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Abstract

This thesis is organised as follows: Chapter 2 gives a general introduction to the field of information retrieval, covering its most important aspects. Further, the tasks of distributed and peer-to-peer information retrieval (P2PIR) are introduced, motivating their application and characterising the special challenges that they involve, including a review of existing architectures and search protocols in P2PIR. Finally, chapter 2 presents approaches to evaluating the e ectiveness of both traditional and peer-to-peer IR systems. Chapter 3 contains a detailed account of state-of-the-art information retrieval models and algorithms. This encompasses models for matching queries against document representations, term weighting algorithms, approaches to feedback and associative retrieval as well as distributed retrieval. It thus defines important terminology for the following chapters. The notion of "multi-level association graphs" (MLAGs) is introduced in chapter 4. An MLAG is a simple, graph-based framework that allows to model most of the theoretical and practical approaches to IR presented in chapter 3. Moreover, it provides an easy-to-grasp way of defining and including new entities into IR modeling, such as paragraphs or peers, dividing them conceptually while at the same time connecting them to each other in a meaningful way. This allows for a unified view on many IR tasks, including that of distributed and peer-to-peer search. Starting from related work and a formal defiition of the framework, the possibilities of modeling that it provides are discussed in detail, followed by an experimental section that shows how new insights gained from modeling inside the framework can lead to novel combinations of principles and eventually to improved retrieval effectiveness.