Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 04. Juni 2021)
1Hu, K. ; Luo, Q. ; Qi, K. ; Yang, S. ; Mao, J. ; Fu, X. ; Zheng, J. ; Wu, H. ; Guo, Y. ; Zhu, Q.: Understanding the topic evolution of scientific literatures like an evolving city : using Google Word2Vec model and spatial autocorrelation analysis.
In: Information processing and management. 56(2019) no.4, S.1185-1203.
Abstract: Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.
Inhalt: Vgl.: https://doi.org/10.1016/j.ipm.2019.02.014.
Themenfeld: Semantisches Umfeld in Indexierung u. Retrieval
2Zhu, Q. ; Kong, X. ; Hong, S. ; Li, J. ; He, Z.: Global ontology research progress : a bibliometric analysis.
In: Aslib journal of information management. 67(2015) no.1, S.27-54.
Abstract: Purpose - The purpose of this paper is to analyse the global scientific outputs of ontology research, an important emerging discipline that has huge potential to improve information understanding, organization, and management. Design/methodology/approach - This study collected literature published during 1900-2012 from the Web of Science database. The bibliometric analysis was performed from authorial, institutional, national, spatiotemporal, and topical aspects. Basic statistical analysis, visualization of geographic distribution, co-word analysis, and a new index were applied to the selected data. Findings - Characteristics of publication outputs suggested that ontology research has entered into the soaring stage, along with increased participation and collaboration. The authors identified the leading authors, institutions, nations, and articles in ontology research. Authors were more from North America, Europe, and East Asia. The USA took the lead, while China grew fastest. Four major categories of frequently used keywords were identified: applications in Semantic Web, applications in bioinformatics, philosophy theories, and common supporting technology. Semantic Web research played a core role, and gene ontology study was well-developed. The study focus of ontology has shifted from philosophy to information science. Originality/value - This is the first study to quantify global research patterns and trends in ontology, which might provide a potential guide for the future research. The new index provides an alternative way to evaluate the multidisciplinary influence of researchers.
Inhalt: Vgl.: http://dx.doi.org/10.1108/AJIM-09-2014-0112.
3Zhou, G.D. ; Zhang, M. ; Ji, D.H. ; Zhu, Q.M.: Hierarchical learning strategy in semantic relation extraction.
In: Information processing and management. 44(2008) no.3, S.1008-1021.
Abstract: This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in semantic relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a discriminative function is determined in a top-down way. As the upper-level class normally has much more positive training examples than the lower-level class, the corresponding discriminative function can be determined more reliably and guide the discriminative function learning in the lower-level one more effectively, which otherwise might suffer from limited training data. In this paper, two classifier learning approaches, i.e. the simple perceptron algorithm and the state-of-the-art Support Vector Machines, are applied using the hierarchical learning strategy. Moreover, several kinds of class hierarchies either manually predefined or automatically clustered are explored and compared. Evaluation on the ACE RDC 2003 and 2004 corpora shows that the hierarchical learning strategy much improves the performance on least- and medium-frequent relations.
Themenfeld: Automatisches Klassifizieren
4Lee, E.S. ; Zhu, Q.: Fuzzy and evidece reasoning.
Heidelberg : Physica-Verlag, 1995. Xii,360 S.
(Studies in fuzziness; vol.6)
Abstract: This volume gives readers a complete picture of fuzzy approximate reasoning and uncertainty reasoning based on evidence theory. The topics are systematically treated from fundamentals to advanced issues. Numerous examples illustrate the concepts