1,646 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
Context generation and information retrieval
The interaction between a user and an information retrieval system can be viewed as a dialogue in which both participants are trying to interpret the others' actions in the light of previous experience. The sys- tem then must try to generate a context in which to interpret the user's response to the presented mate- rial. This notion of context operates on a principle of relevance. Information that the system believes is relevant to the user, or that the user has indicated as relevant will form the basis of the system's notion of the context. This paper presents a way of represent- ing a context that can use both the systems knowl- edge about itself and the user's response to generate a view of the retrieval session
Sense resolution properties of logical imaging
The evaluation of an implication by Imaging is a logical technique developed
in the framework of modal logic. Its interpretation in the context of a âpossible
worldsâ semantics is very appealing for IR. In 1994, Crestani and Van Rijsbergen
proposed an interpretation of Imaging in the context of IR based on the assumption
that âa term is a possibleworldâ. This approach enables the exploitation of termâ
term relationshipswhich are estimated using an information theoretic measure.
Recent analysis of the probability kinematics of Logical Imaging in IR have
suggested that this technique has some interesting sense resolution properties. In
this paper we will present this new line of research and we will relate it to more
classical research into word senses
Retrieval through explanation : an abductive inference approach to relevance feedback
Relevance feedback techniques are designed to automatically improve a system's representation of a query by using documents the user has marked as relevant. However, traditional relevance feedback models suffer from a number of limitations that restrict their potential in supporting information seeking. One of the major limitations of relevance feedback is that it does not incorporate behavioural aspects of information seeking - how and why users assess relevance. We propose that relevance feedback should be viewed as a process of explanation and demonstrate how this limitation of relevance feedback techniques can be overcome by a theory of relevance feedback based on abductive inference
Ranking expansion terms using partial and ostensive evidence
In this paper we examine the problem of ranking candidate expansion terms for query expansion. We show, by an extension to the traditional F4 scheme, how partial relevance assessments (how relevant a document is) and ostensive evidence (when a document was assessed relevant) can be incorporated into a term ranking function. We then investigate this new term ranking function in three user experiments, examining the performance of our function for automatic and interactive query expansion. We show that the new function not only suggests terms that are preferred by searchers but suggests terms that can lead to more use of expansion terms
Combining and selecting characteristics of information use
In this paper we report on a series of experiments designed to investigate the combination of term and document weighting functions in Information Retrieval. We describe a series of weighting functions, each of which is based on how information is used within documents and collections, and use these weighting functions in two types of experiments: one based on combination of evidence for ad-hoc retrieval, the other based on selective combination of evidence within a relevance feedback situation. We discuss the difficulties involved in predicting good combinations of evidence for ad-hoc retrieval, and suggest the factors that may lead to the success or failure of combination. We also demonstrate how, in a relevance feedback situation, the relevance assessments can provide a good indication of how evidence should be selected for query term weighting. The use of relevance information to guide the combination process is shown to reduce the variability inherent in combination of evidence
Topic based language models for ad hoc information retrieval
We propose a topic based approach lo language
modelling for ad-hoc Information Retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models
Investigating the relationship between language model perplexity and IR precision-recall measures
An empirical study has been conducted investigating the relationship between the performance of an aspect based language model in terms of perplexity and the corresponding information retrieval performance obtained. It is observed, on the corpora considered, that the perplexity of the language model has a systematic relationship with the achievable precision recall performance though it is not statistically significant
An adaptive approach for image organisation and retrieval
We propose and evaluate an adaptive approach towards content-based image retrieval (CBIR), which is based on the Ostensive Model of developing information needs. We use ostensive relevance to capture the user's current interest and tailor the retrieval accordingly. Our approach supports content-assisted browsing, by incorporating an adaptive query learning scheme based on implicit feedback from the user. Textual and colour features are employed to characterise images. Evidence from these features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, task-oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. Its strengths are considered to lie in its ability to adapt to the user's need, and its very intuitive and fluid way of operation
- âŠ