A concept space approach to addressing the vocabulary problem in scientific information retrieval: an experiment on the worm community system

This research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive stud!es related to the vcrcabulaw problem and vocabulary-based search aids (thesauri) and then discuss technques for building robust and domain-specific thesauri to assist in cross-domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we racentiy conducted an experiment in the molecular biology domain in whch we created a C. eksgans worm thesaurus of 7,657 worm-specific terms and a Drosophila fty thesaurus of 15,626 terms. About 30% of these terms overtappad, which created vocabulary paths from one subject domain to the other. Based on a cognitive study of term association involving four biologists, we found that a large percentage (59.6-85.6”A ) of the terms suggested by the subjects were identified in the conjoined fly-worm thesaurus. However, we found only a small parentage (6.4-18.1 %) of the associations suggested by the subjects in the thesaurus. In a follow-up document retrieval study involving eight fly biologists, an actual worm database (Worm Community System), and the conjoined flywonn thesaurus, subjects were able to find more relevant documents (an increase from about 9 documents to 20) and to improve the document recall level (from 32.41 to 65.28% ) when using the thesaurus, although the precision level did not improve significantly. Implications of adopting the concept space approach for addressing the vocabulary

[1]  Edward A. Fox,et al.  Building a Large Thesaurus for Information Retrieval , 1988, ANLP.

[2]  Kevin Knight,et al.  Connectionist ideas and algorithms , 1990, CACM.

[3]  Micheline Hancock-Beaulieu,et al.  Query expansion: advances in research in online catalogues , 1992, J. Inf. Sci..

[4]  J. Pomian,et al.  A system based on associational logic for the interrogation of databases , 1987, J. Inf. Sci..

[5]  Gerard Salton,et al.  Generation and search of clustered files , 1978, TODS.

[6]  Alice Yanosko Chamis Vocabulary Control and Search Strategies in Online Searching , 1991 .

[7]  Carolyn L. Foss,et al.  Tools for reading and browsing hypertext , 1989, Inf. Process. Manag..

[8]  Sara D. Knapp,et al.  Creating BRS/TERM, a Vocabulary Database for Searchers. , 1984 .

[9]  Peter Willett,et al.  Criteria for the Selection of Search Strategies in Best-Match Document-Retrieval Systems , 1986, Int. J. Man Mach. Stud..

[10]  Nicholas J. Belkin,et al.  Ask for Information Retrieval: Part II. Results of a Design Study , 1982, J. Documentation.

[11]  K. J. Lynch,et al.  Automatic construction of networks of concepts characterizing document databases , 1992, IEEE Trans. Syst. Man Cybern..

[12]  Louis M. Gomez,et al.  All the Right Words: Finding What You Want as a Function of Richness of Indexing Vocabulary. , 1990 .

[13]  Laurence C. Rosenberg National Science Foundation News , 1992, SGAR.

[14]  R. T. Niehoff Development of an Integrated Energy Vocabulary and the Possibilities for On-line Subject Switching , 1976, J. Am. Soc. Inf. Sci..

[15]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[16]  Peter Willett,et al.  The Limitations of Term Co-Occurrence Data for Query Expansion in Document Retrieval Systems , 1991 .

[17]  M. E. Maron,et al.  An evaluation of retrieval effectiveness for a full-text document-retrieval system , 1985, CACM.

[18]  Hsinchun Chen,et al.  Cognitive process as a basis for intelligent retrieval systems design , 1991, Inf. Process. Manag..

[19]  Peretz Shoval,et al.  Principles, procedures and rules in an expert system for information retrieval , 1985, Inf. Process. Manag..

[20]  Susan T. Dumais,et al.  The vocabulary problem in human-system communication , 1987, CACM.

[21]  Arkalgud Ramaprasad,et al.  Cognitive process as a basis for MIS and DSS design , 1987 .

[22]  Brian Everitt,et al.  Cluster analysis , 1974 .

[23]  Micheline Hancock-Beaulieu,et al.  Interactive thesaurus navigation: intelligence rules ok? , 1995 .

[24]  Bruce R. Schatz,et al.  Semantic Retrieval for the NCSA Mosaic , 1994 .

[25]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[26]  S. Perschke,et al.  AUTOMATIC THESAURUS CONSTRUCTION FOR INFORMATION RETRIEVAL , 1972 .

[27]  John R. Anderson Cognitive Psychology and Its Implications , 1980 .

[28]  Elaine Rich Users are individuals: individualizing user models , 1999, Int. J. Hum. Comput. Stud..

[29]  R. T. Niehoff,et al.  The role of automated subject switching in a distributed information network , 1979 .

[30]  Charles J. Fillmore,et al.  THE CASE FOR CASE. , 1967 .

[31]  Hsinchun Chen,et al.  Browsing in hypertext: a cognitive study , 1992, IEEE Trans. Syst. Man Cybern..

[32]  Margaret Chaplan Mapping "Laborline Thesaurus" Terms to Library of Congress Subject Headings: Implications for Vocabulary Switching , 1995, The Library Quarterly.

[33]  Hsinchun Chen,et al.  An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation): symbolic branch-and-bound search vs. connectionist Hopfield net activation , 1995 .

[34]  F. W. Lancaster,et al.  Vocabulary control for information retrieval , 1972 .

[35]  Carolyn J. Crouch,et al.  An approach to the automatic construction of global thesauri , 1990, Inf. Process. Manag..

[36]  J. Dalton,et al.  Artificial neural networks , 1991, IEEE Potentials.

[37]  Paul R. Cohen,et al.  Information retrieval by constrained spreading activation in semantic networks , 1987, Inf. Process. Manag..

[38]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[39]  Martha W. Evens,et al.  Generating a Relational Lexicon from a Machine–Readable Dictionary* , 1988 .

[40]  Lauren B. Doyle,et al.  Indexing and abstracting by association , 1962 .

[41]  Carolyn J. Crouch,et al.  Experiments in automatic statistical thesaurus construction , 1992, SIGIR '92.

[42]  Toni Petersen Developing a New Thesaurus for Art and Architecture , 1990 .

[43]  Nicholas J. Belkin,et al.  Ask for Information Retrieval: Part I. Background and Theory , 1997, J. Documentation.

[44]  Bruce R. Schatz,et al.  Building an Electronic Community System , 1991, J. Manag. Inf. Syst..

[45]  Hsinchun Chen,et al.  Reducing Indeterminism in Consultation: A Cognitive Model of User/Librarian Interactions , 1987, AAAI.

[46]  Hsinchun Chen,et al.  Automatic Thesaurus Generation for an Electronic Community System , 1995, J. Am. Soc. Inf. Sci..

[47]  Edward A. Felgenbaum The art of artificial intelligence: themes and case studies of knowledge engineering , 1977, IJCAI 1977.

[48]  B R Schatz,et al.  The Worm Community System, release 2.0 (WCSr2). , 1995, Methods in cell biology.

[49]  Jin H. Kim,et al.  A Model of Knowledge Based Information Retrieval with Hierarchical Concept Graph , 1990, J. Documentation.

[50]  Gerald Salton,et al.  Automatic text processing , 1988 .

[51]  Marcia J. Bates,et al.  Subject access in online catalogs: A design model , 1986 .

[52]  C. J. van Rijsbergen,et al.  The selection of good search terms , 1981, Inf. Process. Manag..

[53]  Jaime G. Carbonell,et al.  CoalSORT: A Knowledge-Based Interface , 1987, IEEE Expert.

[54]  John R. Anderson Cognitive psychology and its implications, 2nd ed. , 1985 .

[55]  Allen Newell,et al.  The psychology of human-computer interaction , 1983 .

[56]  Edward A. Fox,et al.  Development of the coder system: A testbed for artificial intelligence methods in information retrieval , 1987, Inf. Process. Manag..

[57]  Betsy L. Humphreys,et al.  The UMLS Knowledge Sources: Tools for Building Better User Interfaces. , 1990 .

[58]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[59]  Anne B. Piternick Searching vocabularies: a developing category of online search tools , 1984 .

[60]  Susan T. Dumais,et al.  Statistical semantics: How can a computer use what people name things to guess what things people mean when they name things? , 1982, CHI '82.

[61]  Jack Minker,et al.  An evaluation of query expansion by the addition of clustered terms for a document retrieval system , 1972, Inf. Storage Retr..

[62]  W. Bruce Croft,et al.  Experiments with query acquisition and use in document retrieval systems , 1989, SIGIR '90.

[63]  Karen A. Frenkel,et al.  The human genome project and informatics , 1991, CACM.

[64]  Alan F. Smeaton,et al.  The Retrieval Effects of Query Expansion on a Feedback Document Retrieval System , 1983, Comput. J..

[65]  Michael Lesk,et al.  Word-word associations in document retrieval systems , 1969 .

[66]  Edie M. Rasmussen,et al.  Clustering Algorithms , 1992, Information Retrieval: Data Structures & Algorithms.

[67]  K. J. Lynch,et al.  Generating, integrating, and activating thesauri for concept-based document retrieval , 1993, IEEE Expert.

[68]  Jay F. Nunamaker,et al.  Automatic concept classification of text from electronic meetings , 1994, CACM.

[69]  J J Hopfield,et al.  Collective computation in neuronlike circuits. , 1987, Scientific American.

[70]  H. Edmund Stiles,et al.  The Association Factor in Information Retrieval , 1961, JACM.

[71]  Peter Willett,et al.  Effectiveness of query expansion in ranked-output document retrieval systems , 1992, J. Inf. Sci..