February 08, 2011

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.

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By Ludwig-Maximilians-Universität München

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