Semantic wikis can improve navigation and search.an OWL schema captures
concepts such as wiki word, wiki page, forward and backward
link, author, etc. We extended
to support semantic annotation following the “social tagging”:
when editing a page, the user can freely enter some keywords and
an auto-completion mechanism proposes existing keywords by
issuing queries to identify existing concepts with compatible
labels. Thus tagging is both easy (keyword-like) and motivating
(real time display of the number of related pages) and concepts
are collected as in folksonomies. To maintain and reengineer the
folksonomy, we reused a web-based editor available in the
underlying semantic web server to edit semantic web ontologies
and annotations.
(((We wanted our wiki to:
• rely on web standards: standards for the wiki page format
(XHTML), for the macros one can put in a page (JSPX/XML
tags), etc.;
• be articulated around a semantic engine that supports
semantic web languages like RDF, RDFS, OWL, SPARQL,
etc.;
propose faceted navigation and enhanced search tools;
• propose metadata editing in the same user interface used for
content editing.))
Ontologies
we
introduced two ontologies:
• an ontology of the wiki structure: the wiki concepts are
usually buried in their ad hoc implementations; this structure
is a special kind of meta-data (forward links, authors,
keywords, etc.) relying on an ontology of wikis (WikiPage,
WikiWord, WikiWeb, etc.). By making this structure and its
ontology explicit, we can reason on it (e.g. to generate
navigation pages) we can modify it (e.g. re-engineer the wiki
structure) and we can build on it (e.g. interoperability between
several wikis).
• an ontology of the topics: each wiki page addresses one or
more topics. In order to ease navigation while maintaining the
usual simplicity, we implemented the usual tag/keyword
mechanism with a domain ontology shared by the whole wiki.
By making this topic ontology explicit we can once again
reason on it (e.g. find semantically close topics) make
complex queries (e.g. find pages tagged with close topics), we
can modify it (e.g. tidy the ontology, merge equivalent
concepts, etc.)
RDF
Objects in the Encyclopedia of Law already have URIs (identifiers) in form of their URL. Statements in RDF can then be expressed as triples of URIs: the first URI denotes the Subject (\Sean
Connery”), the second gives the Predicate (\is actor in”), and the third specifies
the object (\Goldnger (movie)”).
RDF Schema is introduced as a simple ontology language on top of RDF. The RDF Schema type
system knows classes for typing entities, and properties for typing relations between
entities. Classes can be compared to Wikipedia’s categories: they describe
collections of objects and can be organized in a hierarchy.
For the sake of maintenance, concision,
and encapsulation it is often better to add RDF to a document
without repeating the document’s existing data. RDFa proposes a
solution to augment existing markup with metadata, using class
and property types defined in RDF Schemas, combined with the
existing content from the host language.
In XHTML using RDFa, a subject is indicated using the attribute
about and predicates are represented using one of the attributes
property, rel, or rev. Objects which are URI-referenced are
represented using the attribute href, whilst objects that are
literals are represented either with the attribute content, or the
content of the element annotated.
The following example shows how a tag is augmented with
the attribute rel to annotate a blockquote with the URL of its
source according to the Dublin Core ontology:
As defined in
the wikipedia pages, semantics is the study
of meaning but it is distinguished from
ontology
in being about the use of a word more than the
nature of the entity referenced by the word.
Typed links
Hyperlinks between articles are a central feature of any online encyclopedia.
We propose to add typed links that are to hyperlinks what categories are to articles. For example, there could be a type \is main actor of” which is used for links between movies and their
main actors. Such types can be generated by users, much in the same way that
new categories are created for articles. Other suggestive examples of such types
could be “has capital,” is mother of,” or “is year of birth.”
As with categories and articles, giving types for links of course is not mandatory.
In fact, there are many cases where there is no suitable type that characterizes
the relationship specied by a link between two articles. Typed links are powerful but one has to remember each concept,
relation, property before typing it.
As mentioned above, link types are to be administrated like categories. This
includes the possibility to specify a hierarchy of link types that denes whether
some relationships are more general than others. For example “is actor in” is
more general than \is main actor of”: every main actor is also an actor. The
possibility to dene such relationships is crucial for the usage of link types.
Without it, users looking for all actors either would not nd any main actors or
one would have to specify huge numbers of link types for every link.
Usability
The proposed technology is designed to be simple and completely unintrusive: an author can
completely ignore the availability of link types without any noticeable impact
on her editing.
Experience in ontology modelling shows that the number of required relationships is often rather
small when compared to the number of useful categories.
For example, many geographical entities can be related via links of the type \is
located in” (cities are located in countries, islands are located in oceans, etc.).
Still it is possible to integrate typed links in an even more transparent fashion,
by connecting current templates with link types. The template technology allows
one to insert predened parts of Wikipedia source code into articles, and to ll
certain positions within these templates with customized code.
Doing so, one obtains a way to add link type information without even changing the source code of existing articles.
As a first step, one needs to represent a simple management system for
link types. For a start, it is sucient to adapt the current functionality
of Wikipedia’s category system. Note that, unlike categories, link types do
usually not show up explicitly on an article page, so there has to be some
way of accessing their pages. One feasible possibility for this is to create a
simple search or listing function with the type management system.
Next, the denite syntax for typing links within articles needs to be fixed
and implemented. Extracting the according information from source code
should be fairly simple, but one still has to decide whether there should be an
additional internal representation for link types, or whether the OWL export
suces. We remark that the link information is rather independent of the
article. Especially, types are not associated with any particular occurrence
of a link to another article, but describe the general relationship between the
two articles. Thus duplicate connections of the same type can be eliminated.
Furthermore, one again should include a way to access the pages where the
link types that occur in an article have been dened. This can be done in a
way that is similar to the solution for templates, where used templates are
listed at the bottom of the edit page.
Implementation
The additional typing information will be composed of two parts: the general definition of the available types with their hierarchical organization and the concrete instances of these typed links between articles.
Furthermore, all of this data changes continuously. Considering the fact that
processing of a single query will usually require all the information about types
and links, and possibly even about the whole category schema6, one may wonder
whether our proposal is realistic at all.
We argue that it is, because the linking information { although it deals with
connections between articles { is completely local to the article it is specied in.
In other words, it is possible to process the given link types each time that an
article is edited and to attach this information to the article. Changes in other
articles will not aect the validity of the extracted statements.
Luckily, systems that can work on the emerging structure
of typed links (relations), categories (classes), and articles (instances) exist and
have been subject to intense research in recent years. It is thus desirable to provide
Wikipedia’s structural information in an output format that is understood
by available applications: OWL/RDF.
Therefore Wikipedia could utilize numerous powerful applications (many of
them freely available online) by just providing OWL/RDF output. Thus, one can simply combine
the OWL/RDF fragment for each article in an arbitrary order to obtain a valid
knowledge base that can be fed into existing tools. Similarly, it is straightforward
to automatically generate OWL/RDF specications that express the current categories
with their hierarchy and the classied articles.
A way for the user to pose queries
Doing this in an OWL-based query language or in SPARQL
is possible, but most users would not be able to specify their queries in
this language. Another tempting possibility is to allow links within articles
to point to query results instead of articles. For example, one can easily
provide a link to the current list of all movies an actor ever participated in,
without having to write (or update) this list. Many highly specialized lists
and categories could be simplified in this way. The queries in this case are
only given within the link, such that most users do not have to edit them.
Further ways to pose queries in a more user-friendly way include providing
a simplified query language (e.g. a kind of controlled English) or to include a
language processor for transforming natural language queries to formal ones.
It is also possible to provide an interface for query construction that allows
one to create queries by combining predened expressions.
Systems based on ORAKEL can be used
to oer a natural language query interface to the semantic structures inside
the Wikipedia. Users can ask questions like “What are the ten biggest cities in
Nigeria that don’t lie at a river?” and the ORAKEL system translates this into
queries to the underlying inference engine working on the Wikipedia data. The
availability of machine readable information will greatly enhance the capabilities
of such an approach, since the task is reduced to correct query generation (in
contrast to systems like Internet search engines where query answering itself is
also a challenge).
Alternatively, one can implement systems that assist the users in creating
queries based on predened schemas. To this end, it is helpful that the underlying
semantics of OWL allows to combine queries with logical operators.
Especially, one can rene queries by conjunctively adding additional conditions,
such that even a simple query generation interface could be quite powerful as a
searching and answering tool. The possibility of providing a simpler interface to
create queries directly might also involve a simplied query language that can
be translated to OWL-queries automatically.
Other useful properties
“First of all, the constraint of having links only between articles may be too
strict at some point. For example, when looking for all actors with \Sean” as
a rst name, one would not be able to rely on article-article links, since there
are no articles for rst names linked to from the actors articles. However, OWL
supports datatypes and can express relations between instances (articles) and
strings or integers. Likewise, it would be possible to find a convenient syntax
for expressing such relations in Wikipedia’s source code, e.g. by allowing users
to select pieces of text to be interpreted in this way (similar to the hyperlink
syntax, but without any efect on the compiled article). Another advantage is
that datatypes come with additional background information. For example, if
the size of a person is specied as an integer, it is possible to search for all persons
that are smaller than this size. If the size is just an article name, the machine
will usually not be able to interpret the “smaller than” relation appropriately.
However, it still has to be considered whether such relationships are useful in
Wikipedia.”
Next, let us consider the following example: the geographical location of a
city can be dened via a link of type \is located in” like e.g. Karlsruhe is located
in Germany. On the other hand, Germany is located in Europe and, considering
this, one might also want to infer that Karlsruhe is located in Europe. Using
OWL, it is possible to do this during query answering, without having to give
additional \is located in” links in the article on Karlsruhe. One just has to specify
that the property of being located in something is transitive, i.e. that if A is
located in B and B is located in C, then A is located in C. This advanced feature
clearly is of some importance for Wikipedia, since similar transitive relations like
\is a relative of” or \is ingredient in” are common to many application domains.
Another advantage of this is that most users will not have to decide whether a
relation is transitive or not; they can just use the types as usual and possibly
wonder how Wikipedia knows that Karlsruhe is in Europe even if this is written
nowhere in the article. Transitivity of a particular link type can be switched on
or o at any time and can of course be ignored by any application that does not
support this feature of OWL (leading to some fewer results).
Another possible situation is that two types of relations are inverse to each
other. For example, there might be link types \is capital of” (from countries to
cities) and \has as capital” (from cities to countries). Clearly, these two types
are just inverse, so specifying one is sucient to know about the other. Again
this can be expressed in OWL and it is easy to imagine an interface on the type
page to edit the property.
In addition, OWL also supports the specication of axioms for describing
the domain and range of each relation. For example, one may want to require
that every article that has an outgoing link of type \is capital of” belongs to
the category \Country.” Such constraints should never disallow an editing step,
but they may serve to detect problematic specications at some later stage. In
fact, due to the aforementioned locality principle that ensures good performance,
domain and range axioms will not even be available when editing an article.
We remark that the classical interpretation of domain and range in OWL is
to change the classication rather than to reject a specication. For example,
if we declare Berlin as the capital of Germany and require that every capital
must belong to the category \City”, then one could infer that Berlin is a city.
So someone who is looking for all cities might get Berlin even if this is not
explicitly specied in the according article. However, whether this is inferred or whether domain and range is just ignored for querying is the choice of the
concrete implementation.
Finally, OWL does also support statements that constraint the number of
things that are allowed to be related via links of a given type. For instance, one
may want to require that every country has exactly one capital specied. Again,
the usage of such additional information is left to the software working on the
exported OWL specification.
Benefits
The specification of range and domain categories for typed links can be used to
suggest categorization to contributors. For example, the editing system could
warn that articles about someone who was an actor in a movie should also
belong to the category of actors. Such warnings must never prevent the user
from editing as he wishes.
A further advantage is that ontological information, in contrast to full article
texts, can also be collected and generated very easily in a fully automatic fashion.
For example, robots can include semantic information in Wikipedia articles
based on data that is freely provided by other websites, such as online libraries,
shops, movie databases, or scientic repositories. This is the case, for example, of a capability added to Semantic MediaWiki, through an extension called ExternalData. Furthermore, given that the
interlanguage links within Wikipedia are present, such information can readily
be included in encyclopaedias of any language.
Due to the usage of a standard ontology format one can also make use of
existing Semantic Web applications, e.g. for visualization of content: tools like
Aduna or the KAON OI Modeler [http://aduna.biz] are able to visualize the relationships
between articles, and even to oer a user interface based on graph visualization.
Lists could be automatically generated from the infoboxes of other pages. It would also be possible to generate maps, using the location coordinates that feature on some pages, or automatically generate timelines to summarize periods in history covered by many other entries.
Social Tagging
Wikis are designed to be structured by the users
themselves. People differ from each other, every individual has
his own way of classifying and organizing data, and this may
change over time. A hierarchical structure like the one proposed
by the workspaces is certainly a good thing from a technical point
of view but it provides a superficial modularization of a wiki [14].
Horizontal navigation (following links in the page itself) is the
means most people use. Usability tests showed that most people donÂ’t even know the names of the different categories.
You can annotate your entries in the Encyclopedia by associating keywords to them
forming a quasi-classification on-the-fly. These tags are used by
technorati.comÂ’s web bots and a link to your tagged resource is
added to the other entries that share the same tag. The main
interest in this way of tagging is its social approach to
classification. People can use whatever tags they feel represent
the content of their writing, but they may find out that this tag has
never been used before. So there is a higher probability they will
add other tags that link them to other resources. If one creates a
new tag, it is just added and will be proposed as a choice when
another person enters a tag that starts with the same letters, and
maybe this person will in turn choose it. This way, users as
individuals, can categorize their writing any way they want and at
the same time begin a grass roots taxonomy or folksonomy.
Social tagging and folksonomies are the subjects of debate in
different communities, including the semantic web community
[12]. These concepts are often described as an alternative to
ontologies and to the semantic web approach in general [11, 15].
Gruber in [15] published an interesting survey of these different
points of view. Some describe tags and folksonomies as “cheap
metadata for the masses” (taxonomies and ontologies being the
land of experts) [33] and others think they are the one true way
[11] and that a flat-hierarchy is more human-brain-friendly,
imitating the word-as-a-label-for-things. But this is also the main
drawback of the tags: human-language-structured thought can
jump between concepts; the same word can have totally different
meanings. Last but not least: each human has his own worldexperience,
his own tagging-system that may not be generalized.
Where categories are managed by specialists to achieve the best
classification, tags are usersÂ’ rough approximation of
classification for a practical use (ethnoclassification).
Like [13 and 14], we propose a mixed approach in order to
“organize the tags”: we link the tags together within a folksonomy
described using the semantic web languages, where tags are
organized in a hierarchy and related one to another using
relationships like subClassOf, seeAlso, etc. Grubert goes
further and proposed in [15] to define “an Internet ecology” for
folksonomies i.e. an ontology for describing folksonomies. Like
him, we believe that social tagging minimizes cost and maximizes
user participation. So we do support social tagging in SweetWiki,
but we also think that these tags must be organized.
Furthermore, related categories are also displayed in order to
address the ambiguity of homonymy. With this approach, tagging
remains easy (keyword-like) and becomes both motivating and
unambiguous. The
feedback coming from the tags is useful for improving the
ontology.
one can add/remove/edit
tags/concepts (Figure 8 and 9). In particular, if a tag/concept has
been recently added it may be inserted in the hierarchy. Figure 9
shows the concept editing tool.
Using these editors, the folksonomy and the annotations may be
updated. For instance, community experts can pick a couple of
tags and declare semantic relations between them such as
subClassOf. They may also merge concepts when two tags are
synonymous, etc. Enhancements of the ontology seamlessly
improve content sharing: search and faceted navigation benefit
directly from the updates.
Example: The Keyword list is followed, in each item, by its “Subclass of”
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Wiki and Semantic Web Approaches
The idea of combining the usability and openness of a Wiki with the machinereadable,
extensible and standardized languages from the Semantic Web community
is not new and has been discussed for years.(http://www.c2.com/cgi/wiki?SemanticWikiWikiWeb) An approach close to our
suggestions probably is the “Kendra Base” wiki. (http://www.kendra.org.uk/wiki/wiki.pl?KendraBase, reviewed for WikiData at http://meta.wikimedia.org/wiki/Kendra_evaluation).
The recently developed WikiSAR [1] integrates many of our ideas and shows
the feasibility of combining semantic data entry with rich query facilities. Two
mature semantic Wiki implementations that are well-known in the SemanticWeb
community are Platypus [11] and [13]. However, in contrast to our proposal, both
separate Wiki page data from more complex semantic page metadata.
The idea of integrating machine readable data into Wikipedia has also been
discussed earlier (http://de.wikipedia.org/wiki/Benutzer:Duesentrieb/Semantic_Wiki_Web discusses
the semantic content of categories and the possibility of annotating Wikipedia
with RDF-like relations).
The extraction of semantic (linking) information from the current corpus of Wikipedia is also relevant to our current approach, since it can generate candidates for link types automatically.
First steps in this direction are taken in [7].
Another related eort is the semantic annotation of the Wiktionary projects,
with the goal of allowing wide-reaching interoperability between languages and
inclusion of external knowledge-bases like thesauri [10]. It will be important
to ensure compatibility of the semantic languages chosen for this purpose with
possible semantic extensions of Wikipedia, e.g. by employing the same ontology
language as an underlying formalism.
As requested in “The Wiki Way” [6], we stay true to the free-text entry mode
and thus avoid to create an in-browser ontology-editor.
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