This research aims to incorporate the results of co-word analysis into information retrieval as a means to increase search variety for end users in the domain of information retrieval. Relevant data were first collected from Science Citation Index and Social Sciences Citation Index for the period 1987-1997. The results of co-word analysis on the data were compared with similar data obtained from three thesauri: Library and Information Science Abstracts thesaurus, Library of Congress Subject Headings and Thesaurus of Information Technology Terms. The differences detected between them indicate that the search variety may be increased by combining co-word analysis with the use of traditional thesauri. Subsequently, the results of co-word analysis were compared with each other for two different periods (1987-1991 and 1992-1997). The changes among them were identified, implying that co-word analysis may be used directly to identify dynamic changes in its chosen domain area, thereby providing better up-to-date information to aid the information search process.
Get full access to this article
View all access options for this article.
References
1.
[1] J. Aitchison and A. Gilchrist , Thesaurus Construction: A Practical Manual3rd ed. ( Aslib, London , 1997).
2.
[2] M.J. Bates , Subject access in online catalogs: a design model , Journal of the American Society for Information Science37 (1986) 357-376 .
3.
[3] M.J. Bates , Indexing and access for digital libraries and the Internet: human, database, and domain factors , Journal of the American Society for Information Science49(13) (1998) 1185-1205 .
4.
[4] C.L. Borgman (ed.), Scholarly Communication and Bibliometrics ( Sage, Newbury Park, CA , 1990).
5.
[5] R.T. Braam , H.F. Moed and A.F.J. van Raan , Mapping of science by combined cocitation and word analysis. II: Dynamical aspects , Journal of the American Society for Information Science42(4) (1991) 252-266 .
6.
[6] C.C. Byrne and S.A. McCracken , An adaptive thesaurus employing semantic distance, relational inheritance and nominal compound interpretation for linguistic support of information retrieval , Journal of Information Science25(2) (1999) 113-131 .
7.
[7] A. Cambrosio , C. Limoges , J.P. Courtial and F. Laville , Historical scientometrics? Mapping over 70 years of biological safety research with co-word analysis , Scientometrics27(2) (1993) 119-143 .
8.
[8] H. Chen and V. Dhar , Cognitive process as a basis for intelligent retrieval systems design , Information Processing and Management27(5) (1991) 405-432 .
9.
[9] H. Chen and T. Ng , An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation): symbolic branch-and-bound search vs. connectionist hopfield net activation , Journal of the American Society for Information Science46(5) (1995) 348-369 .
10.
[10] H. Chen and K.J. Lynch , Automatic construction of networks of concepts characterizing document databases , IEEE Transactions on Systems, Man, and Cybernetics22(5) (1992) 885-902 .
11.
[11] H. Chen , T.D. Ng , J. Martinez and B.R. Schatz , A concept space approach to addressing the vocabulary problem in scientific information retrieval: an experiment on the worm community system , Journal of the American Society for Information Science48(1) (1997) 17-31 .
12.
[12] H. Chen , J. Martinez , A. Kirchhoff , T.D. Ng and B.R. Schatz , Alleviating search uncertainty through concept associations: automatic indexing, co-occurrence analysis, and parallel computing , Journal of the American Society for Information Science49(3) (1998) 206-216 .
13.
[13] H. Chen , T. Yim , D. Fye and B. Schatz , Automatic thesaurus generation for an electronic community system , Journal of the American Society for Information Science46(3) (1995) 175-193 .
14.
[14] H. Chu , Communication between Chinese and non-Chinese scientists in discovery of high-Tc superconductor: I. The formal perspective , Scientometrics25(2) (1992) 229-252 .
15.
[15] N. Coulter , I. Monarch and S. Konda , Software engineering as seen through its research literature: a study in co-word analysis , Journal of the American Society for Information Science49(13) (1998) 1206-1223 .
16.
[16] J.P. Courtial , A coword analysis of scientometrics , Scientometrics31(3) (1994) 251-260 .
17.
[17] J.P. Courtial , T. Cahlik and M. Callon , A model for social interaction between cognition and action through a keyword simulation of knowledge growth , Scientometrics31(2) (1994) 173-192 .
18.
[18] C.J. Crouch , An approach to the automatic construction of global thesauri , Information Processing and Management26 (1990) 629-640 .
19.
[19] M. De Looze and J. Lemarie , Corpus relevance through co-word analysis: an application to plant proteins , Scientometrics39(3) (1997) 267-280 .
20.
[20] B. Everitt , Cluster Analysis2nd ed. ( Heinemann, London , 1980).
21.
[21] L.M. Gomez , C.C. Lochbaum and T.K. Landauer , All the right words: finding what you want as a function of richness of indexing vocabulary , Journal of the American Society for Information Science41(8) (1990) 547-559 .
22.
[22] J.G. Gregory , Citation study of peripheral theories in an expanding research front , Journal of Information Science7 (1983) 73-80 .
23.
[23] L. Hamers , et al. , Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula , Information Processing and Management25(3) (1989) 315-318 .
24.
[24] S.P. Harter , Psychological relevance and information science , Journal of the American Society for Information Science43 (1992) 602-615 .
25.
[25] S.P. Harter and Y.R. Cheng , Colinked descriptors: improving vocabulary selection for end-user searching , Journal of the American Society for Information Science47(4) (1996) 311-325 .
26.
[26] J. King , A review of bibliometric and other science indicators and their role in research evaluation , Journal of Information Science13 (1987) 261-276 .
27.
[27] T.Y. Lau , Chinese communication studies: a citation analysis of Chinese communication research in English-language journals , Scientometrics33(1) (1995) 65-91 .
28.
[28] J. Law and J. Whittaker , Mapping acidification research: a test of the co-word method , Scientometrics23(3) (1991) 417-461 .
29.
[29] K.W. McCain , Mapping authors in intellectual space: a technical overview , Journal of the American Society for Information Science41(6) (1990) 433-443 .
[31] E.C.M. Noyons and A.F.J. van Raan , Monitoring scientific developments from a dynamic perspective: self-organized structuring to map neural network research , Journal of the American Society for Information Science49(1) (1998) 68-81 .
32.
[32] E.C.M. Noyons , H.F. Moed and M. Luwel , Combining mapping and citation analysis for evaluative biblio-metric purposes: a bibliometric study , Journal of the American Society for Information Science50(2) (1999) 115-131 .
33.
[33] H.J. Peat and P. Willett , The limitation of term co-occurrence data for query expansion in document retrieval systems , Journal of the American Society for Information Science42(5) (1991) 378-383 .
34.
[34] H.P.F. Peters , R.R. Braam and A.F.J. van Raan , Cognitive resemblance and citation relations in chemical engineering publications , Journal of the American Society for Information Science46(1) (1995) 9-21 .
35.
[35] L. Quoniam , F. Balme , H. Rostaing , E. Giraud and J.M. Dou , Bibliometric law used for information retrieval , Scientometrics41(1-2) (1998) 83-91 .
36.
[36] G. Salton , Automatic Text Processing ( Addison-Wesley, Reading, MA , 1989).
37.
[37] G. Salton , A. Wong and C.S. Yang , A vector space model for automatic indexing , Communications of the ACM18 (1975) 613-620 .
38.
[38] T. Saracevic and P. Kantor , A study of information seeking and retrieving. III. Searchers, searches, and overlap , Journal of the American Society for Information Science39 (1988) 197-216 .
[40] R. Shalini , ‘Citation profiles’ to improve relevance in a two-stage retrieval system: a proposal , Information Processing and Management29(4) (1993) 463-470 .
41.
[41] C.J. Van Rijsbergen , Information Retrieval ( Butterworths, London , 1979).
