3,058 research outputs found
Reducing semantic complexity in distributed Digital Libraries: treatment of term vagueness and document re-ranking
The purpose of the paper is to propose models to reduce the semantic
complexity in heterogeneous DLs. The aim is to introduce value-added services
(treatment of term vagueness and document re-ranking) that gain a certain
quality in DLs if they are combined with heterogeneity components established
in the project "Competence Center Modeling and Treatment of Semantic
Heterogeneity". Empirical observations show that freely formulated user terms
and terms from controlled vocabularies are often not the same or match just by
coincidence. Therefore, a value-added service will be developed which rephrases
the natural language searcher terms into suggestions from the controlled
vocabulary, the Search Term Recommender (STR). Two methods, which are derived
from scientometrics and network analysis, will be implemented with the
objective to re-rank result sets by the following structural properties: the
ranking of the results by core journals (so-called Bradfordizing) and ranking
by centrality of authors in co-authorship networks.Comment: 12 pages, 4 figure
An evaluation of Bradfordizing effects
The purpose of this paper is to apply and evaluate the bibliometric method Bradfordizing for information retrieval (IR) experiments. Bradfordizing is used for generating core document sets for subject-specific questions and to reorder result sets from distributed searches. The method will be applied and tested in a controlled scenario of scientific literature databases from social and political sciences, economics, psychology and medical science (SOLIS, SoLit, USB Köln Opac, CSA Sociological Abstracts, World Affairs Online, Psyndex and Medline) and 164 standardized topics. An evaluation of the method and its effects is carried out in two laboratory-based information retrieval experiments (CLEF and KoMoHe) using a controlled document corpus and human relevance assessments. The results show that Bradfordizing is a very robust method for re-ranking the main document types (journal articles and monographs) in todayâs digital libraries (DL). The IR tests show that relevance distributions after re-ranking improve at a significant level if articles in the core are compared with articles in the succeeding zones. The items in the core are significantly more often assessed as relevant, than items in zone 2 (z2) or zone 3 (z3). The improvements between the zones are statistically significant based on the Wilcoxon signed-rank test and the paired T-Test
Visualization of Co-authorshipin DIT Arrow
With the popularization of information technology and the unprecedented development of online reading, the management and service of the library are facing severe challenges; the traditional library operation mode has been challenging to optimize the service. At the same time, there is also a fatal impact on library collection and systematic management, however, with the development of visualization techniques in management and service, the library can alleviate the eïŹect of the current network information basically, which achieves the intellectual development of library ïŹeld. This study empirically provides the evidence to indicate that the force directed layout has the statistically signiïŹcant performance than the radial layout for visualization of co-authorship in DIT Arrow repository based on the results of surveys
Knowledge-Based Techniques for Scholarly Data Access: Towards Automatic Curation
Accessing up-to-date and quality scientific literature is a critical preliminary step in any research activity.
Identifying relevant scholarly literature for the extents of a given task or application is, however a complex and time consuming activity.
Despite the large number of tools developed over the years to support scholars in their literature surveying activity, such as Google Scholar, Microsoft Academic search, and others, the best way to access quality papers remains asking a domain expert who is actively involved in the field and knows research trends and directions.
State of the art systems, in fact, either do not allow exploratory search activity, such as identifying the active research directions within a given topic, or do not offer proactive features, such as content recommendation, which are both critical to researchers.
To overcome these limitations, we strongly advocate a paradigm shift in the development of scholarly data access tools: moving from traditional information retrieval and filtering tools towards automated agents able to make sense of the textual content of published papers and therefore monitor the state of the art.
Building such a system is however a complex task that implies tackling non trivial problems in the fields of Natural Language Processing, Big Data Analysis, User Modelling, and Information Filtering.
In this work, we introduce the concept of Automatic Curator System and present its fundamental components.openDottorato di ricerca in InformaticaopenDe Nart, Dari
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