=Paper=
{{Paper
|id=Vol-360/paper-1
|storemode=property
|title=Towards an Interlinked Semantic Wiki Farm
|pdfUrl=https://ceur-ws.org/Vol-360/paper-19.pdf
|volume=Vol-360
|dblpUrl=https://dblp.org/rec/conf/semwiki/PassantL08
}}
==Towards an Interlinked Semantic Wiki Farm==
https://ceur-ws.org/Vol-360/paper-19.pdf
Towards an Interlinked Semantic Wiki Farm
Alexandre Passant1,2 , Philippe Laublet1
1
LaLIC, Université Paris-Sorbonne,
28 rue Serpente,
75006 Paris, France
firstname.lastname@paris4.sorbonne.fr
2
Electricité de France Recherche et Développement,
1 avenue du Géneral de Gaulle,
92141 Clamart Cedex, France
firstname.lastname@edf.fr
Abstract. This paper details the main concepts and the architecture
of UfoWiki, a semantic wiki farm – i.e. a server of wikis – that uses
form-based templates to produce ontology-based knowledge. Moreover,
the system allows different wikis to share and interlink ontology instance
between each other, so that knowledge can be produced by different and
distinct communities in a distributed but collaborative way.
Key words: semantic wikis, wiki farm, linked data, ontology popula-
tion, named graphs, SIOC
1 Introduction
During the last few years, various Web 2.0 services and principles - such as blog-
ging, wikis, social tagging and social networking - gained interest in corporate
environments, leveraging tools that people are more and more used to in their
personal life to the enterprise [1]. On the other hand, Semantic Web [2] technolo-
gies are used in different business information systems to enrich data integration,
querying and browsing, thanks to powerful means to represent knowledge like
ontologies and standards to model or query data as RDF and SPARQL.
While some consider Web 2.0 and Semantic Web as being opposite concepts
with different origins and goals, we believe as others [3] that these two views
should - and even must - be combined to offer easy-to-use but powerful services
to end-users. Thus, information systems should benefit from usability and social
aspects of Web 2.0 and also from data formalisms of the Semantic Web. It
will provide to end users means to collaboratively build, maintain and re-use
ontology-based data, a task often dedicated to knowledge management experts,
especially in organizations.
In this paper we will describe a semantic wiki-farm system, i.e. a wiki server
where communities can setup new wiki instances, called UfoWiki – Unifying
Forms and Ontologies in a Wiki – that aims to achieve this goal, currently in
2 Alexandre Passant, Philippe Laublet
use at EDF R&D3 . The paper is organized as follows. First, we will briefly intro-
duce the limits of classical wikis and various implementations of semantic wikis
designed to enhance wiki features thanks to semantics. Then we will introduce
the features and the architecture of our semantic wiki farm, as well as novelties
compared to current semantic wikis systems, especially the way we combine data
and meta-data to keep some information about the knowledge created from wiki
pages. We will then describe how people can use one wiki to create ontology
instances thanks to form-based templates and emphasize on how the system in-
terlinks data from one wiki to another one but also allows to re-use external
data. We will then show how created data can be reused to provide advanced
features in a single wiki but also for the complete wiki farm.
2 Wikis and Semantic Wikis for Knowledge Management
2.1 Limits of Traditional Wikis
Among the numerous practices and tools that became popular thanks to Web 2.0,
wikis offer new and interesting possibilities regarding collaborative knowledge
management. Pages versioning, open plus non-hierarchical editing, hyperlinks
and back-links provide useful services to gather and build knowledge within
communities and business environments or in open environments as the Web.
Nevertheless, traditional wikis suffer from the difficulty for computers to ex-
ploit and reuse the knowledge they contain. A reader could learn from a wiki
that EDF is a company that produces nuclear energy in France but a software
agent will not be able to easily answer queries like ”Is EDF located in France
?” or ”List all companies known in that wiki” without natural language pro-
cessing algorithms. Indeed, wikis deal with documents and not with machine-
understandable representations of real-world concepts and objects, as a reader
does when browsing or editing a page. So, a wiki will model that ”There are
some hyper-links between a page titled EDF, a page titled France and a page ti-
tled nuclear energy”, but will not be able to deduce anything about the nature of
those different objects and their relationships, since pages do not carry enough
semantics about the knowledge they contain (Fig. 1).
2.2 The Semantic Web and Ontologies for Better Wikis
To bridge this gap between documents and machine-readable knowledge about
real world objects, data must be described in a way software agents interpret
and understand uniformly in order to reuse it efficiently. Ontologies [4] and the
Semantic Web are effective ways to do so, since they provide common data struc-
tures, vocabularies and languages for modeling and querying domains of interest
and related individuals. During the last few years and since the first SemWiki
workshop [6] various semantic wikis prototypes have been built, combining wiki
3
Electricit de France, aka EDF, is the leading energy company in France, its R&D
department involves about 2000 researchers
Towards an Interlinked Semantic Wiki Farm 3
hyperlink is A Company
EDF
France
Knowledge
Documents Real wold
EDF perception Country
(wiki level) produces (human mind level)
gap
located in is A
Nuclear
energy hyperlink Nuclear
energy France
Fig. 1. The gap between documents and real-world knowledge
features and Semantic Web technologies. While tools use different ways to pro-
duce this machine-readable data thanks to efforts of their community of users,
they all share the common goal of providing value-added services from advanced
pages browsing to query answering or even reasoning upon the created dataset.
Systems such as Semantic MediaWiki [7] or SemperWiki [8] require to use a
special wiki syntax or to directly embed RDF in order to add semantic annota-
tions to wiki pages. While this is an open approach in the spirit of wiki principles,
this can lead to semantic heterogeneity problems since any user can use its own
vocabulary to add annotations in a document, making them difficult to re-use.
A system like IkeWiki [9] combines plain-text feature of wikis and a dedicated
triples-based form interface to help users annotating content by re-using existing
ontologies, while OntoWiki [10] can be used as a complete ontology instances
editor, with a user-friendly interface that offers different views and browsing and
editing interfaces over existing data. Yet, most of those systems require users to
have some knowledge about the Semantic Web at a certain time when using it,
since they have to deal with namespaces or URIs. This makes the tools difficult
to use for people that are not aware of such models, as in business environments
where people need to focus on how to use the tools rather than on how he is
being build, i.e. benefit from Semantic Web technologies without having to learn
them.
In these tools, semantic annotations are mainly used to create and maintain
ontology instances and relationships between them, as well as properties, thus
providing a real-world and machine-readable representation of the content de-
scribed inside the pages. They can help to enhance browsing capabilities of the
wiki, by suggesting related pages sharing similar instances or listing all pages
featuring a certain property as does Semantic MediaWiki. Moreover, new ways
to browse the data are available, like in OntoWiki that features map and calen-
dar view of existing data, while some tools provide a back-end RDF store that
4 Alexandre Passant, Philippe Laublet
allows to query data from the whole wiki and embed query results in wiki pages.
Finally, some tools also feature inferencing capabilities in order to deduce new
knowledge from the current state of the wiki and thus enrich user experience
in discovering new knowledge. For example, IkeWIki and OntoWiki can list all
instances of a given type taking into account instances of various subclasses.
Eventually, it seems important to reference DBpedia [12], a project that aims to
represent Wikipedia content in RDF, as well as other semantic wikis, like Sweet-
Wiki [11] which does not focus on ontology population but on using semantic
web technologies to let users tag their pages and organize those tags, focusing
on pages meta-data rather than modeling content of those pages.
3 Modeling a Semantic Wiki Farm
3.1 Main Features of the System
Regarding various aspects of semantic wikis that have been mentioned before, we
created UfoWiki, a new semantic wiki farm system - i.e. a wiki server designed
to setup and host several wikis - based on the following features, that will be
described in the rest of the paper:
– Ontology-based knowledge representation. Data created from wiki pages is
represented in RDF and is based on a set of ontologies defined by adminis-
trators of the wiki in order to avoid semantic heterogeneity problems of data
modeling;
– Usability. In extent of the previous point and in order to let users easily
produce that ontology-based data, we focused on a combination of plain-
text and intuitive forms to edit wiki pages, so that users do neither confront
to a new syntax or to Semantic Web modeling principles;
– Interlinking data While each wiki of the farm acts independent (regarding
users that can access it, topics, and modeled knowledge), the system allows
different wikis to exchange and interlink their data even if they do not share
hyperlinks between each other, thanks to a common knowledge base for the
whole system;
– Modeling both data and meta-data. While our approach mainly focuses on
modeling knowledge contained within wiki pages, we also separately repre-
sent the complete wiki server meta-data (wikis, users, pages, tagging actions
...) in RDF, combined with links between those two distinct levels of repre-
sentation.
– Immediate reuse of formalized data. RDF data created among the wikis must
be immediately reusable to enhance browsing and querying capabilities of
the system, either for a single wiki or the complete farm. Our system uses
inline macros, that can provide semantic back-links in the wiki.
3.2 Global architecture
To achieve these goals, our system involves different components. The first part
of the architecture consists in a set of ontologies that are used to model RDF
Towards an Interlinked Semantic Wiki Farm 5
data from the wikis, whether it is data about the pages or about their content.
For the latter, ontologies must be defined in RDFS or OWL depending on the
needs of the knowledge field of the wiki. Regarding the RDF description of
wiki pages and user actions, we are using the SIOC ontology [13] and its type
module4 , a model to describe social media meta-data with unified semantics.
We also model tags and tagging actions using the Tag Ontology [14] and the
MOAT ontology [15], so that people can give machine-understandable meanings
to their tags, especially using URIs of ontology instances created within other
wiki pages. Since for all wiki page, data and meta-data are produced within
two distinct RDF documents - so that one can export independently each level
of representation - we extended the SIOC ontology with a specific property,
embedsKnowledge in order to formally represent in RDF the link between a wiki
page (described in RDF) and the data embedded in it (Fig. 2). This property
provides a way to link any instance of sioc:Item - and its subclasses - to the
URI of a named graph [16], i.e. in practice the URL of a document that contains
a set of RDF triples.
Then, the system features its web interface to create wikis, manage wiki
forms and browse and edit wiki pages. This interface uses Drupal and is mainly
based on a fork of the flexinode module5 to let wiki owners define their forms.
Each form is related to a given class - e.g. people (related to foaf:Person) or
software project (doap:Project) - and each part of the form (a field or a set of
fields) can be related to an ontology property and also to a given class, which is
used for the autocompletion features of the system. Thus, the editing interface
of each wiki combines plain-text and structured parts in order to easily manage
the creation of RDF statements according to the ontologies it uses, as we will
see on the next section.
The last component of the system is the knowledge base of the wiki farm,
storing all created RDF statements thanks to a triple-store, using the 3store6
API. By storing in real-time all RDF data as well as ontologies in a single place,
it offers querying capabilities for the complete data and meta-data of all the
wikis, but nevertheless keeps a trace of each statement thanks to its named
graphs compliance, so that queries can involve the complete wiki farm data or
only data of a given wiki. This store also manages basic inference capabilities
(subclasses and subproperties) and supports SPARQL [5] and some SPARUL
patterns (SPARQL update7 ) in order to query and update data created from the
wiki pages.Moreover, since all wikis of the wiki farm share the same knowledge
base, by querying and updating a single RDF store, data can be re-used across
wikis. Thus, an ontology instance created in a given wiki can be linked to an
ontology instance from another one, even if there is not direct hyperlink between
the pages that embeds this knowledge. It allows our system to create knowledge
on a distributed way, even between various communities that do not share the
4
http://rdfs.org/sioc/types
5
http://drupal.org/modules/flexinode
6
http://threestore.sf.net
7
http://jena.hpl.hp.com/~afs/SPARQL-Update.html
6 Alexandre Passant, Philippe Laublet
same wiki but that produce information about the same ontology instances (Fig.
4).
edit
User 2
Wiki page
B
Wiki page HTML hyperlink
edit A
User 1
Document layer
(wiki level)
produces
produces produces produces RDF
Store
RDF
RDF meta-data
RDF about page
RDF description
description
meta-data of objects Semantic B
of objects
about page embedded relationships embedded
A in page A between objects in page B
uses semantic link
semantic link Storage
Semantic Web layer uses
Meta-data
ontologies Data-modeling
(SIOC, DC ...) ontologies
(SKOS, Domain
ontologies ...)
Fig. 2. Architecture of one wiki from the wiki-farm
4 Maintaining and interlinking ontology instances
between wikis
4.1 Using forms to create and maintain ontology instances
As most semantic wikis, our system automatically creates one main ontology
instance for each wiki page, based on the page title. While some wikis do not
explicitly assign them a given type and other rely on the page category to define
it, our system uses the class assigned to the page form to define it. Regarding
definition of properties and relationships of each instance, we use a mix of plain-
text and forms in the wiki editing interface, thus separating plain-text content
from content to be modeled in RDF, as the Semantic Forms extension8 for
Semantic Wiki or Freebase9 can do. When creating the page, translation from
wiki content to RDF data is then automatically done thanks to the mappings
defined by wiki administrators between the form and a set of ontologies. We
think that this combination of plain-text and forms to ease the modeling of
RDF data (Fig. 3) has numerous advantages:
8
http://www.mediawiki.org/wiki/Extension:Semantic_Forms
9
http://www.freebase.com
Towards an Interlinked Semantic Wiki Farm 7
– First, as fields are defined by the wiki owner for each type of page and so
for each class, users know what kind of knowledge is relevant for the wiki
regarding a given page and can focus on essential aspects in this context;
– Moreover, as we kept a simple WYSIWYG field for each page, any other
relevant information can be added there. It can also help to participate in
evolution of the model itself when regular patterns appear, even if the model
must be edited manually in this case;
– Users can benefit from autocompletion features, suggesting possible related
instances by querying the RDF store with on-the-fly SPARQL queries, thanks
to AJAX technologies;
– At last, in our system, this approach allows to create complex relationships
and ontology instances inside a single page. While most semantic wikis allow
only to create relationships between existing objects, a form part can cor-
respond to a dedicated class in our system, offering better ways to manage
complex ontologies population. Moreover, in the page meta-data represen-
tation, we distinguish the main instance and the embedded ones, using two
subproperties of sioc:topic we especially created to achieve this distinc-
tion.
Inline macro
Simple
autocomplete
field
Complex
instance field
Fig. 3. Wiki editing interface
While each page corresponds to a given ontology instance, instances are also
created for each filled relationship field where a class has been assigned. Then,
if one later decides to create a wiki page for these instances, properties will be
added to the existing ones. Moreover, when instances are not used anymore in
any wiki, i.e. do not have any property, they are automatically removed from the
8 Alexandre Passant, Philippe Laublet
RDF store to avoid orphan instances. From these aspects, the wiki really acts
as a collaborative ontology population tool, beneficing from Web 2.0 features to
provide this task. An instance can be created by a user, modified by another,
then linked to a third one by another one and even can disappear from the
knowledge base if a fourth user edit the page that contains its only reference
and removes it.
4.2 Interlinking data between wikis
As we saw in the previous section, our system allows various pages of a given
wiki to add information about a single ontology instance. For example, we can
create an instance in a wiki page and add a relationship from another instance
in a different page than the one that creates it. Yet, our system goes further
by allowing two different and disconnected wikis to manage information about
the same instance in a distributed way, but keeping the trace of which wiki -
and which page - helped to create the information. Thanks to the combination of
named graphs and the embedsKnowledge property we introduced before, the wiki
farm can consider either the whole RDF graph, or subgraphs of RDF statements
related to a given wiki only (Fig. 4).
Such a scenario might be useful in some corporate environments, where peo-
ple do not want to allow anyone to access their wiki, but agree on sharing some
expertise and data with others. By exporting only some parts of the wiki page
in RDF (i.e. the instances and properties created from some fields of the form
page), our model allows the webpage itself to be hidden to not-authorized peo-
ple while the RDF statements can be exported and become available to a larger
community. Moreover, due to our technical architecture that uses SPARQL and
SPARUL, the system allows wikis that are distributed on a network (and not
from the same wiki farm) to exchange data and interlink it the same way, in case
they share a single common RDF database. The system currently do not deal
with inconsistency between data from different wikis. We think that this issue
should be dedicated to some reasoning engine, that would check inconsistency
between produced statements thanks to OWL axioms defined in the ontologies.
Moreover, instead of querying the complete knowledge base, queries can be
restricted to data created from a single wiki by using this kind of SPARQL query:
select ?page ?title
where {
graph ?data {
:EDF ?predicate ?object
} .
?page :embedsKnowledge ?data ;
rdf:type sioct:WikiArticle ;
dc:title ?title ;
sioc:has_container .
a sioct:Wiki .
}
Towards an Interlinked Semantic Wiki Farm 9
This process of combining the two levels of representation can also be used
in the autocompletion field, by restricting the autocompletion SPARQL queries
to data created from a single wiki, rather than to the whole RDF statements.
embedsKnowledge
embedsKnowledge
Wiki page A
athena:EDF athena:EDF
Wiki page B
rdf:type
http://sws.geonames.org/ athena:produces
3017382
geonames:locatedIn
rdf:type
sioct:WikiArticle athena:NuclearEnergy
sioct:WikiArticle
Wiki A
Wiki B
stores stores
RDF
Backend
merges
athena:EDF
geonames:locatedIn
athena:produces
http://sws.geonames.org/
3017382 athena:NuclearEnergy
Fig. 4. Interlinking and merging data from different wikis
4.3 Interlinking wiki data with external knowledge
Our system also allows to connect our data to external, publicly available, RDF
data. At the moment, a single plug-in is available, to reuse the GeoNames10
ontology and knowledge base. Each time a form field corresponds to a place and
is assigned to the geonames:Feature class, the system queries the GeoNames
webservice11 to retrieve the URI of the given instance. Thus, the updated local
instances can be linked to external resources, beneficing from a global connection
between our data and efforts of communities that help to build such knowledge
base. Moreover, we not only link to the URI but also crawl the related RDF
file to put in in the wiki knowledge base. Thus, it allow the system to provide
geo-location features to end users, without the need for them to type the exact
location (i.e. latitude and longitude) of each instance (eg: a people or a company),
as they would have done using systems like Semantic MediaWiki and its Semantic
Layers extension12 .
10
http://www.geonames.org
11
http://ws.geonames.org
12
http://s89238293.onlinehome.us/w/index.php?title=Main_Page
10 Alexandre Passant, Philippe Laublet
In the future, we plan to implement new wrappers and linkage systems for
other RDF data, especially ways to link to DBpedia extracted knowledge, which
can help to provide additional information about instances created within the
wikis, and also contribute to the expansion of the Linked Data Web [17]. Re-
garding this latest point, linking to data from references datasets can help RDF
data from our system to be more easily found on the Semantic Web, thanks to
lookup services such a Sindice [18] that help to retrieve all resources using and
linking to a given URI.
5 Using created data
5.1 Inline macros
The main feature to enhance wiki browsing capabilities in our system is the
use of inline macros, similar to inline queries of Semantic Mediawiki. Those
macros are defined by wiki administrators themselves, using SPARQL and PHP
to render the results and are then called by users in wiki pages with simple
hooks. Since all data are based on a set of predefined ontologies, queries can be
written without having to deal with semantic heterogeneity problems, as people
that would have use different property names for the same one, e.g. isLocatedIn
versus has location. The system then runs the query over the RDF store when
the page loads, so that query results are always up-to-date. While queries can
be complex, users simply type function names, with some arguments if needed,
to use it in wiki pages. For example, [onto|members] will be translated in a
query that will retrieve all people that are member of the organization described
in a wiki page (Fig. 3, Fig. 5). Such queries take inference capabilities of the
system into account, so that, for example, if they must list all organizations
instances described in the wiki, they will also lists companies or associations if
they have been defined as subclasses of the first one in the ontology. Finally, the
administrator can decide that the macro will render a link to add new page in
the wiki to create an instance of a given type, thus facilitating the process of
creating new data.
Inline macro
result featuring
new page
creation link
Inline macro
result
Fig. 5. Browsing an enhanced wiki page
Towards an Interlinked Semantic Wiki Farm 11
Moreover, macros can take into account the way we combine modeling of data
and meta-data in RDF export of wiki pages, so that a wiki can display a list
of pages from another wiki for a given query, as the previous SPARQL snippet
showed. It allows one wiki to benefit from the effort of another community done
in another wiki.
More generally, such queries can be seen as a way to move from classical wiki
back-links to semantic back-links, as we bridged the gap between documents and
Semantic Web formalized data. While a typical wiki could list thanks to its back-
link feature that an organization page has an incoming link from a people page,
our system takes advantage of the data formalism to be more specific about the
nature of this link, mentioning that this company employs that person, going
from the document to the data layer.
5.2 Advanced data view
Finally, those macros can display results according other rendering inter-faces,
such as Google Maps, in case the needed geo-location information is available in
the RDF store thanks to the integration of the GeoNames lookup service. Thus,
while the result is similar to what can be done with the map view of OntoWiki,
users do not have to manually enter the coordinates of each instance (e.g. a
company) but simply fill a ”City, (State), Country” field, that will be used to
retrieve the appropriate RDF data - including coordinates - from GeoNames an
add it in our knowledge base. Here, we clearly see the benefit of using the same
model (i.e. the GeoNames ontology) than an existing RDF dataset to include
data from external services at zero-cost. The Fig. 6 displays the output map of
a macro that retrieves the location of a given association and all of its members
from a single wiki interlinked with GeoNames data in a single SPARQL query.
Fig. 6. Map view of the wiki data
12 Alexandre Passant, Philippe Laublet
5.3 Semantic search
Another feature of the system is a dedicated semantic search engine, taking
into account existing instances described within the wiki (or used in a semantic
tagging process) rather than plain-text only when retrieving data. When a user
search for a given term in the wiki farm, the system first finds the list of all
instances related to this label, using (1) rdfs:label that can have be defined
thanks to the wiki pages and dedicated forms and (2) the moat:Tag instances
that contains this term within their label and that are linked to existing instances
thanks to a related moat:Meaning. Thus, if a user type the search term ”France”,
the system will ask the user if he requires information about”EDF” (since it has
”Electricit de France” as a tag) but also, of course, the ”France” concept.
Then, the system will list independently:
– All wiki pages - for each wiki, identified by their name - that have this
instance as a main topic;
– All wiki pages where the instance is an ”alternative” topic (i.e. an instance
created within a page);
– All wiki pages ”tagged” (thanks to MOAT) with this instance.
Thus, it offers various meta-data representation of the wiki.
Fig. 7. Semantic search results example
Moreover, while we do not currently provide user friendly interface to gen-
erate new queries or macros, advanced users can run SPARQL queries over the
RDF data.
Towards an Interlinked Semantic Wiki Farm 13
6 Conclusion and future works
In this paper, we described a prototype of wiki that combine structure and
Semantic Web modeling capabilities to produce ontology-based and machine-
readable data in a collaborative way. We showed how various wikis could be
used to model and interlink knowledge about ontology instances in an open and
distributed way. We finally showed how such knowledge can be used to enrich
functionalities of the wiki. While this system combines some features that already
exist in various prototypes, it focuses on usability for end-users, as well as, from
the technical side, a way to model and link both data and meta-data, offering
capabilities to view different levels of annotation, either from a single wiki or for
the complete set of wikis.
The system is currently in use at EDF R&D, where users have created more
than 200 instances from various lightweight ontologies. We extensively use the
GeoNames integration, making the geo-location feature easy to integrate in order
to provide new and interesting ways to browse the wiki content. Inline macros
are also useful for end-users since they allow to easily find instances and related
wiki pages. For example, we included a macro that lists, in a page dedicated to
some company, all other organizations working on the same topics.
Regarding our future works, we will concentrate on adding new value-added
functionalities to the wiki for end-users to ease the discovery of relevant infor-
mation from the set of RDF data, as faceted browsing, as well as interlinking
with other existing datasets. We will also focus on how to formalize wiki pages
versioning in RDF, in order to see how statements about a given resource can
evolve during its lifetime and track more precisely each change of information
on a given ontology instance.
Acknowledgements
We would like to thanks the ID-Net team from EDF R&D for their input on the
current experiments about our wiki farm.
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