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This paper discusses the research into information seeking and its directions at a general level. We approach this topic by analysis and argumentation based on past research in the domain. We begin by presenting a general model of information seeking and retrieval which is used to derive nine broad dimensions that are needed to analyze information seeking and retrieval. Past research is then contrasted with the dimensions and shown not to cover the dimensions sufficiently. Based on an analysis of the goals of information seeking research, and a view on human task performance augmentation, it is then shown that information seeking is intimately associated with, and dependent on, other aspects of work; tasks and technology included. This leads to a discussion on design and evaluation frameworks for information seeking and retrieval, based on which two action lines are proposed: information retrieval research needs extension toward more context and information seeking research needs extension towards tasks and technology.

Presents a user-oriented interface for generalized informetric analysis and demonstrates how informetric calculations can be specified through advanced data modeling techniques. Topics include bibliographic data; online information retrieval systems; citation networks; query interface; impact factors; data restructuring; and multi-level aggregation capabilities. (Author/LRW)

Discussion of relevance in information retrieval focuses on manifestations of relevance within a system of relevance. Discusses intention, context, inference, and interaction; explains the concepts of affective, motivational, situational, and socio-cognitive relevance; and proposes a consolidated model of relevance manifestations. (Author/LRW)

Reports the feasibility and reliability of calculating impact factors for Web sites, called Web Impact Factors. Analyzes seven small/medium scale national Web sites, four large Web domains, and six institutional Web sites over a series of snapshots taken of the Web during a month. Describes data isolation and calculation methods and discusses tests. (Author/AEF)

Describes the development of a method for the evaluation and comparison of interactive information retrieval systems, which are based on the concept of a simulated work-task situation and the involvement of real end users. Highlights include real and simulated information needs; relevance assessments; and the dynamic nature of information needs. (Author/LRW)

Discusses recent European library and information science (LIS) events. Describes the development and use of regional and intra-European Union networks for science. Highlights three European conferences held in 1996: ACM-SIGIR on information retrieval held in Switzerland, Information Seeking in Context (ISIC) held in Finland, and Conceptions of Library and Information Science 2 (CoLIS) held in Denmark. (PEN)

Discusses impact of three selected command language facilities (positional or free text operations, crossfile searching, term frequency analysis--ZOOM) on the man-system interface in relation to operational online information retrieval (IR). A cognitive IR model, searchers' knowledge structures, and searching with different types of command languages are described. (32 references) (EJS)

Reports experimental results of a study of the cognitive aspects of information retrieval which focused on user interaction with document organization, user-librarian negotiation, and the librarian's search procedure. Related studies, the cognitive viewpoint, and experimental design and analysis methods are discussed. Forty-two sources are listed. (EJS)

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 (approximately 8%) of the queries, and is much higher for low-depth than deep precision measures. Points (ii) and (iii) constitute novel insights into term dependence. [ABSTRACT FROM AUTHOR]

Background: A professional recognition mechanism is required to encourage expedited publishing of an adequate volume of 'fit-for-use' biodiversity data. As a component of such a recognition mechanism, we propose the development of the Data Usage Index (DUI) to demonstrate to data publishers that their efforts of creating biodiversity datasets have impact by being accessed and used by a wide spectrum of user communities. Discussion: We propose and give examples of a range of 14 absolute and normalized biodiversity dataset usage indicators for the development of a DUI based on search events and dataset download instances. The DUI is proposed to include relative as well as species profile weighted comparative indicators. Conclusions: We believe that in addition to the recognition to the data publisher and all players involved in the data life cycle, a DUI will also provide much needed yet novel insight into how users use primary biodiversity data. A DUI consisting of a range of usage indicators obtained from the GBIF network and other relevant access points is within reach. The usage of biodiversity datasets leads to the development of a family of indicators in line with well known citation-based measurements of recognition. [ABSTRACT FROM AUTHOR]

We report data fusion experiments carried out on the four best-performing retrieval models from TREC 5. Three were conceptually/algorithmically very different from one another; one was algorithmically similar to one of the former. The objective of the test was to observe the performance of the 11 logical data fusion combinations compared to the performance of the four individual models and their intermediate fusions when following the principle of polyrepresentation. This principle is based on cognitive IR perspective (Ingwersen & Järvelin, 2005) and implies that each retrieval model is regarded as a representation of a unique interpretation of information retrieval (IR). It predicts that only fusions of very different, but equally good, IR models may outperform each constituent as well as their intermediate fusions. Two kinds of experiments were carried out. One tested restricted fusions, which entails that only the inner disjoint overlap documents between fused models are ranked. The second set of experiments was based on traditional data fusion methods. The experiments involved the 30 TREC 5 topics that contain more than 44 relevant documents. In all tests, the Borda and CombSUM scoring methods were used. Performance was measured by precision and recall, with document cutoff values (DCVs) at 100 and 15 documents, respectively. Results show that restricted fusions made of two, three, or four cognitively/algorithmically very different retrieval models perform significantly better than do the individual models at DCV100. At DCV15, however, the results of polyrepresentative fusion were less predictable. The traditional fusion method based on polyrepresentation principles demonstrates a clear picture of performance at both DCV levels and verifies the polyrepresentation predictions for data fusion in IR. Data fusion improves retrieval performance over their constituent IR models only if the models all are quite conceptually/algorithmically dissimilar and equally and well performing, in that order of importance. [ABSTRACT FROM AUTHOR]

This article focuses on basic framework for webometrics. Library and information sciences(LIS) and related fields in the sociology of science and science and technology studies have developed a range of theories and methodologies now including webometrics. The breakthrough of online citation analysis parallels the later avalanche of webometric studies enabled by access to large-scale Web data. Webometrics and cybermatics are currently the two most widely adopted terms in library and information science for this emerging research field. Webometrics is partially covered by scientometrics, as many scholarly activities today are Web- based, while other such activities are even beyond bibliometrics, i.e., nonrecorded, like person-to-person conversation. The initial exploratory phases of an emerging field like webometrics inevitably lead to a variety in the terminology used. For example, a link received by a Web node (the network term node here denotes a unit of analysis like a Web page, directory, or Web site.

The article reviews the book "Interactive Information Seeking, Behaviour and Retrieval," edited by Ian Ruthven and Diane Kelly.

Abstract: 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 to this 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 design an evaluation approach for web search engines for rare disease diagnosis which includes 56 real life diagnostic cases, performance measures, information resources and guidelines for customising Google Search to this task. 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 set-up 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 standard web search. The proposed evaluation approach can be valuable for future development and benchmarking. The FindZebra search engine is available at http://www.findzebra.com/. [Copyright &y& Elsevier]