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When submitting queries to information retrieval (IR) systems, users often have the option of specifying which, if any, of the query terms are heavily dependent on each other and should be treated as a fixed phrase, for instance by placing them between quotes. In addition to such cases where users specify term dependence, automatic ways also exist for IR systems to detect dependent terms in queries. Most IR systems use both user and algorithmic approaches. It is not however clear whether and to what extent user-defined term dependence agrees with algorithmic estimates of term dependence, nor which of the two may fetch higher performance gains. Simply put, is it better to trust users or the system to detect term dependence in queries? To answer this question, we experiment with 101 crowdsourced search engine users and 334 queries (52 train and 282 test TREC queries) and we record 10 assessments per query. We find that (i) user assessments of term dependence differ significantly from algorithmic assessments of term dependence (their overlap is approximately 30%); (ii) there is little agreement among users about term dependence in queries, and this disagreement increases as queries become longer; (iii) the potential retrieval gain that can be fetched by treating term dependence (both user- and system-defined) over a bag of words baseline is reserved to a small subset (approxi-mately 8%) of the queries, and is much higher for low-depth than deep preci-sion measures. Points (ii) and (iii) constitute novel insights into term dependence.

Interactive Information Retrieval refers to the branch of Information Retrieval that considers the retrieval process with respect to a wide range of contexts, which may affect the user's information seeking experience. The identification and representation of such contexts has been the object of the principle of Polyrepresentation, a theoretical framework for reasoning about different representations arising from interactive information retrieval in a given context. Although the principle of Polyrepresentation has received attention from many researchers, not much empirical work has been done based on it. One reason may be that it has not yet been formalised mathematically. In this paper we propose an up-to-date and exible mathematical formalisation of the principle of Polyrepresentation for information needs. Specifically, we apply Subjective Logic to model different representations of information needs as beliefs marked by degrees of uncertainty. We combine such beliefs using different logical operators, and we discuss these combinations with respect to different retrieval scenarios and situations. A formal model is introduced and discussed, with illustrative applications to the modelling 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.

Background: The web has become a primary information resource about illnesses and treatments for both medical and non-medical users. Standard web search is by far the most common interface for such information. It is therefore of interest to find out how well web search engines work for diagnostic queries and what factors contribute to successes and failures. Among diseases, rare (or orphan) diseases represent an especially challenging and thus interesting class to diagnose as each is rare, diverse in symptoms and usually has scattered resources associated with it. Methods: We use an evaluation approach for web search engines for rare disease diagnosis which includes 56 real life diagnostic cases, state-of-the-art evaluation measures, and curated information resources. In addition, we introduce FindZebra, a specialized (vertical) rare disease search engine. FindZebra is powered by open source search technology and uses curated freely available online medical information. Results: FindZebra outperforms Google Search in both default setup and customised to the resources used by FindZebra. We extend FindZebra with specialized functionalities exploiting medical ontological information and UMLS medical concepts to demonstrate different ways of displaying the retrieved results to medical experts. Conclusions: Our results indicate that a specialized search engine can improve the diagnostic quality without compromising the ease of use of the currently widely popular web search engines. The proposed evaluation approach can be valuable for future development and benchmarking. The FindZebra search engine is available at http://www.findzebra.com/.

Reviews and discusses critically the development during the last decade of the cognitive approach to information retrieval research and theory. The focus is analytic and empirical research on the complex nature of information need formation and situation, their inherent association with the concept of relevance, and the development of cognitive and related information retrieval theory and evaluation methods. (Contains 173 references.) (AEF)

Discusses the retrieval dimensions of data collection activity online and their influence on the research evaluation outcome to reinforce the link between online retrieval and bibliometrics. Topics include publication counts; citation analyses; citation databases from the Institute for Scientific Information; and data set isolation. (Author/LRW)

Discusses the basic elements of a global cognitive theory for information retrieval (IR) interaction from a cognitive point of view. Within this framework are outlined the principles underlying the concept of polyrepresentation applied simultaneously to the user's cognitive space and the information space of IR systems. Contains 95 references. (Author/LRW)

Outlines principles underlying the theory of polyrepresentation as it applies to user cognitive space and information space of information retrieval (IR) systems. A discussion and diagram illustrate the interactive communication that takes place between users and IR systems in order to complete work tasks such as retrieving full text information. (Author/JMV)

This study of negotiation processes between users and librarians in typical reference situations describes association patterns and search procedures in the information transfer process in terms of theoretical approaches, design, analysis, and findings. The thinking-aloud technique of cognitive psychology was adopted to the project, which involved exploratory recordings of communication and information searching, training 20 librarians and seven users in the technique, recording search processes of 13 librarians, and recording the communication and search procedures between three librarians and five users. Transcribing the recordings into protocols (text) included noting pauses, behavioral remarks, and materials used. Microanalysis of the protocols identified the relations between activities, thought processes, and information perceived by the subject during the problem solving process, while macroanalysis identified general problems in the search. Findings identify three basic mental attitudes which determine the nature of searches: open, fixed, and semi-fixed. Influence of knowledge structures on question negotiation processes and implications of the findings are also discussed. Eleven figures, four tables, and 67 sources are provided. (RBF)