Dissertation, Otto-Friedrich-Universität Bamberg, 2025

Beyond the search for content and information, such as texts and documents of all types in information retrieval, class-based search provides information on suitable, superordinate categories, suitable sources, and appropriate generic terms or classifications based on document rankings and their properties.

The starting point and basis of this thesis is existing research on voting techniques in expertise retrieval, which addresses the search for suitable experts on a searched topic based on the relevant and ranked documents of a collection. In this thesis, the concept of expert search is derived, generalized, and referred to as a class search. To find these suitable superordinate classes in response to a query, associated scored and ranked documents with their properties vote for them. This voting process does not correspond to the classic electoral voting systems, but uses voting techniques that aggregate the probative value of voting documents for each class in different ways.

Following theoretic analyses, the properties of conventional and, in the course of the work, also new voting techniques are examined in a larger experimental setup, and their applicability in general scenarios is investigated. In addition to their use at the document level, voting techniques are further investigated at the level of document passages; the relevance of document rankings resulting from passages that vote for their documents is examined.

In addition to similarity measures, the applicability of voting techniques is also examined and evaluated at the level of distance measures. Using the example of hierarchical clustering, the application of voting techniques is related to known clustering techniques, and their systemic behavior is analyzed.

With its theoretical considerations and evaluations of practical scenarios, this work provides a broad overview of voting techniques, their characteristics, differences, and their diverse application scenarios.