4 research outputs found
Dependency Updates and Reasoning in KiWi
KiWi is a framework for semantic social software applica-
tions that combines the Wiki philosophy with Semantic Web technolo-
gies. Applications based on KiWi can therefore leverage i.a. reasoning
and versioning to follow both aspects and even go beyond existing tech-
nologies. For example, KiWi allows composition of content items, which
poses a challenge to the versioning system. In this paper we discuss ver-
sioning of composed content items and challenges related to reasoning in
collaborative social software, as both topics are concerned with updates
on the application state
Keyword-Based Querying for the Social Semantic Web
Enabling non-experts to publish data on the web is an important
achievement of the social web and one of the primary goals of the social
semantic web. Making the data easily accessible in turn has received only
little attention, which is problematic from the point of view of
incentives: users are likely to be less motivated to participate in the
creation of content if the use of this content is mostly reserved to
experts.
Querying in semantic wikis, for example, is typically realized in terms of
full text search over the textual content and a web query language such as
SPARQL for the annotations. This approach has two shortcomings that limit
the extent to which data can be leveraged by users: combined queries over
content and annotations are not possible, and users either are restricted
to expressing their query intent using simple but vague keyword queries or
have to learn a complex web query language.
The work presented in this dissertation investigates a more suitable form
of querying for semantic wikis that consolidates two seemingly conflicting
characteristics of query languages, ease of use and expressiveness. This
work was carried out in the context of the semantic wiki KiWi, but the
underlying ideas apply more generally to the social semantic and social
web.
We begin by defining a simple modular conceptual model for the KiWi wiki
that enables rich and expressive knowledge representation. A component of
this model are structured tags, an annotation formalism that is simple yet
flexible and expressive, and aims at bridging the gap between atomic tags
and RDF. The viability of the approach is confirmed by a user study, which
finds that structured tags are suitable for quickly annotating evolving
knowledge and are perceived well by the users.
The main contribution of this dissertation is the design and
implementation of KWQL, a query language for semantic wikis. KWQL combines
keyword search and web querying to enable querying that scales with user
experience and information need: basic queries are easy to express; as the
search criteria become more complex, more expertise is needed to formulate
the corresponding query. A novel aspect of KWQL is that it combines both
paradigms in a bottom-up fashion. It treats neither of the two as an
extension to the other, but instead integrates both in one framework. The
language allows for rich combined queries of full text, metadata, document
structure, and informal to formal semantic annotations. KWilt, the KWQL
query engine, provides the full expressive power of first-order queries,
but at the same time can evaluate basic queries at almost the speed of the
underlying search engine. KWQL is accompanied by the visual query language
visKWQL, and an editor that displays both the textual and visual form of
the current query and reflects changes to either representation in the
other. A user study shows that participants quickly learn to construct
KWQL and visKWQL queries, even when given only a short introduction.
KWQL allows users to sift the wealth of structure and annotations in an
information system for relevant data. If relevant data constitutes a
substantial fraction of all data, ranking becomes important. To this end,
we propose PEST, a novel ranking method that propagates relevance among
structurally related or similarly annotated data. Extensive experiments,
including a user study on a real life wiki, show that pest improves the
quality of the ranking over a range of existing ranking approaches