162 research outputs found
Towards a geometrical model for polyrepresentation of information objects
The principle of polyrepresentation is one of the
fundamental recent developments in the field of
interactive retrieval. An open problem is how to
define a framework which unifies different as-
pects of polyrepresentation and allows for their
application in several ways. Such a framework
can be of geometrical nature and it may embrace
concepts known from quantum theory. In this
short paper, we discuss by giving examples how
this framework can look like, with a focus on in-
formation objects. We further show how it can be
exploited to find a cognitive overlap of different
representations on the one hand, and to combine
different representations by means of knowledge
augmentation on the other hand. We discuss the
potential that lies within a geometrical frame-
work and motivate its further developmen
Supporting polyrepresentation in a quantum-inspired geometrical retrieval framework
The relevance of a document has many facets, going beyond the usual topical one, which have to be considered to satisfy a user's information need. Multiple representations of documents, like user-given reviews or the actual document content, can give evidence towards certain facets of relevance. In this respect polyrepresentation of documents, where such evidence is combined, is a crucial concept to estimate the relevance of a document. In this paper, we discuss how a geometrical retrieval framework inspired by quantum mechanics can be extended to support polyrepresentation. We show by example how different representations of a document can be modelled in a Hilbert space, similar to physical systems known from quantum mechanics. We further illustrate how these representations are combined by means of the tensor product to support polyrepresentation, and discuss the case that representations of documents are not independent from a user point of view. Besides giving a principled framework for polyrepresentation, the potential of this approach is to capture and formalise the complex interdependent relationships that the different representations can have between each other
Preliminary Experiments using Subjective Logic for the Polyrepresentation of Information Needs
According to the principle of polyrepresentation, retrieval accuracy may
improve through the combination of multiple and diverse information object
representations about e.g. the context of the user, the information sought, or
the retrieval system. Recently, the principle of polyrepresentation was
mathematically expressed using subjective logic, where the potential
suitability of each representation for improving retrieval performance was
formalised through degrees of belief and uncertainty. No experimental evidence
or practical application has so far validated this model. We extend the work of
Lioma et al. (2010), by providing a practical application and analysis of the
model. We show how to map the abstract notions of belief and uncertainty to
real-life evidence drawn from a retrieval dataset. We also show how to estimate
two different types of polyrepresentation assuming either (a) independence or
(b) dependence between the information objects that are combined. We focus on
the polyrepresentation of different types of context relating to user
information needs (i.e. work task, user background knowledge, ideal answer) and
show that the subjective logic model can predict their optimal combination
prior and independently to the retrieval process
Exploiting information needs and bibliographics for polyrepresentative document clustering
In this paper we explore the potential of combining the principle of polyrepresentation with document clustering. Our idea is discussed and evaluated for polyrepresentation of information needs as wells as for document-based polyrepresentation where bibliographic information is used as representation. The main idea is to present the user with the highly ranked polyrepresentative clusters to support the search process. Our evaluation suggests that our approach is capable of increasing retrieval performance, but performance varies for queries with a high or low number of relevant documents
Fedora and GSearch in a Research Project about Integrated Search
4th International Conference on Open RepositoriesThis presentation was part of the session : Fedora User Group PresentationsDate: 2009-05-21 10:30 AM – 12:00 PMThe Royal School of Library and Information Science in Denmark is performing a research project about integrated search. DTU Library provides assistance in the form of a Fedora and GSearch installation.
The presentation will focus on the technical challenges involved in the setup and indexing of the various sources, facilitating the integrated search.DEFF, Denmark's Electronic Research Librar
Citation chain aggregation: An interaction model to support citation cycling
This is the postprint version of the conference paper.Citation chaining is a powerful means of exploring the academic literature. Starting from just one or two known relevant items, a
naïve researcher can cycle backwards and forwards through the citation graph to generate a rich overview of key works, authors and journals relating to their topic. Whilst online citation indexes
greatly facilitate this process, the size and complexity of the search space can rapidly escalate. In this paper, we propose a
novel interaction model called citation chain aggregation (CCA). CCA employs a simple three-list view which highlights the
overlaps that occur between the first-generation relations of known relevant items. As more relevant articles are identified, differences in the frequencies of citations made by or to unseen articles provide strong relevance feedback cues. The benefits of this technique are illustrated using a simple case study
A probabilistic approach for cluster based polyrepresentative information retrieval
A thesis submitted to the University of Bedfordshire in
partial ful lment of the requirements for the degree of
Doctor of PhilosophyDocument clustering in information retrieval (IR) is considered an alternative to rank-based retrieval approaches, because of its potential to support user interactions
beyond just typing in queries. Similarly, the Principle of Polyrepresentation (multi-evidence: combining multiple cognitively and/or functionally diff erent information need or information object representations for improving
an IR system's performance) is an established approach in cognitive IR with plausible applicability in the domain of information seeking and retrieval. The combination of these two approaches can assimilate their respective individual
strengths in order to further improve the performance of IR systems.
The main goal of this study is to combine cognitive and cluster-based IR approaches for improving the eff ectiveness of (interactive) information retrieval systems. In order to achieve this goal, polyrepresentative information retrieval
strategies for cluster browsing and retrieval have been designed, focusing on the evaluation aspect of such strategies.
This thesis addresses the challenge of designing and evaluating an Optimum Clustering Framework (OCF) based model, implementing probabilistic document clustering for interactive IR. Thus, polyrepresentative cluster browsing
strategies have been devised. With these strategies a simulated user based method has been adopted for evaluating the polyrepresentative cluster browsing
and searching strategies.
The proposed approaches are evaluated for information need based polyrepresentative clustering as well as document based polyrepresentation and the combination thereof. For document-based polyrepresentation, the notion of citation
context is exploited, which has special applications in scientometrics and bibliometrics for science literature modelling. The information need polyrepresentation,
on the other hand, utilizes the various aspects of user information need, which is crucial for enhancing the retrieval performance.
Besides describing a probabilistic framework for polyrepresentative document clustering, one of the main fi ndings of this work is that the proposed combination
of the Principle of Polyrepresentation with document clustering has the potential of enhancing the user interactions with an IR system, provided that the various representations of information need and information objects are utilized.
The thesis also explores interactive IR approaches in the context of polyrepresentative interactive information retrieval when it is combined with document clustering methods. Experiments suggest there is a potential in the proposed
cluster-based polyrepresentation approach, since statistically signifi cant improvements were found when comparing the approach to a BM25-based baseline in an ideal scenario. Further marginal improvements were observed when cluster-based re-ranking and cluster-ranking based comparisons were made.
The performance of the approach depends on the underlying information object and information need representations used, which confi rms fi ndings of previous studies where the Principle of Polyrepresentation was applied in diff erent ways
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