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  • × author_ss:"Qin, J."
  1. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.03
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    Abstract
    The growing predominance of social semantics in the form of tagging presents the metadata community with both opportunities and challenges as for leveraging this new form of information content representation and for retrieval. One key challenge is the absence of contextual information associated with these tags. This paper presents an experiment working with Flickr tags as an example of utilizing social semantics sources for enriching subject metadata. The procedure included four steps: 1) Collecting a sample of Flickr tags, 2) Calculating cooccurrences between tags through mutual information, 3) Tracing contextual information of tag pairs via Google search results, 4) Applying natural language processing and machine learning techniques to extract semantic relations between tags. The experiment helped us to build a context sentence collection from the Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning semantic relations to tag pairs. This paper also explores the implications of this approach for using social semantics to enrich subject metadata.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  2. Qin, J.; Paling, S.: Converting a controlled vocabulary into an ontology : the case of GEM (2001) 0.02
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    Date
    24. 8.2005 19:20:22
  3. Qin, J.; Wesley, K.: Web indexing with meta fields : a survey of Web objects in polymer chemistry (1998) 0.01
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    Abstract
    Reports results of a study of 4 WWW search engines: AltaVista; Lycos; Excite and WebCrawler to collect data on Web objects on polymer chemistry. 1.037 Web objects were examined for data in 4 categories: document information; use of meta fields; use of images and use of chemical names. Issues raised included: whether to provide metadata elements for parts of entities or whole entities only, the use of metasyntax, problems in representation of special types of objects, and whether links should be considered when encoding metadata. Use of metafields was not widespread in the sample and knowledge of metafields in HTML varied greatly among Web object creators. The study formed part of a metadata project funded by the OCLC Library and Information Science Research Grant Program
  4. Qin, J.: ¬A relation typology in knowledge organization systems : case studies in the research data management domain (2018) 0.01
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    Date
    4. 9.2014 14:11:40
  5. Qin, J.: Evolving paradigms of knowledge representation and organization : a comparative study of classification, XML/DTD and ontology (2003) 0.01
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    Date
    12. 9.2004 17:22:35
  6. Qin, J.; Creticos, P.; Hsiao, W.Y.: Adaptive modeling of workforce domain knowledge (2006) 0.01
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    Source
    Knowledge organization for a global learning society: Proceedings of the 9th International ISKO Conference, 4-7 July 2006, Vienna, Austria. Hrsg.: G. Budin, C. Swertz u. K. Mitgutsch
  7. Qin, J.; Zhou, Y.; Chau, M.; Chen, H.: Multilingual Web retrieval : an experiment in English-Chinese business intelligence (2006) 0.01
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    Date
    4. 6.2006 19:58:57
  8. Qin, J.: Semantic patterns in bibliographically coupled documents (2002) 0.00
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    Abstract
    Different research fields have different definitions for semantic patterns. For knowledge discovery and representation, semantic patterns represent the distribution of occurrences of words in documents and/or citations. In the broadest sense, the term semantic patterns may also refer to the distribution of occurrences of subjects or topics as reflected in documents. The semantic pattern in a set of documents or a group of topics therefore implies quantitative indicators that describe the subject characteristics of the documents being examined. These characteristics are often described by frequencies of keyword occurrences, number of co-occurred keywords, occurrences of coword, and number of cocitations. There are many ways to analyze and derive semantic patterns in documents and citations. A typical example is text mining in full-text documents, a research topic that studies how to extract useful associations and patterns through clustering, categorizing, and summarizing words in texts. One unique way in library and information science is to discover semantic patterns through bibliographically coupled citations. The history of bibliographical coupling goes back in the early 1960s when Kassler investigated associations among technical reports and technical information flow patterns. A number of definitions may facilitate our understanding of bibliographic coupling: (1) bibliographic coupling determines meaningful relations between papers by a study of each paper's bibliography; (2) a unit of coupling is the functional bond between papers when they share a single reference item; (3) coupling strength shows the order of combinations of units of coupling into a graded scale between groups of papers; and (4) a coupling criterion is the way by which the coupling units are combined between two or more papers. Kessler's classic paper an bibliographic coupling between scientific papers proposes the following two graded criteria: Criterion A: A number of papers constitute a related group GA if each member of the group has at least one coupling unit to a given test paper P0. The coupling strength between P0 and any member of GA is measured by the number of coupling units n between them. G(subA)(supn) is that portion of GA that is linked to P0 through n coupling units; Criterion B: A number of papers constitute a related group GB if each member of the group has at least one coupling unit to every other member of the group.