바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

  • P-ISSN1013-0799
  • E-ISSN2586-2073
  • KCI

A Study on the Visual Representation of TREC Text Documents in the Construction of Digital Library

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2004, v.21 no.3, pp.1-14
https://doi.org/10.3743/KOSIM.2004.21.3.001


Abstract

Visualization of documents will help users when they do search similar documents, and all research in information retrieval addresses itself to the problem of a user with an information need facing a data source containing an acceptable solution to that need. In various contexts, adequate solutions to this problem have included alphabetized cubbyholes housing papyrus rolls, microfilm registers, card catalogs and inverted files coded onto discs. Many information retrieval systems rely on the use of a document surrogate. Though they might be surprise to discover it, nearly every information seeker uses an array of document surrogates. Summaries, tables of contents, abstracts, reviews, and MARC recordsthese are all document surrogates. That is, they stand infor a document allowing a user to make some decision regarding it, whether to retrieve a book from the stacks, whether to read an entire article, etc.In this paper another type of document surrogate is investigated using a grouping method of term list. Using Multidimensional Scaling Method (MDS) those surrogates are visualized on two-dimensional graph. The distances between dots on the two-dimensional graph can be represented as the similarity of the documents. More close the distance, more similar the documents.

keywords
문헌 대체, 문헌간 유사성, 정보검색시스템, DL, MDS, 용어집단화방법Document Surrogate, Document Similarity, IRS, DL, Multidimensional Scaling (MDS), Term Grouping Method, 문헌 대체, 문헌간 유사성, 정보검색시스템, DL, MDS, 용어집단화방법Document Surrogate, Document Similarity, IRS, DL, Multidimensional Scaling (MDS), Term Grouping Method

Reference

1.

박일종. (2000). 디지털 도서관시대에 대비한 도서관자동화시스템의 비교효용성과 개발방향에 대한 연구. 정보관리학회지, 17(2), 207-231.

2.

Bartolucci, Alfred. (1986). Multidimensio- nal Scaling and the Information it Conveys.. American Journal of Public Health, 76(7), 747-771.

3.

Gazda, George. (1994). Multidimensional Scaling for the 21st Century. Journal of Group Psychotherapy, Psychodrama & Sociometry., 47(2), -.

4.

Goodrum, A. (2000). An Open Source Agenda for Research Linking Text and Image Content Features.. Journal of the American Society for Information Science., , -.

5.

Jones, Karen Sparck. (2000). Further reflections on TREC.. Information Processing and Management, 36, 37-85.

6.

Khorfage, Robert B. (1997). Information Storage and Retrieval.:New York: John Wiley..

7.

Lancaster, F.W. (1998). Indexing and Abstracting in Theory and Practice.:Champaign, IL: University of Illinois..

8.

Rorvig, M. (1997). Visualization and Scaling of TREC topic document sets.. International Journal of Information Processing and Management, , -.

9.

Rorvig, M. (1998). A visualization case study of feature vectorand stemmer effects on TREC topic-document Proceedings of the 1998 Annual Meeting of the American Society for Information Science..

10.

Rorvig, M. (2000). Shape recovery: a visual method for evaluation of information retrieval experiments.. Journal of the American Society for Information Science,, 51(13), 1205-1210.

11.

Voorhees, Ellen M. (2000). Overview of the Sixth Text Retrieval Conference (TREC-6). Information Processing and Management,, 36, 3-35.

12.

Young, F.W. (1987). Multidimensional scaling: History, theory and applications. Hillsdale:NJ: Erlbaum..

Journal of the Korean Society for Information Management