1,721,081 research outputs found
A study of search user interface design based on Hofstede’s six cultural dimensions
No full textAn information seeker’s cultural background could influence their preference for search user interface (UI) design. To study cultural influences Geert Hofstede’s cultural dimensions have been applied to website design for a number of years. In this paper, we examine if Hofstede’s six cultural dimension can be applied to inform the design of search engine user interfaces. The culturally designed search user interfaces have been evaluated in a study with 148 participants of different cultural backgrounds. The results have been analysed to determine if Hofstede’s cultural dimensions are appropriate for understanding users’ preferences on search user interface design. Whilst the key findings from the study suggest Hofstede cross-cultural dimensions can be used to model users’ preferences on search interface design, further work is still needed for particular cultural dimensions to reinforce the conclusions
BIRDS-Bridging the Gap between Information Science, Information Retrieval and Data Science
No full textThe BIRDS workshop aimed to foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and Data Science (DS). Recognising the commonalities and differences between these communities, the proposed full-day workshop brought together experts and researchers in IS, IR and DS to discuss how they can learn from each other to provide more user-driven data and infor-mation exploration and retrieval solutions. Therefore, the papers aimed to convey ideas on how to utilise, for instance, IS concepts and theories in DS and IR or DS approaches to support users in data and information exploration
SciPlore Xtract: Extracting Titles from Scientific PDF Documents by Analyzing Style Information (Font Size)
No full textOn Table Extraction from Text Sources with Markups
No full textTable extraction is the task of locating tables in documents and extracting their entries along with the arrangement of the entries inside the tables. The notion of tables applied in this work excludes any sort of meta data, e.g. only the content elements of the tables are to be extracted. We follow a simple unsupervised approach by selecting the tables according to a score that measures the in-column consistency as pairwise similarities of entries where separators columns are also taken into account. Since the average is less reliable for smaller table this score demands a levelling in favor of greater tables for which we make different propositions that are covered by experiments on a test set of HTML documents. In order to reduce the number of candidate tables we use assumptions on the entry borders in terms of markup tags. They only hold for a part of the test set but allow us to evaluate any potential table without referring to the HTML syntax. The experiments show that the discriminative power of the in-column similarities are limited but also considerable given the simplicity of the applied similarity functions
How do practitioners, PhD students and postdocs in the social sciences assess topic-specific recommendations?
Full text link"In this paper we describe a case study where researchers in the social sciences (n=19) assess topical relevance for controlled search terms, journal names and author names which have been compiled by recommender services. We call these services Search Term Recommender (STR), Journal Name Recommender (JNR) and Author Name Recommender
(ANR) in this paper. The researchers in our study (practitioners, PhD students and postdocs) were asked to assess the top n preprocessed
recommendations from each recommender for specific research topics which have been named by them in an interview before the experiment. Our results show clearly that the presented search term, journal name and author name recommendations are highly relevant to the researchers topic and can easily be integrated for search in Digital Libraries. The average precision for top ranked recommendations is 0.749 for author names, 0.743 for search terms and 0.728 for journal names.
The relevance distribution differs largely across topics and researcher types. Practitioners seem to favor author name recommendations while postdocs have rated author name recommendations the lowest. In the experiment the small postdoc group favors journal name recommendations." (author's abstract
Supporting polyrepresentation in a quantum-inspired geometrical retrieval framework
Full text linkThe relevance of a document has many facets, going beyond the usual topical one, which have to be considered to satisfy a user's information need. Multiple representations of documents, like user-given reviews or the actual document content, can give evidence towards certain facets of relevance. In this respect polyrepresentation of documents, where such evidence is combined, is a crucial concept to estimate the relevance of a document. In this paper, we discuss how a geometrical retrieval framework inspired by quantum mechanics can be extended to support polyrepresentation. We show by example how different representations of a document can be modelled in a Hilbert space, similar to physical systems known from quantum mechanics. We further illustrate how these representations are combined by means of the tensor product to support polyrepresentation, and discuss the case that representations of documents are not independent from a user point of view. Besides giving a principled framework for polyrepresentation, the potential of this approach is to capture and formalise the complex interdependent relationships that the different representations can have between each other
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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