The effects on topic familiarity on online search behaviour and use of relevance criteria

This paper presents an experimental study on the effect of topic familiarity on the assessment behaviour of online searchers. In particular we investigate the effect of topic familiarity on the resources and relevance criteria used by searchers. Our results indicate that searching on an unfamiliar topic leads to use of more generic and fewer specialised resources and that searchers employ different relevance criteria when searching on less familiar topics

Introduction to the special issue on evaluating interactive information retrieval systems

Evaluation has always been a strong element of Information Retrieval (IR) research, much of our focus being on how we evaluate IR algorithms. As a research field we have benefited greatly from initiatives such as Cranfield, TREC, CLEF and INEX that have added to our knowledge of how to create test collections, the reliability of system-based evaluation criteria and our understanding of how to interpret the results of an algorithmic evaluation. In contrast, evaluations whose main focus is the user experience of searching using IR systems have not yet reached the same level of maturity. Such evaluations are complex to create and assess due to the increased number of variables to incorporate within the study, the lack of standard tools available (for example, test collections) and the difficulty of selecting appropriate evaluation criteria for study

Query-Based Document Skimming: A User-Centred Evaluation of Relevance Profiling

We present a user-centred, task-oriented, comparative evaluation of two query-based document skimming tools. ProfileSkim bases within-document retrieval on computing a relevance profile for a document and query; FindSkim provides similar functionality to the web browser Find-command. A novel simulated work task was devised, where experiment participants are asked to identify (index) relevant pages of an electronic book, given subjects from the existing book index. This subject index provides the ground truth, against which the indexing results can be compared. Our major hypothesis was confirmed, namely ProfileSkim proved significantly more efficient than Find-Skim, as measured by time for task. Moreover, indexing task effectiveness, measured by typical IR measures, demonstrated that ProfileSkim was better than FindSkim in identifying relevant pages, although not significantly so. The experiments confirm the potential of relevance profiling to improve query-based document skimming, which should prove highly beneficial for users trying to identify relevant information within long documents

Foodmaster and three stories

Graduation date: 2004The purpose of this thesis was to create a sustained piece of fiction that both\ud represented my growth as a writer throughout my time at Oregon State University, and\ud wove together a mixture of imagination, language, and creativity. My hope was to write\ud a novella that incorporated and drew from themes including work, community, and\ud family relationships, and also was an exploration in the very structure and form of literary\ud fiction.\ud After completing the novella, I found that similar themes continued to appear\ud within my fiction during my ongoing growth as a writer. What I ended up with was a\ud novella and collection of related stories that reflected the influences of my advisor Tracy\ud Daugherty and his tutelage, the courses that I took at this university and my\ud undergraduate university, and my own personal history.\ud This thesis was written over a two-year period, during which drafts of this novella\ud and stories were written and rewritten. Each story and chapter was submitted to a writing\ud workshop, read and edited by my major and minor advisor, and carefully reworked and\ud redrafted after much scrutiny and attention.\ud During the course of writing this thesis, many things influenced me, the most\ud prominent being the world of fiction that existed all around me. I was influenced by\ud fiction that I was reading in my course work, such as Donald Barthelme and Philip Roth,\ud but writers that I had grown up with, like Edgar Allen Poe and Ray Bradbury also\ud influenced me. Beyond the world of published fiction, I found not only influence, but\ud also more importantly inspiration from the work and criticism of the writers and students\ud within the Creative Writing Program here at Oregon State University.\ud The end result of these two years of work, study, writing, and criticism was a\ud piece of fiction that I am proud of, and plan to publish. This collection of fiction\ud represents not only a sustained study on the craft of creative writing, but also serves an\ud exploration of my own voice and style, and an awakening of my identity as a fiction\ud writer

Studying How Health Literacy Influences Attention during Online Information Seeking

Health literacy affects how people understand health information and, therefore, should be considered by search engines in health searches. In this work, we analyze how the level of health literacy is related to the eye movements of users searching the web for health information. We performed a user study with 30 participants that were asked to search online in the context of three work task situations defined by the authors. Their eye interactions with the Search Results Page and the Result Pages were logged using an eye-tracker and later analyzed. When searching online for health information, people with adequate health literacy spend more time and have more fixations on Search Result Pages. In this type of page, they also pay more attention to the results' hyperlink and snippet and click in more results too. In Result Pages, adequate health literacy users spend more time analyzing textual content than people with lower health literacy. We found statistical differences in terms of clicks, fixations, and time spent that could be used as a starting point for further research. That we know of, this is the first work to use an eye-tracker to explore how users with different health literacy search online for health-related information. As traditional instruments are too intrusive to be used by search engines, an automatic prediction of health literacy would be very useful for this type of system

Modelling the response of phytoplankton in a shallow lake (Loch Leven, UK) to changes in lake retention time and water temperature.

The phytoplankton community of Loch Leven in 2005 was modelled and subjected to a combination of different flushing rates and water temperatures in order to assess the lake’s sensitivity to these two climatic drivers. Whilst the simulated annual mean total chlorophyll a proved relatively insensitive to these changes, at the species level marked changes were recorded. Some species responded positively to increased temperature (e.g. Aulacoseira), some negatively (e.g. Asterionella), whilst others were negatively affected by increased flow (e.g. Aphanocapsa) and others enhanced (e.g. Stephanodiscus). However, this relationship with flow was season dependent with, for example, a simulated increase in summer inflows actually benefiting some species through increased nutrient supply, whereas an equivalent increase in flow in wetter seasons would have negatively affected those species (i.e. through flushing loss). Overall, the simulations showed that the range of species types simulated in the community was sufficient for one species to always benefit from the changing niches created by the multiple climatic drivers applied in this study. The level of exploitation by such a species was only constrained by the nutrient carrying capacity of the system, which led to the overall dampened response in the total chlorophyll a measure, both at the annual and season scale. Thus, whilst overall biomass showed relatively little reaction to the two climatic drivers tested, the phytoplankton community composition responded markedly

Evaluating implicit feedback models using searcher simulations

In this article we describe an evaluation of relevance feedback (RF) algorithms using searcher simulations. Since these algorithms select additional terms for query modification based on inferences made from searcher interaction, not on relevance information searchers explicitly provide (as in traditional RF), we refer to them as implicit feedback models. We introduce six different models that base their decisions on the interactions of searchers and use different approaches to rank query modification terms. The aim of this article is to determine which of these models should be used to assist searchers in the systems we develop. To evaluate these models we used searcher simulations that afforded us more control over the experimental conditions than experiments with human subjects and allowed complex interaction to be modeled without the need for costly human experimentation. The simulation-based evaluation methodology measures how well the models learn the distribution of terms across relevant documents (i.e., learn what information is relevant) and how well they improve search effectiveness (i.e., create effective search queries). Our findings show that an implicit feedback model based on Jeffrey's rule of conditioning outperformed other models under investigation

Creating and Using Organisational Semantic Webs in Large Networked Organisations

Abstract. Modern knowledge management is based on the orchestration of dynamic communities that acquire and share knowledge according to customized schemas. However, while independence of ontological views is favoured, these communities must also be able to share their knowledge with the rest of the organization. In this paper we introduce K-Forms and K-Search, a suite of Semantic Web tools for supporting distributed and networked knowledge acquisition, capturing, retrieval and sharing. They enable communities of users to define their own domain views in an intuitive way (automatically translated into formal ontologies) and capture and share knowledge according to them. The tools favour reuse of existing ontologies; reuse creates as side effect a network of (partially) interconnected ontologies that form the basis for knowledge exchange among communities. The suite is under release to support knowledge capture, retrieval and sharing in a large jet engine company