Search (3 results, page 1 of 1)

  • × author_ss:"Hui, S.C."
  • × theme_ss:"Citation indexing"
  1. He, Y.; Hui, S.C.: Mining a web database for author cocitation analysis (2002) 0.00 
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    Type
    a
  2. He, Y.; Hui, S.C.: PubSearch : a Web citation-based retrieval system (2001) 0.00 
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    Abstract
    Many scientific publications are now available on the World Wide Web for researchers to share research findings. However, they tend to be poorly organised, making the search of relevant publications difficult and time-consuming. Most existing search engines are ineffective in searching these publications, as they do not index Web publications that normally appear in PDF (portable document format) or PostScript formats. Proposes a Web citation-based retrieval system, known as PubSearch, for the retrieval of Web publications. PubSearch indexes Web publications based on citation indices and stores them into a Web Citation Database. The Web Citation Database is then mined to support publication retrieval. Apart from supporting the traditional cited reference search, PubSearch also provides document clustering search and author clustering search. Document clustering groups related publications into clusters, while author clustering categorizes authors into different research areas based on author co-citation analysis.
    Type
    a
  3. Tho, Q.T.; Hui, S.C.; Fong, A.C.M.: ¬A citation-based document retrieval system for finding research expertise (2007) 0.00 
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    Abstract
    Current citation-based document retrieval systems generally offer only limited search facilities, such as author search. In order to facilitate more advanced search functions, we have developed a significantly improved system that employs two novel techniques: Context-based Cluster Analysis (CCA) and Context-based Ontology Generation frAmework (COGA). CCA aims to extract relevant information from clusters originally obtained from disparate clustering methods by building relationships between them. The built relationships are then represented as formal context using the Formal Concept Analysis (FCA) technique. COGA aims to generate ontology from clusters relationship built by CCA. By combining these two techniques, we are able to perform ontology learning from a citation database using clustering results. We have implemented the improved system and have demonstrated its use for finding research domain expertise. We have also conducted performance evaluation on the system and the results are encouraging.
    Type
    a

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