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.