=Paper=
{{Paper
|id=Vol-464/paper-13
|storemode=property
|title=An Extensible Semantic Wiki Architecture
|pdfUrl=https://ceur-ws.org/Vol-464/paper-07.pdf
|volume=Vol-464
|dblpUrl=https://dblp.org/rec/conf/semwiki/ReutelshoferHLB09
}}
==An Extensible Semantic Wiki Architecture==
https://ceur-ws.org/Vol-464/paper-07.pdf
An Extensible Semantic Wiki Architecture
Jochen Reutelshoefer, Fabian Haupt, Florian Lemmerich, Joachim Baumeister
Institute for Computer Science, University of Würzburg, Germany
email: {reutelshoefer, fhaupt, lemmerich, baumeister}@informatik.uni-wuerzburg.de
Abstract. Wikis are prominent for successfully supporting the quick
and simple creation, sharing and management of content on the web.
Semantic wikis improve this by semantically enriched content. Currently,
notable advances in different fields of semantic technology like (para-
consistent) reasoning, expressive knowledge (e.g., rules), and ontology
learning can be observed. By making use of these technologies, semantic
wikis should not only allow for the agile change of its content but also
the fast and easy integration of emerging semantic technologies into the
system. Following this idea, the paper introduces an extensible semantic
wiki architecture.
1 Introduction
Semantic wikis have become an attractive solution for collaborative knowledge
formalization and sharing. One major challenge at that task is the fact that
knowledge can be represented on many different levels of formality and in a
wide variety of formalisms [1]. Further, the requirements for a semantic wiki
application strongly depend on the domain and the targeted community. In con-
sequence, a wide range of diverse semantic wiki approaches has evolved employ-
ing different techniques, e.g. different types of formalized content or reasoning
capabilities. While, for example, Semantic Media Wiki [2] focuses on efficient
reasoning on large data, IkeWiki [3] allows for easy ontology editing with rich
expressiveness. SweetWiki [4] provides an elaborated Wiki Object Model. The
OntoWiki system [5] supports the combination and visualization of multime-
dia data. AceWiki [6] follows a different knowledge acquisition strategy using
a controlled language. Further wikis work with mathematical, prolog-based or
classification knowledge [7–9]. All these systems were created having various dis-
tinct application scenarios in mind. Accordingly, they are utilizing a wide range
of strategies and technologies, e.g., efficient reasoners, specialized reasoners, con-
trolled languages, various markups, different content browsers and visualizations.
Each of these systems is fitting well to its particular intended purposes but if
one intends to support a specific (novel) semantic wiki application each of the
systems reveals advantages and disadvantages. Without a suitable extensible
semantic wiki one has to choose a suboptimal solution or implement an entire
new semantic wiki system from scratch. We claim that the optimal solution to
support a semantic wiki application needs to be adapted precisely to its require-
ments, considering the domain, the targeted user community and the envisioned
use-cases thoroughly. This analysis reveals the appropriate formalisms, reasoning
support, visualization and querying capabilities needed. But in order to serve a
wider range of these possible requirements and to be able to optimize each se-
mantic wiki application to its domain and community it is beneficial to have
an extensible semantic wiki architecture with a basic toolkit for knowledge for-
malization, knowledge visualization and reasoning. We envision that this kind
of extensible semantic wiki architecture enables the customization of a seman-
tic wiki system to a particular application at low (software) engineering costs
offering various reusable and extensible components. Providing optimal support
to any (non-technical) domain and community will raise the general acceptance
and spread of this technology. Beside selection and integration of existing tech-
niques into a semantic wiki also the agile integration of novel technologies can
improve the general semantic wiki functionality and customization to specific ap-
plications. We presume, that especially advanced reasoners and NLP-techniques
employed for semi-automated knowledge formalization can bring considerable
benefit when being combined with the existing wiki technology.
In this paper we describe the concept of an extensible semantic wiki archi-
tecture and motivate the emerging possibilities. With the system KnowWE we
present a prototype of an extensible semantic wiki architecture and show its
current extensions.
2 Challenges and Dimensions of extending Semantic
Wikis
We briefly outline a conceptual view on semantic wikis in general followed by the
discussion of the possibilities and challenges extending semantic wikis. Figure 1
shows the three components of what we call the “knowledge pipeline” in seman-
tic wikis. It shows the flow of the formalized knowledge from the contributing
user role to the consuming user role through the Knowledge Formalization Com-
ponent, the Reasoning Component, and the Knowledge Presentation Component.
Fig. 1. Sketch of the “knowledge pipeline” of a semantic wiki.
– Knowledge Formalization Component: The knowledge formalization
component allows the user to formalize parts of the (textual) knowledge.
This usually is done by markups or (semantic) forms. These markups are
extracted and transformed into a target representation which is commonly
stored explicitly (e.g., in RDF) to allow for efficient reasoning. This transfor-
mation implicitly defines the semantics of the formalized knowledge having
the target reasoner and the ontology in mind.
– Reasoning Component: A reasoning component uses the formalized know-
ledge created by the knowledge formalization component and is able to de-
duce higher-level information from it. While most semantic wikis employ an
RDF-reasoner, there are several other reasoning approaches present, that are
beneficial in particular application scenarios.
– Knowledge Presentation Component: This component describes the
method how the additional functionality provided by meta-data and reasoner
is used to supply the user with the right (high-level) information in a suitable
form. This includes as an important aspect the visualization of the results
or functionality. The reasoning capabilities can be used to provide semantic
navigation, querying, rendering fact sheets, meta-data browser, and more.
These components together provide the additional value of a semantic wiki. In
the following the possibilities of extending each of these components are discussed
in more detail.
2.1 Dimensions of Semantic Wiki Extensions
As mentioned in Section 1 the possible extensions of semantic wikis are manifold
and cannot be foreseen in general. In order to allow for many kinds of extensions
we discuss each of the three components separately:
1. Formalization extension: Given any methods (e.g., markup) to insert
atomic formal relations, in a technical point of view any knowledge base can
be created. However, the widespread employment of semantic technology is
hindered by the formalization task being not simple and efficient enough
(Knowledge Acquisition Bottleneck [10]). One way to counteract is lowering
the barriers of knowledge formalization. The development of (domain spe-
cific) high-level markup languages with comfortable editing support can help
to make knowledge definition compact, transparent and efficient. The use of
controlled language is one approach in this direction [11]. Another possi-
bility for reducing the workload of the domain specialists is the integration
of (preconfigured) text mining methods, that propose formalizations based
on the informal text content. Thus, the users only have to decide whether
to confirm or dismiss a formalization proposition.
2. Reasoning extension: Although basic reasoning engines are currently avail-
able there are still challenges with respect to scalability and expressive-
ness [12] to be addressed. Further, there is ongoing research to cope with
inconsistent knowledge, incompleteness and uncertainty [13–15]. For some
applications it will be valuable to replace or enhance the basic reasoning
engine by an early prototype result from such research work.
3. Presentation extension: The challenge of these kinds of extensions is to
present the user the right high-level information in the right form at the
right time (without overflowing him). These extensions must be specified
according to the use-cases addressed by the intended application. One fre-
quent application might be precompiled (possibly parameterized) use-case
specific queries decorated by a GUI component for execution and having a
visualization component attached for result presentation (e.g., table-based,
graph-based, highlighted).
When designing an extensible semantic wiki architecture these three levels
of extension need to be considered as shown in Figure 2.
Fig. 2. Sketch of the “knowledge pipeline” of a semantic wiki with extensions.
Examining the possibilities of the extensions of each level it becomes evident,
that an extension on one component can extend the entire functionality of a se-
mantic wiki system. Thus, the three components can be extended separately or
combined. This results in the semantic wiki extension space sketched in Figure 3.
Assuming that the core semantic wiki system itself already provides some func-
tionality in each component/dimension extensions on the three dimensions can
separately or combined contribute to the total functionality of the semantic wiki.
Hence, an extensible semantic wiki architecture should allow for (independent)
extension of these three dimensions. If the core functionality of the extensible
semantic wiki nearly fits the requirements single dimensions can be extended
denoting “refining” extensions. Heavy-weight extensions along all three dimen-
sions might have their own language, reasoning and presentation functionality.
To clarify the threefold distinction in a more practical context we present three
extensions along three, two and one dimensions respectively in Section 3.
2.2 Decorating Semantic Wikis
As already mentioned in the introduction we claim, that a semantic wiki system
should be precisely tailored to a semantic wiki application considering the do-
main, community, and use-cases. This method is in compliance with the ideas
presented recently by Yaron Koren at the semantic wiki mini series1 . One can
assume that there are many domains where semantic wiki technology could be
employed beneficially. One must not assume that every possible user in any do-
main is able and willing to get used to concepts like Semantic Wiki, ontology,
1
http://ontolog.cim3.net/cgi-bin/wiki.pl?ConferenceCall 2008 12 11
Fig. 3. The semantic wiki extension space.
RDF-triple, SPARQL or DL-Reasoning. Nonetheless it is possible to create se-
mantic wikis that allow for efficient knowledge sharing and use for these user
groups at the cost of some customization. The intended use cases and the user’s
mental model of the knowledge need to be identified in advance. Then, the on-
tology capturing the knowledge that is necessary to support the use cases can be
modeled. Further, a method for knowledge formalization (e.g., user and domain
specific markup) is designed that fits the mental model of the users. This markup
or editing component implicitly creates (possibly complex) RDF-structures. At
last an extension of the knowledge presentation component is implemented. This
extension exactly supports the use cases revealed be the requirements analysis.
It executes the necessary calls of the reasoning engine and presents the result in
a visualization matching to the mental model of the user. One example might
be some buttons with underlying predefined SPARQL queries with result sets
rendered in some (domain specific) visualization. Any technical details (e.g.,
property names, creation of triples, SPARQL queries) are completely hidden
from the user. The typical extension pattern for decorated semantic wikis is
shown in Figure 4.
Fig. 4. Extension pattern of a “decorated” semantic wiki application.
The core functionality for knowledge formalization and knowledge presen-
tation should be hidden from the untrained user to reduce confusion. This ap-
proach has been implemented on the HermesWiki project that is described in
more detail in Section 3.
2.3 Challenges towards an Extensible Semantic Wiki
In the following we discuss the three most important aspects when designing an
extensible semantic wiki:
1. Basic functionality: In order to allow powerful extensions with advanced
features with little implementation costs, it is necessary to decide what se-
mantic core-functionality comes along with the basic semantic wiki architec-
ture. Enabling applications that have to deal with large data sets and high
user activity, requires a slim and scalable text-processing and reasoning en-
gine. Reasoners that focus on processing of more expressive or inconsistent
knowledge are often consuming more computational power and need to be
included by an extension if necessary.
2. Usability: One of the most important reasons for the wide acceptance and
success of wikis is their high usability and the low training costs for new users.
Semantic wikis are bringing new possibilities to wikis and thus are inevitably
adding some complexity to its usage. Adding these new functionalities to the
Wiki interface with the least mental overload is a critical issue in semantic
wiki design. The core strength of the Wiki approach being simple otherwise
can easily vanish. In every case it is sensible to enable ’non-semantic’ users
to work like in a ’non-semantic’ wiki with no adaptation allowing them to
discover single additional functionalities step by step. We propose the idea
of a ’shadow’-semantic wiki hiding all of its advanced functionality at the
beginning. Advanced features (e.g., fact sheets, meta-data browsers) can be
added to pages using tags or configuration settings by more experienced
users when necessary.
3. Extension mechanism: Various extension mechanisms on the software en-
gineering level are applicable to create feature-rich and flexible software.
However, there are several challenges specific to the context of semantic wiki
functionality. The mechanism should be able to support light-weight “refin-
ing” extensions in a very simple way and at the same time still allow for
complex extensions (e.g., with own markup, meta-data representation, rea-
soners and result visualization). In general, an unpleasant issue in modular
software engineering in general is the (programming-)language barrier. For
technical reasons combinations of software components written in different
programming languages are often insufficiently manageable and inefficient.
Unfortunately, this poses some kind of barrier for employing various imple-
mentations of semantic technologies to an extensible semantic wiki architec-
ture.
The best solutions to all these aspects cannot be stated straight forward con-
taining several trade-offs that might have to be revised after future experiences.
3 The Extensible Semantic Wiki KnowWE
We have designed and implemented the extensible semantic wiki KnowWE (Know-
ledge Wiki Environment) aiming to the concept of extensibility as stated in
Section 2. As many software components on NLP and reasoning engines are im-
plemented in Java, it appears helpful having our system also implemented in this
language. We especially focused on the simple definition of new markups and
tools allowing for easy text-based refactoring of these. In this chapter we intro-
duce the core functionality of KnowWE. Additionally, three existing extensions
are presented to demonstrate our approach.
Currently, KnowWE runs on top of JSPWiki2 , but it can be easily used
with any (java-based) wiki engine having an extension mechanism similar to
JSPWiki’s PageFilter concept. Only the wiki connector providing page- and
user-management has to be reimplemented accordingly.
3.1 The KnowWE Core
In KnowWE each article content is held in a tree-based data-structure. This tree
can be used for refactoring, rendering (e.g., personalization, syntax highlighting),
navigation, external editors and also allows the mapping between the textual
content and the extracted meta-data. Arbitrary semantic extensions along any
dimensions can be created without touching the core components of KnowWE.
For any declared formalization extension (e.g., custom markups) the parse-tree
is generated and hang up into the article content tree.
To provide a flexible library of reusable components, we integrated tools
that are valuable for a wide range of possible scenarios managing formalized
knowledge in a Wiki:
– A semantic renaming tool and an annotation browser which is demonstrated
Section 3.4
– Table editing functionality allowing for visual editing of structured data.
– A flexible include mechanism for reusing arbitrary page snipplets across wiki
pages for modular content and knowledge management.
– Parsing components for table-based, line-based, xml-based and regex-based
markups to simplify the declarative definition of markup without or low
efforts on parsing functionality.
Without any extensions, KnowWE comes with a set of basic functionality
comprising the general features of a semantic wiki. To include formalized knowl-
edge the KnowWE core version provides simple markup and the possibility to
import knowledge from external sources. New properties can also be introduced
ad-hoc to the system by the use of property definition sections on every wiki
page. The properties defined within these tags are not created as standard OWL
properties but rather as N-ary relations3 . This allows us to automatically add
2
http://www.jspwiki.org
3
http://www.w3.org/TR/swbp-n-aryRelations/
supplementary information to the created knowledge. One of the automatically
added nodes is a TextOrigin node that contains a reference to the textual position
within the wiki text where the annotation was made, as well as revision informa-
tion of the annotation’s creation. There are several predefined properties which
can be used to create annotations. Those key properties like subClassOf,type
and subPropertyOf are treated separately from the user created properties.
They are imported into the wiki-knowledge as their RDFS counterparts, allowing
the reasoner to work on the generated knowledge without further translations.
An annotation is created by a simple link-like markup syntax. In its basic
form an annotation is similar to the markup used in Semantic MediaWiki [2]. The
following example is taken from a consumer domain describing digital cameras
and shows the annotation of the concept associated with the local page by the
property hasBrand with the value Canon.
Canon released the new 50D [hasBrand::Canon] a while ago.
In this form the subject of the annotation’s property is the default concept
of the page. Beside the double square brackets the differences to a Semantic
MediaWiki annotation is that the annotation in this form has no explicit textual
representation in the page view. Thus there is no Link to Canon created in the
text but the formal knowledge is attached to the preceding word in this case.
The markups can be extended by optional components like a subject different
from the current page concept. Another way of modifying an annotation is by
including a specific piece of text to be annotated. The following example shows
the annotation of the text phrase “new camera” by a formal relation connecting
the camera instance Canon EOS 50D and the brand instance Canon by the
hasBrand relation.
Canon released the [new camera <=> Canon EOS 50D hasBrand::Canon]
a while ago.
This additional syntax provides more flexibility compared to the standard an-
notation. It allows to define relations outside the wiki page representing the in-
stance. Although recommended in KnowWE, it is technically not necessary that
every concept has its own wiki page. Further, it allows for precise attachment of
a formal relation to a text phrase for documentation. This also enables queries to
find all text phrases that for example were annotated with a hasBrand relation.
KnowWE also provides a basic fact sheet markup to define multiple relations in
a compact manner.
Our wiki uses the Sesame RDF storage4 for saving and querying the created
knowledge. The reasoning capabilities of our system are provided by the OWLIM
engine (version 3.0b9)5 . The default setting on the reasoning level is owl-max
providing full RDFS reasoning as well as OWL-Lite semantics.
4
http://www.openrdf.org
5
see http://www.ontotext.com/owlim/
The reasoning and the N-ary representation of properties provide the back-
ground to answering SPARQL queries on the wiki ontology and imported ontolo-
gies. A SPARQL query is embedded into a wiki page using the simple XML-tag
sparql. The query itself is being processed by the sesame query engine and the
results are rendered in a table on the wikipage. For example, the following rather
simple query produces a list of all concepts, in this case all cameras which are
produced by Canon.
partial product list described in this wiki:
SELECT ?cam
WHERE {
?t rdf:subject ?cam .
?t rdf:predicate lns:hasBrand .
?t rdf:object lns:Canon .
} LIMIT 5
The abbreviation lns is replaced by the local namespace which is constructed
from the url of the wiki installation. The results are rendered as links to the pages
where they are defined as shown in Figure 5. Omitting the render attribute
creates a simple table of the Canon products.
Fig. 5. The result of a simple query for Canon products.
3.2 The d3web Extension
The d3web extension is a KnowWE extension to enable the definition and use
of classification knowledge. We outline this extension only briefly since it has
previously been described in detail [9].
– Knowledge formalization extensions: Different markup languages have
been developed to capture various types of classification knowledge, e.g.,
covering models, rules, decision trees [16]. Here, the KnowWE architecture
allows for the integration of context sensitive editing support and syntax-
highlighting. The textual markup is compiled into two different representa-
tions. First it is transformed into the proprietary object structure that is
used by the integrated d3web6 reasoning engine. To exploit querying capa-
bilities, it additionally is compiled into an OWL-ontology using an upper
ontology for classification which is explained more detailed in [17].
– Reasoning extensions: The d3web project summarizes a set of reasoners
for diagnostic problem-solving. The reasoning engine is employed in this
extension to work on explicit knowledge created by the use of the markup
languages described above.
– Knowledge presentation extensions: Beside visualization and browsing
mechanisms for the formalized problem-solving knowledge several possibili-
ties for the execution of knowledge bases are included. A user can initiate a
problem-solving session either by starting a structured interview or by freely
answering questions that are rendered in the wiki pages. After each entered
answer the currently most appropriate solutions (calculated by the d3web
reasoning engine with the knowledge base) are displayed. Such a problem-
solving session can be considered as an incremental personalized query to a
classification system.
The d3web extension represents a heavy-weight form of a semantic wiki ex-
tension coming along with libraries containing reasoners, parsers and dialog com-
ponents. However, this extension is especially intended for small to medium sized
data sets and small user communities.
3.3 The HermesWiki Extension
The HermesWiki is a KnowWE-based Wiki in the historical domain developed in
German language. It is built in cooperation with historians from the University
of Würzburg. The main purpose of the HermesWiki is to provide an overview on
ancient Greek history for teaching purposes of (undergraduate) students. Addi-
tionally, the Wiki provides direct links from the descriptions of historical events
to translations of their (historical) sources. The Wiki consists of three parts: A
collection of about twenty essays giving a comprehensive domain walkthrough,
translations of the describing ancient sources, and an extensive glossary. En-
tries in the glossary are semantically tagged, e.g., as “politician” or “poet”. The
project, started in summer 2008, currently features more than 500 wiki articles,
often illustrated with maps and pictures.
Technically, the wiki implements extensions along the dimension of formaliza-
tion and presentation. The most important formalization extension of the Her-
mesWiki is a specialized markup to explicitly define historical events in the main
essay articles. This markup was developed in cooperation with the historians to
allow for maximum usability in this community. Each historical event is defined
in its own text block, structured as the following example:
6
www.d3web.de
<>
Each event is enclosed with double angle brackets. In the first line the title of
the event (“Lamian War”) is given, followed by a single number in parenthe-
ses, which describes the importance of the event. For example, events with an
importance rating of ’1’ are considered essential while events with a rating of
’3’ are categorized as “additional knowledge”. In the second line of the markup
the point or interval in time when the event occurred is noted in a compressed
form, e.g., the string “323b-322b” points out, that the event occurred from 323
BC until 322 BC. Further annotations can reflect more precise dating as well as
uncertainty. After one empty line a free text description of the event follows. At
the end of each event block ancient sources of the event are mentioned, explicitly
marked by the keyword “SOURCE:” as the first word of a new line. This markup
for historical events is currently used around 600 times in the HermesWiki. A
time event will be modeled in OWL using a small ontology containing a class for
timeline events with the properties as hasImportance, hasTimeDesignation, has-
Description and hasSource. Having this information extracted to OWL allows
for several forms of exploitation in the presentation dimension. HermesWiki can
generate different views on the timeline events by filtering them on constraints
regarding the time, in which an event occurred, on event importance, and on the
article, where an event was defined or on sources occurring. Figure 6 shows an
exemplary generated view on the timeline in the HermesWiki featuring parts of
the conquests of Alexander the Great (in German language). The importance of
the events is color coded. One (domain-specific) use case supported by this ex-
tension is the (semantic) navigation “through the time” using the links provided
by the ordered time line views. This Wiki project demonstrates how a rather
light-weight domain specific extension along the dimensions of formalization and
presentation can yield a “decorated” wiki as described in Section 1 that is usable
by domain specialists which are not familiar with semantic techniques in general.
3.4 A POS-Tagger Extension
To demonstrate the possibilities achieved with low efforts extending KnowWE
we have implemented a small toy-extension to enable Part-Of-Speech-Tagging.
Further, it serves as a tutorial for the KnowWE extension mechanism. To point
out the simple integration of NLP-tools and libraries we employed the Stanford
Fig. 6. A generated timeline in the HermesWiki
POS-tagger7 to analyze the wiki content. Figure 7 shows the resulting wiki view
with this extension activated.
In this example configuration the extension calls the POS-tagger and marks
all verbs found as VerbType nodes in the content tree. To show the results in
the wiki a yellow highlighting-renderer is attached to the node type VerbType.
Fig. 7. The view of a wiki article with the POS-Tagging-Demo extension enabled.
This extension, consisting of a few lines of code calling a POS-Tagger library,
enables to use the following tools coming along with KnowWE:
7
http://nlp.stanford.edu/software/tagger.shtml
The annotation browser This tool shown in Figure 8 allows for browsing the
wiki by annotation-types. For each annotation a link to the occurrence in the
wiki page is provided. The column ancestors shows the types on the path of the
content tree depicting the semantic context of the finding, which is of course
TaggingDemo in this example. In this verb-tagging scenario this could be used
to review the POS-tagging results manually. The browser can also be extended
to support a two-step semi-automated workflow, where users can confirm or
dismiss annotations to create verified meta-data. In general, this tool can serve
as a statistical overview on formalized content.
Fig. 8. Annotation browser listing all existing annotations in the wiki for a selected
type
The semantic renaming tool The semantic annotation given to words by
the POS-Tagger can be used for text refactoring. The semantic renaming tool
is basically a standard global search and replace tool but additionally enabling
semantic type-filtering. Thus, we can globally rename a string, but only if the
string has a certain “role” in a particular markup, that is VerbType in this exam-
ple. One use case which is enabled by this simple single-dimensional extension
(Formalization) is that one could start some disambiguation efforts on the given
content (e.g., for further processing by other NLP-techniques). Since this article
is about crop fields we might like to rename the verb “crop” to a synonym like
“cut” or “truncate” ensuring no confusion between the verb “crop” and the noun
“crop” can arise. The renaming tool allows for sorting and filtering the replace-
ments of the searched string by annotation-type and by articles. One can search
for the string “crop” and all occurrences in the wiki are listed with annotation-
context. Thus, one can select to replace all occurrences that are annotated as
VerbType in a subset of Wiki articles. In this way all nouns of “crop” stay un-
touched - this of course only works assuming that the POS-Tagger successfully
separated the verbs from the nouns.
This semantic renaming tool working on all installed formalization extensions
of a KnowWE system forms a basic refactoring tool for knowledge at different
degrees of formality tagged by various formalization techniques.
4 Discussion
In this paper we motivated and discussed the concept of an extensible semantic
wiki architecture. As an example implementation we introduced our extensi-
ble semantic wiki KnowWE and presented some of its current extensions. In
our work we focus on supporting complex markups and the interconnection
of formalized and textual content. The support of refactoring methods on the
semi-formalized contents is the subject of our current research. The probably
most popular semantic wiki Semantic MediaWiki shows numerous extensions
on formalization and presentation. It is employing the “Decoration” pattern in
different applications by the use of extensions like semantic templates, semantic
forms and semantic queries. We think, that project-oriented customization of
the tools towards the needs of the targeted domain and user community will
become a more and more important challenge in the future. To demonstrate
and evaluate our approach on this task we presented some case studies. Beside
the HermesWiki project we are also applying this approach to the BIOLOG 8
project in order to optimally support the management of biological knowledge
collected there. In this project we plan to use KnowWE to develop methods for
semi-automated knowledge formalization using the agile employment of ontol-
ogy learning methods. Considered from the other side, for researchers developing
such ontology learning methods semantic wikis are attractive as an evaluation
platform if simple integration of these methods is possible. We hope, that ex-
tensibility in general will reduce the setup-costs of semantic wiki solutions and
therefore help to further establish this technology.
We will drive the KnowWE implementation towards even more flexibility
and stability. For the latest news we refer to the project page of KnowWE on
sourceforge9 .
References
1. Schaffert, S., Gruber, A., Westenthaler, R.: A semantic wiki for collaborative
knowledge formation. In: Proceedings of SEMANTICS 2005 Conference, Trauner
Verlag (2006)
2. Krötzsch, M., Vrandecić, D., Völkel, M.: Semantic MediaWiki. In: ISWC’06:
Proceedings of the 5th International Semantic Web Conference, LNAI 4273, Berlin,
Springer (2006) 935–942
8
www.biolog-europe.org
9
http://sourceforge.net/projects/knowwe/
3. Schaffert, S.: IkeWiki: A semantic wiki for collaborative knowledge management.
In: STICA’06: 1st International Workshop on Semantic Technologies in Collabo-
rative Applications, Manchester, UK (2006)
4. Buffa, M., Gandon, F., Ereteo, G., Sander, P., Faron, C.: : A semantic wiki. Web
Semantics 8(1) (2008) 84–97
5. Auer, S., Dietzold, S., Riechert, T.: OntoWiki – A Tool for Social, Semantic
Collaboration. In: ISWC’06: Proceedings of the 5th International Semantic Web
Conference, Berlin, Springer (2006) 736–749
6. Kuhn, T.: Combining Semantic Wikis and Controlled Natural Language. In Bizer,
C., Joshi, A., eds.: Proceedings of the Poster and Demonstration Session at the
7th International Semantic Web Conference (ISWC2008). Volume 401., CEUR
Workshop Proceedings (2008)
7. Lange, C., Kohlhase, M.: A semantic wiki for mathematical knowledge man-
agement. In Völkel, M., Schaffert, S., eds.: Proceedings of the First Workshop
on Semantic Wikis – From Wiki To Semantics. Workshop on Semantic Wikis,
ESWC2006 (June 2006)
8. Nalepa, G.J., Wojnicki, I.: Proposal of a prolog-based knowledge wiki. In Nalepa,
G.J., Baumeister, J., eds.: KESE. Volume 425 of CEUR Workshop Proceedings.,
CEUR-WS.org (2008)
9. Baumeister, J., Puppe, F.: Web-based Knowledge Engineering using Knowledge
Wikis. In: Proceedings of Symbiotic Relationships between Semantic Web and
Knowledge Engineering (AAAI 2008 Spring Symposium). (2008)
10. Wagner, C.: Breaking the knowledge acquisition bottleneck through conversational
knowledge management. Information Resources Management Journal 19(1) (2006)
70–83
11. Kaljurand, K.: ACE View — an ontology and rule editor based on Attempto
Controlled English. In: 5th OWL Experiences and Directions Workshop (OWLED
2008), Karlsruhe, Germany (26–27 October 2008)
12. Krötzsch, M., Schaffert, S., Vrandecic, D.: Reasoning in semantic wikis. In: Rea-
soning Web. (2007) 310–329
13. Huang, Z., van Harmelen, F., ten Teije, A.: Reasoning with inconsistent ontolo-
gies. In: Proceedings of the Nineteenth International Joint Conference on Artificial
Intelligence (IJCAI’05), Edinburgh, Scotland (August 2005)
14. Ma, Y., Hitzler, P., Lin, Z.: Algorithms for paraconsistent reasoning with owl. In
Franconi, E., Kifer, M., May, W., eds.: The Semantic Web: Research and Appli-
cations. Proceedings of the 4th European Semantic Web Conference, ESWC2007,
Innsbruck, Austria, June 2007. Volume 4519 of Lecture Notes in Computer Sci-
ence., Springer (JUN 2007) 399–413
15. Klinov, P., Parsia, B.: Pronto: Probabilistic ontological modeling in the semantic
web. In: Proceedings of the Poster and Demonstration Session at the 5th Euro-
pean Semantic Web Conference (ESWC2008). Volume 401 of CEUR Workshop
Proceedings., CEUR-WS.org (2008)
16. Baumeister, J., Reutelshoefer, J., Puppe, F.: Markups for Knowledge Wikis. In:
SAAKM’07: Proceedings of the Semantic Authoring, Annotation and Knowledge
Markup Workshop, Whistler, Canada (2007) 7–14
17. Reutelshoefer, J., Baumeister, J., Puppe, F.: Ad-hoc knowledge engineering with
semantic knowledge wikis. In: SemWiki’08: Proceedings of 3rd Semantic Wiki
workshop - The Wiki Way of Semantics (CEUR Proceedings 360). (2008)