Исследовательский поиск научных статей

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model support d by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world. Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks

Pointing Users Toward Citation Searching: Using Google Scholar and Web of Science Robert Schroeder (bio) Much has been written about citation indexing since Eugene Garfield's seminal article in 1955, "Citation Indexes for Science: A New Dimension in Documentation through Association of Ideas."1 In the 1960s, the discussion was about Garfield's Science Citation Index and later its successor, Web of Science. For almost half a century, these were the only available tools for tracing scholarly discourse forward in time. Now there are two new tools. SCOPUS is a commercial product from Elsevier and was launched in November 2004. Google Scholar also arrived on the scene in November 2004. Because Google Scholar is freely accessible from the Google site, students and faculty are finding and using it. They are beginning to ask librarians for their professional opinions of its efficacy. Practicing reference and instruction librarians need to understand the strengths of both Google Scholar and Web of Science so that they can appropriately recommend them for use by their patrons—whether they are undergraduates, graduate students, or faculty. Since April 2005, 10 studies have been published that directly compare the citation features of Google Scholar to those of Web of Science. In an effort to better understand the advantages and disadvantages of these tools, the author undertook an investigation of this research. Although the parameters of the studies varied greatly with respect to the disciplines, dates, and sample sizes, analysis of these preliminary studies can help us gain an initial—albeit sketchy—impression of the relative strength and weakness of each service. The results of this effort are reported here as an informal meta-analysis, followed by some recommendations for utilizing these tools across a range of needs. The coverage of each database is both overlapping and complementary. Web of Science is comprised of a known list of highly prestigious journals. The extent to which disciplines are covered is well known, but it is limited to these journal holdings. Although Google Scholar overlaps with Web of Science in some of its coverage, it also includes conference proceedings, books, preprints, and a variety of versions of articles available in open access databases and institutional repositories. Search results do, however, include many "false hits"—non-scholarly sources or titles that are similar to, but not [End Page 243] exact matches for, the target citation. These spurious links cause the researcher to spend much more time analyzing and evaluating the sources. Click for larger view View full resolution Table 1. Overview of the scope of the 10 studies Date coverage is also complementary, with Web of Science consistently providing older articles. Google Scholar often returns more current results due to its ability to access early versions of works in progress and open access articles available on the Internet. Although the controlled vocabulary in Web of Science is less than perfect, it wins hands-down over Google Scholar, which is totally lacking in any of the finer points of indexing. [End Page 244] Click for larger view View full resolution Table 2. Summary of positive and negative characteristics of Web of Science and Google Scholar from the 10 studies The findings from these initial studies suggest a variety of uses for both of the databases' citation features in academic reference and instruction. For instructing undergraduate students in the use of the "cited by" features, Google Scholar's lack of advanced search functions may actually be a boon. Most students today are familiar with the look and feel of the "one box searching" of Google. Having a similar uncluttered look, Google Scholar appears less intimidating to novice users, allowing them to focus on the concepts involved in citation searching and analysis. Google Scholar acts as a bridge from the known quantity of Google to more advanced instruction required for Web of Science. Discussing the strengths and weaknesses of both databases is a good way to begin a dialog about when Google Scholar might be an appropriate tool [End Page 245] for research and also allow librarians to make a stronger case for why other databases such as Web of Science need to be used as well. For many of these same reasons, Google Scholar...

Adaptive visualization for exploratory information retrieval

Carrier Cell-based Delivery of an Oncolytic Virus Circumvents Antiviral Immunity

Exploratory search is a search paradigm that plays a vital role in databases, data mining, and information retrieval to assist users to get familiar with the underlying databases. It supports iterative query formulation to explore the data space. Despite its growing importance, exploratory search on graph-structured data has not received adequate attention in the literature. In this paper, we demonstrate a novel system called Vincent that facilitates an efficient exploratory subgraph search in a graph database containing a large collection of small or medium-sized graphs. By automatically generating the content for panels in GUI and diversified patterns from databases and providing a visual result explorer, Vincent supports data-driven visual query formulation, incremental subgraph processing, and efficient query result summarization.

Search and information retrieval systems are becoming more expressive in interpreting user queries beyond the traditional weighted bag-of-words model of document retrieval. For example, searching for a flight status or a game score returns a dynamically generated response along with supporting, pre-authored documents contextually relevant to the query. In this paper, we extend this hybrid search paradigm to data repositories that contain curated data sources and visualization content. We introduce a semantic search interface, Olio, that provides a hybrid set of results comprising both auto-generated visualization responses and pre-authored charts to blend analytical question-answering with content discovery search goals. We specifically explore three search scenarios - question-and-answering, exploratory search, and design search over data repositories. The interface also provides faceted search support for users to refine and filter the conventional best-first search results based on parameters such as author name, time, and chart type. A preliminary user evaluation of the system demonstrates that Olio’s interface and the hybrid search paradigm collectively afford greater expressivity in how users discover insights and visualization content in data repositories.

A Review of:
 Ritchie, S. M., Young, L. M., & Sigman, J. (2018). A comparison of selected bibliographic database subject overlap for agricultural information. Issues in Science and Technology Librarianship, 89. http://doi.org/10.5062/F49Z9340
 Abstract
 Objective – To determine the most comprehensive database(s) for agricultural literature searching.
 Design – Data collection and analysis was conducted using a modified version of the bibliography method, overlap analysis, chi square tests, and data visualization methods.
 Setting – An academic library in the U.S.
 Subjects – Eight commonly used bibliographic databases, including comprehensive agricultural indexes (AGRICOLA, AGRIS, and CAB Abstracts), specialized databases (BIOSIS Previews and FSTA), and multidisciplinary databases (Google Scholar, Scopus, and Web of Science).
 Methods – The researchers selected three review articles that represented sub-topics within the field of agriculture. Sources listed in the bibliographies of the three review articles were used to build a bibliographic citation set for analysis.
 Using a modified version of the bibliography method, 90 citations were randomly selected from the above-mentioned citation set. Researchers then turned to the 8 selected databases and searched for all 90 citations in each platform. Search queries were crafted in two ways: unique title strings in quotation marks and combinations of terms entered into the “title”, “keyword”, “journal source”, and “author” fields. Citations were considered to be covered in a database if the full bibliographic record was located using the above-mentioned search strategy.
 Next, chi square tests were used to evaluate if the expected number of citations from the sample group were found in each database or if the frequency differed between the eight databases. The overlap analysis method provided numerical representation of the degree of similarity and difference across the eight databases. Finally, data visualizations created in Excel and Gephi enhanced comparisons between the eight databases and highlighted differences that were not obvious based solely on the analysis of numerical data.
 Main Results – Researchers found that comprehensive databases (AGRICOLA, AGRIS, and CAB Abstracts) were not in fact comprehensive in their coverage of agricultural literature. However, the results suggested that CAB Abstracts was more comprehensive than AGRICOLA or AGRIS, particularly in regard to its coverage of the sub-topics “agronomy” and “meat sciences”. However, coverage of the sub-topic “sustainable diets” lagged behind multidisciplinary databases, which may be explained by the fact that the topic is interdisciplinary in nature. The superior coverage of CAB Abstracts over other comprehensive databases is consistent with findings reported by Kawasaki (2004).
 The analysis of specialized databases (BIOSIS Previews and FSTA) suggested that citations within the scope of the database were covered very well, while those out of scope were not. For instance, the sub-topics “sustainable diets” and “meat science” are out of scope of the biological sciences and thus, were not well covered in BIOSIS.
 The multidisciplinary databases (Google Scholar, Scopus and Web of Science) provided the most comprehensive coverage agricultural literature. All three databases covered most citations included in the data set. However, researchers noted that all three databases provided weak coverage of trade published items, books, or older journals.
 Conclusion – The study found that multidisciplinary databases provide close to full coverage of agricultural literature. In addition, they provide the best access to content that is interdisciplinary in nature. Specialized and comprehensive databases are recommended when research topics are within the scope of the database. Also, they best support in-depth projects such as bibliographies or comprehensive review articles.

Despite the growing importance of exploratory search, information retrieval (IR) systems tend to focus on lookup search. Lookup searches are well served by optimising the precision and recall of search results, however, for exploratory search this may be counterproductive if users are unable to formulate an appropriate search query. We present a system called PULP that supports exploratory search for scientific literature, though the system can be easily adapted to other types of literature. PULP uses reinforcement learning (RL) to avert the user from context traps resulting from poorly chosen search queries, trading off between exploration (presenting the user with diverse topics) and exploitation (moving towards more specific topics). Where other RL-based systems suffer from the cold start problem, requiring sufficient time to adjust to a user's information needs, PULP initially presents the user with an overview of the dataset using temporal topic models. Topic models are displayed in an interactive alluvial diagram, where topics are shown as ribbons that change thickness with a given topics relative prevalence over time. Interactive, exploratory search sessions can be initiated by selecting topics as a starting point.

This paper focuses on the practical limitations in the content and software of the databases that are used to calculate the h -index for assessing the publishing productivity and impact of researchers. To celebrate F. W. Lancaster’s biological age of seventy-five, and “scientific age” of forty-five, this paper discusses the related features of Google Scholar, Scopus, and Web of Science (WoS), and demonstrates in the latter how a much more realistic and fair h -index can be computed for F. W. Lancaster than the one produced automatically. Browsing and searching the cited reference index of the 1945–2007 edition of WoS, which in my estimate has over a hundred million “orphan references” that have no counterpart master records to be attached to, and “stray references” that cite papers which do have master records but cannot be identified by the matching algorithm because of errors of omission and commission in the references of the citing works, can bring up hundreds of additional cited references given to works of an accomplished author but are ignored in the automatic process of calculating the h -index. The partially manual process doubled the h -index value for F. W. Lancaster from 13 to 26, which is a much more realistic value for an information scientist and professor of his stature.

Exploratory search is an intuitive concept in interactive information retrieval. It is known that searchers use exploratory search strategies when learning and investigating a new domain. Many definitions for Exploratory Search have been proposed. However, the main dimensions involve high uncertainty with respect to the problem context, the user expertise, and the search process. In this work, we study exploratory search in the literature, and we provide a conceptual model of exploratory search. We also conduct a user study to examine how literature searches are exploratory and what factors influence the exploratory dimensions and characteristics. Moreover, we review the exploratory support tasks, and we try to better design and evaluate exploratory user interfaces.

The study examined information delivery in distance learning libraries through metaverse technologies. The study adopted an exploratory strategy through an extensive literature analysis. Relevant academic papers, journals, industry reports, and conference proceedings located through internet databases such as Google Scholar, Scopus, and Web of Science were analyzed. The findings revealed that the types of information delivered through metaverse technologies in distance learning libraries are interactive lectures, virtual simulations, 3D visualizations, and collaborative learning spaces. These technologies enable the delivery of immersive content, such as virtual field trips, digital archives, and interactive tutorials, fostering deeper engage­ment. major metaverse technologies, such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and 3D collaborative platforms. These technologies are used to create immersive library experiences, including virtual library tours, AI-driven avatars for user assistance, and real-time collaboration hubs. Strategic, phased approach, backed by stakeholder collaboration and institutional support, are vital for successful integration, ensuring that metaverse technologies enhance both user engagement and accessibility. The best practices identified include investment in scalable infrastructure, ensuring data security, and developing inclusive designs that accommodate diverse user needs. Staff training, user feedback, and partnerships with technology developers are essential for continuous improvement. Challenges associated with using metaverse for information delivery are limited infrastructure, inadequate funding, and the digital divide. Libraries also face issues with staff capacity, poor internet connectivity, and outdated service delivery methods. User barriers, such as low digital literacy and lack of awareness of available resources, exacerbate these issues.

Recently, exploratory search has received much attention in information retrieval and database fields. This search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data space. Specifically, query formulation evolves iteratively as the user becomes more familiar with the content. Despite its growing importance, exploratory search on graph-structured data has received little attention in the literature. We demonstrate a system called picasso to realize exploratory sub-structure search on a graph database containing a set of small or medium-sized data graphs. picasso embodies several novel features such as progressive ( i.e. , iterative) formulation of queries visually and incremental processing, multi-stream results exploration wall to visualize, explore, and analyze search results to identify possible search directions.

Search activities involving knowledge acquisition, investigation and synthesis are collectively known as exploratory search. Exploratory search is challenging for users, who may be unable to formulate search queries, have ill-defined search goals or may even struggle to understand search results. To ameliorate these difficulties, reinforcement learning-based information retrieval systems were developed to provide adaptive support to users. Reinforcement learning is used to build a model of user intent based on relevance feedback provided by the user. But how reliable is relevance feedback in this context? To answer this question, we developed a novel permutation-based metric for scoring the consistency of relevance feedback. We used this metric to perform a retrospective analysis of interaction data from lookup and exploratory search experiments. Our analysis shows that for lookup search relevance judgments are highly consistent, supporting previous findings that relevance feedback improves retrieval performance. For exploratory search, however, the distribution of consistency scores shows considerable inconsistency.

Promoting Evidence-Based Nursing Procedures by Partnering With a Vendor

The purpose of this study was to examine how much celebrity endorsement stimulates skin color racism in the cosmetics industry. The data were collected from Google Scholar and Web of Science published articles, and researchers had chosen 45 research articles. Some of the research articles used a quantitative research approach while others had used qualitative research approach. And for the current study, content analysis has been used. The study finds that celebrity endorsement does influence and promote racism, implying that when celebrities promote fairness products, people perceive themselves inferior due to darker skin tone that gives birth to the notion of racism. Brand Image intervenes in the relationship between Celebrity Endorsement and Racism, and also, intervenes in the relationship between Celebrity Endorsement and Purchase Intention. This study is only limited to Google Scholar and Web of Science directory. Only forty-five articles were taken from 2001 to 2021year. Real examples were taken from all over the world, but especially from the Less Developed countries like Pakistan and India due to the huge population, rising income, and surging cosmetics industry. Hence, the findings of this study cannot be generalized to the Technologically Advanced Countries. It is obvious that firms design advertisement campaigns that can get consumers' attention. For this purpose, they engage celebrities to evoke more interest and awareness as well as perception. The study will help the management of different brands to understand that how they can improve their advertisements in a way that does not promote racism. And the celebrities, signing contracts with brands that promote racism, will keep in mind the negative influence these endorsements have on society while companies will make sure that they are also not promoting racism by making such promotional campaigns.