1.5 Main Contributions
The following summarizes the main contributions of this thesis, which adds both
theoretical insights and practical contributions to the body of existing work in the
- Novel relevance feedback methods — We develop two query modeling
methods for relevance feedback that are based on leveraging the similarity
between feedback documents and the set thereof.
- Comparison of relevance feedback methods — We provide a
comprehensive analysis, evaluation, comparison, and discussion (in both
theoretical and practical terms) of our novel and various other core models
for query modeling using relevance feedback.
- Concept-based query modeling — We develop a way of using
document-level annotations to improve end-to-end retrieval performance.
Our model naturally generates concept models, which may serve to support,
for example, interaction tools for users or which can be used to determine
semantic similarity between concepts using the language observed in the
documents associated with the concepts.
- Novel method for linking queries to concept languages — We develop
and evaluate a novel way of associating concepts with queries that
effectively handles arbitrary features. For example, features pertaining to
the query, concepts, search history, etc.
- Understanding of relevant features for concept identification in queries
— We provide insights why some (groups of features) perform better than
others in the context of linking queries to concepts.
- Wikipedia-based query modeling — We show that using the linked
concepts can be effectively used to improve diversity and ad hoc retrieval
effectiveness on two large test collections.
- State of the art retrieval effectiveness — Through extensive experimental
evaluations on various test collections (including those from the biomedical,
web, social science, and news domains) we validate and analyze our
proposed models. In most cases we show consistent and significant
improvements over established and state-of-the-art methods on ad hoc