-centered de-
sign approach to information-oriented websites is shown. The following chapters refer to
the proposed user-centered design and demonstrate how the KTP and the processes re-
lated to it have been tailored to meet those principles.
Figure 1. Garre’s five planes of user experience focus on information-oriented websites consider-
ing the level of abstraction and the process of conception to completion [32].
2.1. User Needs and Site Objectives
In the first abstraction layer (1), site objectives and user needs are defined. The per-
sonal requirements of the users with regard to knowledge transfer in functional biodiver-
sity research were identified through the workshops and related to the description of the
work of the FunDivEUROPE project. The overall project goal was to foster communication
with forest managers and policymakers during and after project life and support research-
ers in the aggregation of findings of the project’s forest biodiversity research [33]. There-
fore, authors should be able to create new knowledge, raise or prove hypotheses on the
role of species diversity in the provision of ecosystem services, and discuss their
knowledge elicitation. Forest Managers and policymakers require a mechanism to formu-
late questions and address them to the community of researchers to get them answered.
The KTP supports individuals from research and forest management with a defined set of
Visual Design
Navigation Design
Visual Design
Information Architecture
Content Strategy
User Needs
Site Objectives
Completion
Time
Conception
Level of Abstraction
5. Surface Plane
4. Skeleton Plane
3. Structure Plane
2. Scope Plane
1. Strategy Plane
Figure 1. Garrett’s five planes of user experience focus on information-oriented websites considering
the level of abstraction and the process of conception to completion [32].
2.1. User Needs and Site Objectives
In the first abstraction layer (1), site objectives and user needs are defined. The personal
requirements of the users with regard to knowledge transfer in functional biodiversity
research were identified through the workshops and related to the description of the work
of the FunDivEUROPE project. The overall project goal was to foster communication with
forest managers and policymakers during and after project life and support researchers
in the aggregation of findings of the project’s forest biodiversity research [
33
]. Therefore,
authors should be able to create new knowledge, raise or prove hypotheses on the role
of species diversity in the provision of ecosystem services, and discuss their knowledge
elicitation. Forest Managers and policymakers require a mechanism to formulate questions
and address them to the community of researchers to get them answered. The KTP
supports individuals from research and forest management with a defined set of tools to
Information 2025,16, 37 5 of 19
communicate, find, and explore the FunDivEUROPE knowledge base. The knowledge
transfer process from the user’s requirements perspective is drawn in Figure 2.
Information 2025, 16, x FOR PEER REVIEW 5 of 19
tools to communicate, find, and explore the FunDivEUROPE knowledge base. The
knowledge transfer process from the user’s requirements perspective is drawn in Figure
2.
Figure 2. Platform facilitated knowledge transfer concept of the KTP. Dashed lines refer to the
knowledge domain of different communities (e.g. researchers, forest managers, policy makers) who
try to derive a common understanding via the KTP (solid lines) by using the push or the pull strat-
egy.
Based on the user needs, two strategies of knowledge transfer were designed to pro-
vide a push- and pull knowledge workflow: The transferrable objects in these workflows
are content items, including elicited knowledge. The different content items will be de-
scribed in the content strategy section.
A push strategy supports the research community and authors of the platform to
elicit their domain knowledge and allows them to disseminate research findings. In this
strategy, researchers gain the possibility of communicating contrasting or provoking re-
sults that are able to confirm existing hypotheses on functional diversity research. Differ-
ent content items are semantically preprocessed and enhanced with metadata to allow
meaningful exchange of knowledge between two individuals of different communities.
The pull strategy defines the request of an individual from the community of forest
managers for a particular problem. A manager may want to know how tree species mix-
tures should be adapted to mitigate the negative impacts of climate change. The KTP helps
to formulate questions that fit the common knowledge base and provide access to infor-
mation which may be useful. Otherwise, when there is no matching information to be
transferred, the questions are redirected to an expert with the necessary expertise to reply.
2.2. Content Strategy
In line with the second plane (2) of the user-centered design approach by Garre the
content strategy of the KTP is described [32]. The platform allows the transfer of expert-
Figure 2. Platform facilitated knowledge transfer concept of the KTP. Dashed lines refer to the
knowledge domain of different communities (e.g., researchers, forest managers, policy makers) who
try to derive a common understanding via the KTP (solid lines) by using the push or the pull strategy.
Based on the user needs, two strategies of knowledge transfer were designed to
provide a push- and pull knowledge workflow: The transferrable objects in these work-
flows are content items, including elicited knowledge. The different content items will be
described in the content strategy section.
A push strategy supports the research community and authors of the platform to elicit
their domain knowledge and allows them to disseminate research findings. In this strategy,
researchers gain the possibility of communicating contrasting or provoking results that
are able to confirm existing hypotheses on functional diversity research. Different content
items are semantically preprocessed and enhanced with metadata to allow meaningful
exchange of knowledge between two individuals of different communities.
The pull strategy defines the request of an individual from the community of forest
managers for a particular problem. A manager may want to know how tree species mixtures
should be adapted to mitigate the negative impacts of climate change. The KTP helps to
formulate questions that fit the common knowledge base and provide access to information
which may be useful. Otherwise, when there is no matching information to be transferred,
the questions are redirected to an expert with the necessary expertise to reply.
2.2. Content Strategy
In line with the second plane (2) of the user-centered design approach by Garrett the
content strategy of the KTP is described [
32
]. The platform allows the transfer of expert-
generated knowledge utilizing different tools. Each tool orchestrates a set of consistent
Information 2025,16, 37 6 of 19
content items to represent information. The transferable content items are categorized into
five main categories.
•
(a) Knowledge Elements, (b) Publications, (c) Facts, or (d) Hypotheses are formulated
by researchers,
•
while (e) Questions are often a vehicle to raise knowledge needs from a user point
of view.
A knowledge element (a) is the central content item of the KTP which captures
transferable, elicited knowledge, including references to a set of related content items.
The researcher community uses this type to support knowledge elicitation processes.
Knowledge elements (KE) are used to deliver a structured knowledge entity regarding
the functional significance of biodiversity for the provision of forest ecosystem services.
Providing context (e.g., location, keywords) and references (e.g., publications, reports,
facts) to the knowledge element allows others to understand the statement. Authors can
use knowledge elements to respond to specific questions or relate them to hypothesis. A
knowledge element has to address specific user groups (forest manager, policy maker,
and researcher). Publications (b) and facts (c) represent domain knowledge which could
be referenced in a knowledge element to frame the context in the knowledge transfer.
Scholarly content, such as journal papers and books, can be used to provide evidence,
reference, or further readings on the knowledge elements that are published. For each
publication, the full record of information (authors, abstract, keyword, and publication
details) will be displayed.
By raising questions (e), registered users of the KTP can drop a scientific question to
the community and stimulate a discussion on a certain aspect. Providing context to the
question (hypothesis, publications, location) will allow focus on a particular aspect. There
can be more knowledge elements related to a single question, which allows also different
statements to be published in the context of a single question.
Adding hypothesis (d) allows posting new assumptions regarding the functional sig-
nificance of biodiversity for the provision of forest ecosystem services in Europe. Providing
context to the hypothesis (e.g., publications) allows responding more clearly. There can
be more questions or knowledge elements related to a single hypothesis, which enables
contrasting statements and observations for a single hypothesis. Locations can be used
to add a new regional entity (continents, sub-continents, states, provinces, regions). The
location links a geographical reference to a content element.
Those requirements lead to functional specifications of platform tools which combine
various content items and allow to set up knowledge transfer workflows and tools. We
specify tools to (i) communicate, (ii) find and (iii) explore research knowledge about the
functional significance of forest biodiversity.
(i)
“Communication” tools allow a user to formulate a knowledge request based on
a question raised by the experts or provide a comment on an existing knowledge
element. The experts communicate with the users by responding to a question or by
commenting on the user entries.
(ii)
“Find” tools handle user requirements where an individual of a community of in-
terested stakeholders (forest manager or politicians) intends to search for elicited
forest biodiversity knowledge. These knowledge elements are often a result of a push
workflow. Another requirement of the “find tool” was the careful investigation of
the knowledge base to support the knowledge elicitation process. A graph-based
navigation approach was designed to interactively explore the content items of the
common vocabulary in a web browser. The graph representation allows the user to
traverse to single terms of interest (e.g., soil, litter, tree) and retrieve a definition of
the selected term, and all the content which is related. Users are able to define filters
Information 2025,16, 37 7 of 19
(intended target users, author, tag) and initiate the search by using the tag cloud,
a search bar, and graph-based navigation. Additionally, platform users are able to
search for other users to find experts of a special interest.
(iii)
The “explore” option allows dynamic interaction with the grounded platform content
to stimulate knowledge generation. The “explore” toolset deals with requirements
focusing on processes which need more complex user interaction. An example of an
exploration tool is the developed user cockpit (“myKTP”) which acts as a personal user
control center. In the tool implementation of myKTP, users are enabled to formulate
questions and relate them to specific researchers. For researchers, myKTP shows
open questions where a knowledge request was set by a user acting like a knowledge
consumer. Additionally, the user cockpit shows the recent platform activities of
the user.
2.3. Information Architecture
The next step (3) in the development cycle of the user-centered design approach was to
describe the information architecture which allows interconnecting different content items
and semantically enriching them with metadata. The provided metadata support a common
understanding and a domain-specific interpretation of the terms used. In contrast to the
overall system architecture, the information architecture focuses on the structural design of
the information environment based on (Garett, 2010). The linking and logical order of the
five main content items (a, b, c, d, e) was guaranteed by the use of a controlled vocabulary.
A common understanding is absolutely indispensable for knowledge transfer success
(Figure 1). We developed a FunDivEUROPE thesaurus to act as a controlled vocabulary,
where each content item refers to one or more terms of the knowledge base. For the KTP,
the knowledge representation includes terms related to forest biodiversity research as well
as terms native to forest managers or politicians. The knowledge representation is based
on accepted conceptualizations and relations from the Millennium Ecosystem Assessment
framework (Millenium Ecosystem Assessment, 2005) whereas each term contains a term
definition and an order in a hierarchical structure. A setting of consecutive workshops with
the main actors in forest and biodiversity research in Helsinki and Florence was used to
evolve the original domain thesaurus into a controlled vocabulary [
34
]. Additional findings
of [
35
–
38
] were used to assembly earlier versions of the thesaurus in follow-up discussions.
An in-depth description of the knowledge base engineering process is described by [39].
The Forest biodiversity vocabulary allows the platform users to postulate hypotheses
and link them to a common term. Those categorizations bind related content items (a, b, c,
d, and e) to invalid sequences. A typical sequence starts with a user question and ends with
a relevant knowledge element answering the question. Due to that definition, the forest
biodiversity vocabulary provides domain terms which support the creation of sequences of
content items.
2.4. Information/Navigation Design
Based on the specifications of previous development steps, the navigation design
(4) describes the user interaction of platform tools and involved knowledge elements
(KE). The design follows the principles of the pull and push strategy. Within the push
option, the research community and authors of the platform are supported to disseminate
research findings. Each distinct target user gets a set of related knowledge elements
presented in the personalized user cockpit. To allow intuitive navigation, four different
entry points were defined: (1) featured knowledge elements (selected content which is of
high relevance), (2) recent knowledge elements (all KE that have been added during the
last weeks), (3) most viewed Knowledge Elements (all KE that have the highest rank in
Information 2025,16, 37 8 of 19
page impressions), and (4) top rated Knowledge Elements (all KE that have been rated
by the users with a high score). Figure 3shows an example of a featured knowledge
element on the main page of the KTP and the related content items. The annotated text
allows the user to further explore the presented information in various formats (text,
graph, publication, video, map). A full playlist of all videos can be found here: https:
//www.youtube.com/playlist?list=PL8nmJRi21moFbNkI5xgjGOKIYgCRyd-1e (accessed
on 19 December 2024).
Information 2025, 16, x FOR PEER REVIEW 8 of 19
weeks), (3) most viewed Knowledge Elements (all KE that have the highest rank in page
impressions), and (4) top rated Knowledge Elements (all KE that have been rated by the
users with a high score). Figure 3 shows an example of a featured knowledge element on
the main page of the KTP and the related content items. The annotated text allows the user
to further explore the presented information in various formats (text, graph, publication,
video, map). A full playlist of all videos can be found here: URL (accessed on 19 December
2024): hps://www.youtube.com/playlist?list=PL8nmJRi21moFbNkI5xgjGOKIYgCRyd-
1e.
Figure 3. Example of a featured knowledge element on the main page of the KTP and the related
content items (text, publication, video, map).
With the pull option, the target groups are able to formulate a question via the plat-
form or search for relevant information using the different find options (full-text search,
graphical navigation, categories). In case sufficient knowledge elements are available, the
knowledge transfer process can be supported directly; otherwise, a set of content items,
including at least a knowledge element, would have to be tailored to the needs of the
formulated request.
The knowledge elements are central to the navigation design and so they have to
meet a set of predefined criteria. A final knowledge element has to provide links to a set
of other content items to express its relation to the existing domain knowledge. Each
Figure 3. Example of a featured knowledge element on the main page of the KTP and the related
content items (text, publication, video, map).
With the pull option, the target groups are able to formulate a question via the platform
or search for relevant information using the different find options (full-textsearch, graphical
navigation, categories). In case sufficient knowledge elements are available, the knowledge
transfer process can be supported directly; otherwise, a set of content items, including at
least a knowledge element, would have to be tailored to the needs of the formulated request.
Information 2025,16, 37 9 of 19
The knowledge elements are central to the navigation design and so they have to meet
a set of predefined criteria. A final knowledge element has to provide links to a set of other
content items to express its relation to the existing domain knowledge. Each knowledge
element that provides a link to formulated questions is linked to one or more hypotheses
formulated by the researchers and contains a set of related publications to confirm the
evidence or address related research. Additionally, the knowledge elements are enhanced
with metadata (tags, location) to set the context within the knowledge domain. The user
can navigate along these links to explore the information. Two main user groups (forest
managers and politicians) were predefined for the categorization of the content items.
Knowledge elements can serve several purposes: (1) provide an answer to a question,
(2) link one or more hypotheses to a transferable piece of knowledge, (3) bundle references
to related literature and (4) include facts. Tags are used to indirectly link similar knowledge
elements in a thematic context. Tagging and annotation mechanisms allow the user to find
related content with respect to the domain vocabulary.
To prove the navigation concept for the knowledge representation, we used a work-
shop setting during summer school and several project meetings to obtain feedback from
the research community members about the interaction and navigation options. Based on
the cumulative feedback, we added a graphical navigation to the knowledge browser in
the search options to support the knowledge elicitation process even more.
2.5. Visual Design
The visual design provides a consistent look and feel for all KTP (5) content items.
Visual designs were predefined by the graphical features used in the corporate identity of
the project.
As we identified usability as one major factor for user acceptance, a simple and
recognizable design of content items (a, b, c, d, and e) was made with the help of a
professional graphic designer. The general content view was designed to host the icons for
all content items (a, b, c, d, e) and guide the user through the different concepts. Figure 2
shows a complete list of icons for each content item.
Each generic content item includes a title (e.g., a question or a knowledge element title)
and a larger text body. Additional information (rating, comments, authors, and references)
is added as context.
We designed a graph-based, interactive representation to interact with the forest
biodiversity vocabulary (Figure 4). A search request retrieves a set of vocabulary terms
where the distance of the connected terms represents the degree of relatedness. In the
center of the graph, the search term is always displayed. Each term of the vocabulary has
a definition to provide a distinct meaning in the context of forest biodiversity research.
A search result includes the definition, a set of related vocabulary terms, and a list of
all referred content items (e.g., knowledge elements, questions, hypothesis, literature) to
provide the user with a broad overview of the search topic. Figure 5shows two examples
matching the search on “Forest biodiversity”.
Figure 5generic short view—design of platform content. (1) Icon representing the ele-
ment, (2) title, (3) text preview, (4) teaser picture, (5) rating, (6) author, (7) more information
about the author, and (8) comments on the element
Information 2025,16, 37 10 of 19
Information 2025, 16, x FOR PEER REVIEW 10 of 19
Figure 4. KTP Browser shows a graph-based representation of the forest biodiversity vocabulary.
Figure 5. design of content items within a search result view.
Figure 5 generic short view—design of platform content. (1) Icon representing the
element, (2) title, (3) text preview, (4) teaser picture, (5) rating, (6) author, (7) more infor-
mation about the author, and (8) comments on the element
Figure 4. KTP Browser shows a graph-based representation of the forest biodiversity vocabulary.
Information 2025, 16, x FOR PEER REVIEW 10 of 19
Figure 4. KTP Browser shows a graph-based representation of the forest biodiversity vocabulary.
Figure 5. design of content items within a search result view.
Figure 5 generic short view—design of platform content. (1) Icon representing the
element, (2) title, (3) text preview, (4) teaser picture, (5) rating, (6) author, (7) more infor-
mation about the author, and (8) comments on the element
Figure 5. Design of content items within a search result view.
3. System Architecture of the Knowledge Transfer Platform
The system architecture of the FunDivEUROPE knowledge transfer tool (KTP) im-
plements the concept identified during the user-centered design approach. To fulfill the
collected user requirements, we choose a combination of a traditional WCMS (Web con-
tent management system) and the services of a semantic engine (see Figure 6). In this
Information 2025,16, 37 11 of 19
way, the content items of the WCMS, persisted in the content repository, were combined
and expanded with additional meta-information from the metadata repository of the se-
mantic engine. Additionally, a vocabulary server was used to develop and maintain the
metadata repository. The metadata repository acts as a common understanding and a
domain-specific interpretation of a shared vocabulary for the platform community. The
current implementation uses a combination of Drupal
®
[
40
,
41
] and Apache Stanbol™ [
42
]
as a framework to perform the metadata enhancement. Tematres© [
43
,
44
] was used for the
role of vocabulary server.
Information 2025, 16, x FOR PEER REVIEW 11 of 19
3. System Architecture of the Knowledge Transfer Platform
The system architecture of the FunDivEUROPE knowledge transfer tool (KTP) im-
plements the concept identified during the user-centered design approach. To fulfill the
collected user requirements, we choose a combination of a traditional WCMS (Web con-
tent management system) and the services of a semantic engine (see Figure 6). In this way,
the content items of the WCMS, persisted in the content repository, were combined and
expanded with additional meta-information from the metadata repository of the semantic
engine. Additionally, a vocabulary server was used to develop and maintain the metadata
repository. The metadata repository acts as a common understanding and a domain-spe-
cific interpretation of a shared vocabulary for the platform community. The current im-
plementation uses a combination of Drupal® [40,41] and Apache Stanbol™ [42] as a frame-
work to perform the metadata enhancement. Tematres© [43,44] was used for the role of
vocabulary server.
Figure 6. General System Architecture.
Architectural Components
Traditional Web Content Management Systems like Drupal combine various plat-
form components to organize data in a structured way to facilitate collaboration. Data are
stored in a tree of nodes and persist in the content repository of the platform. Each node
can have one or more types that define the properties, the number, and type of its child
nodes, and its behavior. Modules make use of these basic structures and add functionality
on top of it. Blocks and menus allow interaction with respect to the configured modules.
User management controls allow the platform to target contributed information to the
Figure 6. General System Architecture.
Architectural Components
Traditional Web Content Management Systems like Drupal combine various platform
components to organize data in a structured way to facilitate collaboration. Data are stored
in a tree of nodes and persist in the content repository of the platform. Each node can
have one or more types that define the properties, the number, and type of its child nodes,
and its behavior. Modules make use of these basic structures and add functionality on top
of it. Blocks and menus allow interaction with respect to the configured modules. User
management controls allow the platform to target contributed information to the intended
audience and templates define the look and feel of the involved websites. Generated
knowledge elements, defined hypothesis, questions, and other content items are stored in
the content repository of the WCMS. The find, communication, and exploration tools of the
KTP (see Section 2.2) add functionalities (KTP tools) to the existing WCMS modules and
orchestrate content items in a way that helps platform users in their knowledge transfer
tasks (including tag clouds, graph visualization of the knowledge base or the integration
of a bibliographic module to enhance related research with metadata). All KTP Tools
support the implementation of the content strategy (see Section 2.2). For each of the
Information 2025,16, 37 12 of 19
three categories (communication, find, and explore) we developed tools to support the
knowledge transfer process between the community of researchers and forest managers.
As an example of each category, one tool is introduced to demonstrate its interaction
within the system architecture. The WCMS supports communication with the push and
pull workflow in the cockpit (myKTP tool). In the push workflow, a researcher registers
as an author and publishes the research with intrinsic motivation to the community of
stakeholders. This functionality facilitates knowledge elicitation within the KTP. The pull
workflow is realized via the user cockpit of the KTP. Web scripts for knowledge elicitation of
various content items were developed to describe the transferable knowledge of the content
and metadata repositories. We developed a semantic search tool to support platform
users in browsing related content items. The WCMS standard search was extended by
a faceted search mechanism to facilitate discovery-driven examination of content items
of the knowledge base [
45
]. We defined a semantic navigation module to visualize the
forest biodiversity knowledge base for WCMS users (Figure 4). Each term of the controlled
vocabulary allows exploring the term definition and retrieves related content items. The
navigation is interconnected to a faceted search functionality which allows the user to
switch between the views without losing focus. When a user is searching for content items
related to the term “forest biodiversity” a semantic research algorithm not only retrieves
knowledge elements which are directly related to the searched term but also knowledge
elements which are related to it (e.g., species diversity) using semantic relations between the
terms of the common vocabulary. Menu and block structures enable interaction between
tools and implement the navigation strategy of the KTP. Templates are primarily used to
transcribe the visual design. KTP users (forest managers and researchers) act as authors
who actively generate content items or consumers who passively browse content. The
WCMS user permission module adds functionality to manage permissions for registered
and unregistered users by granting access to KTP tools depending on the permissions for
each group. Unregistered users are allowed to use tools to find knowledge elements while
registered users additionally utilize communication and exploration tools. User data are
stored in the WCMS database.
The Semantic Engine extends the provided content items of the WCMS with respect to
the information architecture of forest biodiversity research (see Section 2.3). The semantic
engine structures concepts, relationships, and logic of the information architecture in a
machine-understandable representation of the domain of forest biodiversity research. The
enhancer of the semantic engine is responsible for semantically enriching content so that
the target users are able to interpret content items in the specific forest biodiversity domain
vocabulary. Content enhancement was conducted by combining the CMS content items
of the content repository with the metadata added from the metadata repository of the
semantic engine. The current version of the forest biodiversity knowledge representation
was imported from the vocabulary server into the semantic engine. The imported SKOS
(Simple Knowledge Organization System) thesaurus persists in the entity hub of the
semantic engine and an interface is supporting the updating process. Entities of the forest
biodiversity thesaurus include terms as well as relations between those terms and term
definitions. The enhancement engine is responsible for facilitating the process of content
enhancement. To retrieve content items of the WCMS, including metadata, the reasoner
responds based on the implemented set of rules. REST (Representational State Transfer)
calls accomplish interaction between the CMS and the semantic engine via a web service
infrastructure. For the automatic annotation, user interface widgets are used. The WCMS
relies on the VIE (Vienna IKS Editables) JavaScript library [
46
]. Each time a platform
user adds a content item to the CMS, the enhancement engine is triggered to add tags
and/or annotate the text body of the element. At any time, the thesaurus representation
Information 2025,16, 37 13 of 19
of the semantic engine acts as a controlled set of vocabulary terms which are used to
interpret information within the knowledge domain. Annotation and automated tagging
functionalities allow the group of experts to enhance the generated content. The annotation
approach analyzes full text, semi-automatically enhances the text with definitions, and
relates matched terms to a controlled vocabulary. The second variant, tagging suggestion,
verifies different content items and adds metadata to a content item. Both concepts are
used to interpret WCMS content as enhanced KTP knowledge elements in the knowledge
transfer. While “annotation” provides meaning to identified text passages, “tagging” is
responsible for classifying content items based on the thesaurus structure of the semantic
engine. Exploration patterns rely more on “tagging” while “annotation” fits best with
communication patterns.
A vocabulary server facilitates the iterative, collaborative development process of the
domain thesaurus and persists in a common vocabulary representation [
39
]. The taxonomy
manager allows for maintaining a consistent vocabulary. Term definitions, the relation
between terms, and the hierarchical order of terms are stored in the common vocabulary
repository of the vocabulary server. The representation of the forest biodiversity domain is
used to update the metadata repository of the entity hub. The semantic engine publishes a
SKOS (Simple Knowledge Organization System) interface to support the updating process
of the vocabulary (Figure 6).
4. Discussion
There are several challenges related to knowledge transfer which are related to the
different perceptions regarding the problem domain [
18
,
20
]. The approach taken to design
the components of the FunDivEurope platform tries to overcome those challenges in con-
ducting a collaborative development process. The limitations of the chosen methodology
and the role of semantic technology are critically discussed here. We identify how various
perspectives of the development methodology, utilized technology, and characteristics of
community structures of the KTP are used to overcome individual barriers (loss of power,
revelation, uncertainty, and motivation) and social barriers (language, conflict avoidance,
bureaucracy, and hierarchy and incoherent paradigms) (compare [23]).
4.1. Development Methodology and Design Principles
User involvement during the development process allows identifying user require-
ments right from the start. A user-centered development perspective helps to identify
many of the individual and social barriers in different stages of development.
The approach following the design principles of Garrett [
32
] allowed us to design the
knowledge transfer platform with respect to the demands of the community of researchers
and forest managers including their requirements (Section 2.1). The content strategy, the
information architecture, and the visual design elements have been tailored to the needs
identified in the collaborative development process. Authors of the KTP should be able
to create new content items for describing the role of species diversity in the provision of
ecosystem services. Forest Managers and policymakers have been supported to formulate
questions of relevant decision problems related to biodiversity research and address them
to the community of researchers to obtain answers. Due to the differences in the community
structure, we wanted to overcome the identified knowledge transfer barriers. Users of the
community of researchers tend to represent a community of practice where individuals
share the same interests, use a common language, and share mutual understanding in the
forest biodiversity community. These characteristics lower various social and individual
barriers of the user group of researchers. To support the involvement of members in the pe-
riphery, we used a codification strategy based on semantic web technologies (see Section 2.3)
Information 2025,16, 37 14 of 19
to allow the sharing of a common understanding between researchers and forest managers.
The user involvement facilitated the design of the information architecture in collaborative,
iterative development cycles. We considered various stakeholder perspectives (researchers,
forest managers, politicians) to generate a core thesaurus on forest biodiversity-related
topics. As a result, the controlled vocabulary of forest biodiversity research [
39
] targets
enriching the precision of content items of the KTP and lowering uncertainty in knowledge
transfer. This and the derived content strategy (see Section 2.2) have reduced the overall
knowledge gap between members from different backgrounds and allowed participants
within the forest biodiversity domain to conduct a precise, meaningful knowledge transfer.
Additionally, Palmer defines usability and design metrics as important drivers for user
acceptance of websites [
47
]. Navigable websites will be associated with greater perceived
success by site users. Following this principle, a main focus in the navigation design of the
FunDivEurope KTP was the creation of a consistent navigation concept (see Section 2.4)
with a high level of recognition to generate an intuitive user experience (see Section 2.5).
Each content item shares a similar basic view, including a recognizable icon, paired with a
title and description, a set of meta-information, and references linking to more in-depth in-
formation in questions, facts, hypothesis, publications, and knowledge elements. However,
web analytics will need to be conducted in the future to verify usability constraints.
4.2. Tagging and Annotation Technology
Hansen describes codification and personalization as relevant strategies to overcome
knowledge transfer challenges [
48
]. We used tagging and annotation as core techniques to
locate created content to meaningful terms in the domain of forest biodiversity research.
Scientific literature discusses collaborative tagging and tagging based on controlled
vocabularies as the main tagging concepts. Collaborative tagging facilitates users to elicit
and relate information in their own ways and allows including the user perspective in
system development. Tagging based on controlled vocabularies supports the structuring
and codification of information in larger contexts and supports users in the categoriza-
tion. Macgregor and McCulloch argue that collaborative tagging often lacks precision but
increases acceptance due to their usability advantages [
49
]. There are many studies on
tagging behavior in social media [
50
–
52
]. Those meta-studies identified the advantages and
disadvantages of free and controlled tagging mechanisms. Greenberg foresees increased
costs of metadata extraction, especially in fast-growing knowledge domains [
53
]. Studies
have demonstrated that for a particular content item, the number of users who tagged it,
the initial date when the item was tagged, and the life span of the item are statistically sig-
nificant to the ratio of the distinct tag number (in terms of the initial date of tagging) to the
total tag number (in terms of the life span that tags are provided) for a given item [
54
]. We
respond to those arguments by combining both strategies with a hybrid tagging mechanism
used in the KTP. A controlled vocabulary provides a basic thesaurus for all platform users.
This domain vocabulary was developed collaboratively with the user group of researchers
of the FunDivEurope core-periphery network. The vocabulary serves as a formal structure
of terms and relations where each term relies on a distinct definition. The vocabulary can
be used for manual or semi-manual tagging of content items and automatic annotation
of documents. Additionally, the KTP allows collaborative tagging to further stimulate
the development of the controlled vocabulary and reduce costs in vocabulary revision.
However, further developments need to address the automatic interaction between those
two tagging concepts.
Many studies show positive effects on learning enhanced by annotation tech-
niques [
55
]. By using the automatic annotation feature of the KTP, the users support
knowledge management processes [
56
]. Carr et al. identify annotation as a key process
Information 2025,16, 37 15 of 19
to mark information explicitly in documents [
57
]. The semantically enhanced documents
allow better interoperability of documents and support semantic search algorithms that
rely on annotation. Authors often refer to various domain concepts in their documents
and use implicit terms to add relevant meaning to their annotation processes. In this
context, the annotated content defines a piece of knowledge connected with the author’s
intrinsic knowledge representation, which enhances the transfer [
58
]. However, future
developments will have to consider any sort of WCMS document (e.g., pdf) to increase the
annotation penetration.
4.3. Community Network Structures
In this paper, we describe the technical perspective of implementing a successful
knowledge transfer. The KTP provides access to 125 expert profiles, more than 250 pub-
lications, related knowledge elements, 13 videos, and in addition various questions and
hypotheses that relate to the presented knowledge elements. Some performance indicators
indicate low usage and interaction with the KTP; 52 out of 125 members never accessed
their profile. The top knowledge elements had only 250 views, the top videos more than
500 views and generally there was a low number of comments provided. Despite the
fact that we provide a toolset to support forest managers, policymakers, and the research
community in collaboration in a specific knowledge domain, we were not able to address
the social and organizational perspective of knowledge transfer. Bodin and Crona analyzed
various social network structures to show their implication on a successful knowledge
transfer [
24
]. An imbalanced network may establish social or individual knowledge transfer
barriers. Balanced, highly dense networks increase any kind of collaboration and increase
the level of trust over time. Another important network measure is the level of cohesion.
In our approach, we define two different user groups: the authoring group “produces
knowledge” and the user group “consumes elicited knowledge”. In this regard, it has been
shown that affiliation to different institutions often leads to social barriers and low cohesive
network structures that might influence collaboration processes between subgroups in
one community negatively [
59
]. Also Böcher argue that scientists and policymakers act in
totally different “worlds” based on different interests and values [
20
]. Politics focuses on
the acquisition of power and interests under short-term conditions, while science is based
on the continuous, unlimited search for truth. Therefore, scientific findings, no matter how
important they are, are not likely to be utilized if the central interests of political actors run
counter to them. However, the knowledge transfer is not limited to the group of researchers,
forest managers, and policymakers, based on the user’s community membership. The
KTPs authorization concept only differs between authors and non-authors.
The KTP aimed to create and maintain a core-periphery network [
25
] where ties within
the knowledge transfer involved communities vary in connection density (see Figure 1).
The core network, the community of researchers, is densely tied together while actors
in the community of forest managers or politicians are primarily connected to the core
actors of the research group. Especially in core-periphery network structures, the role of
single individuals within a network is important. The influence of a single author may
lead to the centrality of a network and may hinder collaboration processes [
60
]. Another
important factor is the existence of bonding and bridging ties within or between (sub-)
networks [
61
]. Bonding ties between subgroups are essentially required for knowledge
transfer processes while bridging ties connect one community network with external
resources to support or initiate collective actions. Böcher and Krott have argued in their
RUI approach that there is a need that scientific knowledge produced in the science system
(Research), and science-based problem solutions that are utilized within practice by political
actors (Utilization) require a strong “Integration” [
22
]. In case the scientific information
Information 2025,16, 37 16 of 19
cannot be integrated into a particular use case, the knowledge transfer might fail. Besides
the joint research activities in the project, the development process of the KTP supported
generally collaborative actions on bridging the ties. In the initializing phase of the KTP,
no imbalanced structures could be identified but there is a need to monitor the network
structure over time and to react to possible unwanted changes. Without the continuous
integration of useful knowledge to support the problem-solving of forest managers and
policymakers, the success of a KTP will be limited [22,62].
The approach presented to support knowledge transfer on research about the func-
tional significance of forest biodiversity can minimize barriers between researchers, forest
managers, and policymakers. However, various challenges of the knowledge transfer need
to be addressed in identifying and defining the user requirements and needs. The method-
ology was able to include the different perspectives of the knowledge transfer process in
any step of the platform development. The user-centered focus allowed us to focus on the
user’s requirements from a conceptual point. With the use of semantic web technology,
annotation and tagging were made possible within the knowledge representation to bridge
information gaps between the communities. To validate the semantic web approach, we
constantly need to monitor and discover meaningful patterns in the knowledge transfer
platform to show how well barriers are minimized with our architectural approach. In the
future, the approach could be easily adapted to other knowledge domains making use of
the existing components of the KTP and taking up the outcomes of follow-up projects on
functional diversity research [63].
Author Contributions: Conceptualization, R.A. and V.H.; methodology, R.A. and V.H.; software,
R.A.; validation, V.H.; formal analysis, R.A.; investigation, R.A.; resources, V.H.; data curation, R.A.;
writing—original draft preparation, R.A.; writing—review and editing, V.H.; visualization, R.A.;
supervision, V.H.; project administration, V.H.; funding acquisition, V.H. All authors have read and
agreed to the published version of the manuscript.
Funding: European Commission: The research leading to these results has received funding from the
European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n
◦
265171.
The APC was funded by BOKU University.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: http://project.fundiveurope.eu/ (accessed on 31 December 2024).
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
CBD. Convention on Biological Diversity, Rio de Janeiro, Argentina. Convention on Biological Diversity. 1992. Available online:
https://www.cbd.int/convention/ (accessed on 31 December 2024).
2.
Nadrowski, K.; Wirth, C.; Scherer-Lorenzen, M. Is forest diversity driving ecosystem function and service? Curr. Opin. Environ.
Sustain. 2010,2, 75–79. [CrossRef]
3.
Le Provost, G.; Schenk, N.V.; Penone, C.; Thiele, J.; Westphal, C.; Allan, E.; Ayasse, M.; Binkenstein, J.; Blüthgen, N.; Boeddinghaus,
R.; et al. The supply of multiple ecosystem services requires biodiversity across spatial scales. Nat. Ecol. Evol. 2023,7, 236–249.
[CrossRef]
4. Franklin, J.F. Preserving biodiversity: Species, eco- systems, or landscapes? Ecol. Appl. 1993,3, 202–205. [CrossRef] [PubMed]
5.
Renner, S.C.; Gossner, M.M.; Ayasse, M.; Böhm, S.; Teuscher, M.; Weisser, W.W.; Jung, K. Forest structure, plants, arthropods,
scale, or birds’ functional groups: What key factor are forest birds responding to? PLoS ONE 2024,19, e0304421. [CrossRef]
6.
Springer, K.; Manning, P.; Boesing, A.L.; Ammer, C.; Fiore-Donno, A.-M.; Fischer, M.; Goldmann, K.; Le Provost, G.; Overmann, J.;
Ruess, L.; et al. Identifying the stand properties that support both high biodiversity and carbon storage in German forests. For.
Ecol. Manag. 2024,572, 122328. [CrossRef]
Information 2025,16, 37 17 of 19
7.
Wehner, K.; Schuster, R.; Simons, N.; Norton, R.A.; Blüthgen, N.; Heethoff, M. How land-use intensity affects sexual and
parthenogenetic oribatid mites in temperate forests and grasslands in Germany. Exp. Appl. Acarol. 2021,83, 343–373. [CrossRef]
[PubMed]
8.
Baeten, L.; Verheyen, K.; Wirth, C.; Bruelheide, H.; Bussotti, F.; Finér, L.; Jaroszewicz, B.; Selvi, F.; Valladares, F.; Ampoorter,
E.; et al. A novel comparative research platform designed to determine the functional significance of tree species diversity in
European forests. Perspect. Plant Ecol. Evol. Syst. 2013,15, 281–291. [CrossRef]
9.
Scherer-Lorenzen, M.; Allan, E.; Ampoorter, E.; Avacaritiei, D.; Baeten, L.; Barnoaiea, I.; Bastias, C.C.; Bauhus, J.; Benavides,
R.; Benneter, A.; et al. The functional significance of tree species diversity in European forests—The FunDivEUROPE dataset
[Dataset]. Dryad 2023. [CrossRef]
10.
Menge, J.H.; Magdon, P.; Wöllauer, S.; Ehbrecht, M. Impacts of forest management on stand and landscape-level microclimate
heterogeneity of European beech forests. Landsc. Ecol. 2023,38, 903–917. [CrossRef]
11.
Davenport, T.H.; Prusak, L. Working Knowledge: How Organizations Manage What They Know; Harvard Business Press: Boston, MA,
USA, 1998.
12.
Liao, S.H. Expert system methodologies and applications—A decade review from 1995 to 2004. Expert Syst. Appl. 2004,28, 93–103.
[CrossRef]
13. Rauscher, H.M. The encyclopedia of AI applications to forest science. AI Appl. 1991,5.
14.
Saarikko, J. Forestry Information Resources on the Internet. 1994. Available online: http://www.funet.fi/pub/sci/forest/doc/
guides/Saarikko-1994b.html (accessed on 31 December 2024).
15.
Thompson, A.J. Elicitation and representation of Traditional Ecological Knowledge, for use in forest management. Comput.
Electron. Agric. 2000,27, 155–165. [CrossRef]
16.
Rauscher, H.M.; Reynolds, K.; Vacik, H. Editorial: Decision support systems for forest management. Comput. Electron. Agric. 2005,
49, 1–5. [CrossRef]
17.
Shaohua, J.; Xue, F.; Bo, Z.; Jingting, S. Semantic enrichment for BIM: Enabling technologies and applications. Adv. Eng. Inform.
2023,56, 101961. [CrossRef]
18.
Szulanski, G. The Process of Knowledge Transfer: A Diachronic Analysis of Stickiness. Organ. Behav. Hum. Decis. Process. 2000,
82, 9–27. [CrossRef]
19.
Thompson, A.J. Knowledge elicitation tools for use in a virtual adaptive environmental management workshop. Comput. Electron.
Agric. 2000,27, 57–70. [CrossRef]
20.
Böcher, M. Research Trends: Advanced approaches for a better understanding of scientific knowledge transfer in forest and
forest-related policy. For. Policy Econ. 2020,114, 102165. [CrossRef] [PubMed]
21.
Vacik, H.; Torresan, C.; Hujala, T.; Khadka, C.; Reynolds, K. The role of knowledge management tools in supporting sustainable
forest management. For. Syst. 2013,22, 442–455. [CrossRef]
22.
Böcher, M.; Krott, M. The RIU model as an analytical framework for scientific knowledge transfer: The case of the “decision
support system forest and climate change”. Biodivers. Conserv. 2014,23, 3641–3656. [CrossRef]
23.
Disterer, G. Individual and social barriers to knowledge transfer. In Proceedings of the 34th Annual Hawaii International
Conference on System Sciences, Maui, HI, USA, 3–6 January 2001; p. 7.
24.
Bodin, Ö.; Crona, B.I. The role of social networks in natural resource governance: What relational patterns make a difference?
Glob. Environ. Change 2009,19, 366–374. [CrossRef]
25. Borgatti, S.P.; Everett, M.G. Models of core/periphery structures. Soc. Netw. 2000,21, 375–395. [CrossRef]
26.
Stegwee, R.; Rukanova, B. Identification of different types of standards for dmonain-specific interoperability. In Proceedings
of the Conference Proceedings Standard Making: A Critical Research Frontier for Information Systems, MISQ Special Issue
Workshop, Seattle, WA, USA, 12–14 December 2003.
27. Berners-Lee, T.; Hendler, J.; Lassila, O. The Semantic Web; Scientific America: New York, NY, USA, 2001.
28.
Marques, A.F.; Rosset, C.; Rasinmaki, J.; Vacik, H.; Gordon, S.; Nobre, S.; Falcao, A.; Weber, D.; Granitzer, M.; Eriksson, L.O.
Collaborative development of a semantic wiki on forest management decision support. Scand. J. For. Res. 2014,29 (Suppl. 1),
30–43. [CrossRef]
29.
Abacha, A.B.; Zweigenbaum, P. MEANS: A medical question-answering system combining NLP techniques and semantic Web
technologies. Inf. Process. Manag. 2015,51, 570–594. [CrossRef]
30. Dalkir, K. Knowledge Management in Theory and Practice; Routledge: London, UK, 2013.
31.
De Silva, M.; Al-Tabbaa, O.; Pinto, J. Academics engaging in knowledge transfer and co-creation: Push causation and pull
effectuation? Res. Policy 2023,52, 104668. [CrossRef]
32.
Garrett, J.J. Elements of User Experience, the: User-Centered Design for the Web and Beyond; Pearson Education: Upper Saddle River,
NJ, USA, 2010.
Information 2025,16, 37 18 of 19
33.
DOW. Description of Work, Functional significance of forest diversity in Europe (FunDivEUROPE). Project proposal used to
apply for funding under the European Union Seventh Framework Programme (FP7/2007-2013) 2010.
34.
FunDivEUROPE. Documentation on the collaborative workshop “How to produce useful knowledge?”. In Proceedings of the 1st
Annual Meeting of FunDivEUROPE, Helsinki, Finland, 16–19 January 2012.
35.
Chapin, F.S., III; Zavaleta, E.S.; Eviner, V.T.; Naylor, R.L.; Vitousek, P.M.; Reynolds, H.L.; Hooper, D.U.; Lavorel, S.; Sala, O.E.;
Hobbie, S.E.; et al. Consequences of changing biodiversity. Nature 2000,405, 234–242. [CrossRef]
36.
Haines-Young, R.; Potschin, M. The links between biodiversity, ecosystem services and human well-being, Chapter 6. In Ecosystem
Ecology: A New Synthesis; Raffaelli, D., Frid, C., Eds.; BES Ecological Reviews Series; CUP: Cambridge, UK, 2010; pp. 110–139.
37.
Martín-López, B.; Gómez-Baggethun, E.; González, J.A.; Lomas, P.L.; Montes, C. The assessment of ecosystem services provided
by biodiversity: Re-thinking concepts and research needs. In Handbook of Nature Conservation: Global, Environmental and Economic
Issues; Nova Science Pub Inc.: Hauppauge, NY, USA, 2009; pp. 261–282.
38.
TEEB. The Economics of Ecosystems and Biodiversity: Mainstreaming the Economics of Nature: A Synthesis of the Approach, Conclusions
and Recommendations of TEEB; TEEB: Brussels, Belgium, 2010.
39.
Radl, A.; Vacik, H. Potential of Semantic Web Technologies to Support Knowledge Transfer in Forest Management; KOED: New Delhi,
India, 2013.
40. Drupal®.Drupal Version 7; Dries Buytaert: Portland, OR, USA, 2013.
41.
Corlosquet, S.; Delbru, R.; Clark, T.; Polleres, A.; Decker, S. Produce and Consume Linked Data with Drupal! In The Semantic
Web-ISWC 2009; Springer: Berlin/Heidelberg, Germany, 2009; pp. 763–778.
42. Stanbol™. Stanbol Version 0.11; Apache Software Foundation: Wakefield, MA, USA, 2013.
43.
Tematres©. Tematres 1.6, Diego Ferreyra, University of Buenos Aires. 2013. Available online: https://github.com/tematres
(accessed on 31 December 2024).
44.
Gonzales-Aguilar, A.; Ramírez-Posada, M.; Ferreyra, D. TemaTres: Software para gestionar tesauros. El Prof. Inf. 2012,21, 319–325.
[CrossRef]
45.
Laporte, M.A.; Garnier, E.; Mougenot, I. A faceted search system for facilitating discovery-driven scientific activities: A use case
from functional ecology. In Proceedings of the 1st International Workshop on Semantic for Biodiversity, Montpellier, France, 27
May 2013.
46.
Grünwald, S.; Bergius, H. Decoupling Content Management. In Proceedings of the Developer Track, WWW2012 Conference,
Lyon, France, 16–20 April 2012.
47. Palmer, J.W. Web site usability, design, and performance metrics. Inf. Syst. Res. 2002,13, 151–167. [CrossRef]
48. Hansen, M.T.; Nohria, N.; Tierney, T. What’s Your Strategy For Managing Knowledge. Harv. Bus. Rev. 1999,77, 106–116.
49.
Macgregor, G.; McCulloch, E. Collaborative tagging as a knowledge organisation and resource discovery tool. Libr. Rev. 2006,55,
291–300. [CrossRef]
50. Golder, S.A.; Huberman, B.A. Usage patterns of collaborative tagging systems. J. Inf. Sci. 2006,32, 198–208. [CrossRef]
51.
Mathes, A. Folksonomies-Cooperative Classification and Communication Through Shared Metadata. 2004. Available online:
https://adammathes.com/academic/computer-mediated-communication/folksonomies.html (accessed on 31 December 2024).
52.
Kang, J.; Yoon, J.; Park, E.; Han, J. “Why tag me?”: Detecting motivations of comment tagging in Instagram. Expert Syst. Appl.
2022,202, 117171. [CrossRef]
53.
Greenberg, J. Metadata extraction and harvesting: A comparison of two automatic metadata generation applications. J. Internet
Cat. 2004,6, 59–82. [CrossRef]
54.
Mathioudakis, M.; Koudas, N.; Marbach, P. Early online identification of attention gathering items in social media, WSDM 2010.
In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, New York City, NY, USA, 3–6 February
2010; pp. 301–310.
55.
Su, A.Y.; Yang, S.J.; Hwang, W.Y.; Zhang, J. A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in
collaborative learning environments. Comput. Educ. 2010,55, 752–766. [CrossRef]
56.
Uren, V.; Cimiano, P.; Iria, J.; Handschuh, S.; Vargas-Vera, M.; Motta, E.; Ciravegna, F. Semantic annotation for knowledge
management: Requirements and a survey of the state of the art. J. Web Semant. 2006,4, 14–28. [CrossRef]
57.
Carr, L.; Miles-Board, T.; Woukeu, A.; Wills, G.; Hall, W. The case for explicit knowledge in documents. In Proceedings of the 2004
ACM Symposium on Document Engineering, Milwaukee, WI, USA, 28–30 October 2004; pp. 90–98.
58.
Abiodun C., R. Annotation for knowledge sharing in a collaborative environment. J. Knowl. Manag. 2009,1, 111–119. [CrossRef]
59. Frank, K.A. Identifying cohesive subgroups. Soc. Netw. 1995,17, 27–56. [CrossRef]
60. Burt, R.S. Structural Holes: The Social Structure of Competition; Harvard University Press: Cambridge, MA, USA, 2009.
61.
Newman, L.; Dale, A. Homophily and agency: Creating effective sustainable development networks. Environ. Dev. Sustain. 2007,
9, 79–90. [CrossRef]
Information 2025,16, 37 19 of 19
62.
Heinrichs, S.; Schall, P.; Ammer, C.; Fischer, M.; Gossner, M. Annahmen und Ergebnisse zur Biodiversität im Wirtschaftswald—
Neues aus der Biodiversitätsforschung. WSL-Berichte 2020,100, 15–29.
63.
BEO. Zentrales Koordinationsbüro der Biodiversity Exploratives, Informationsveranstaltung: Highlights aus 15 Jahren Biodiver-
sitätsforschung. 2024. Available online: https://www.biodiversity-exploratories.de/de/news/ (accessed on 19 December 2024).
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