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

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

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

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

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

[6]  Louis M. Gomez,et al.  All the right words: Finding what you want as a function of richness of indexing vocabulary , 1990, J. Am. Soc. Inf. Sci..

[7]  R Pool,et al.  Beyond databases and E-mail. , 1993, Science.

[8]  Peter Willett,et al.  The limitations of term co-occurrence data for query expansion in document retrieval systems , 1991, J. Am. Soc. Inf. Sci..

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

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

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

[12]  P. Sopp Cluster analysis. , 1996, Veterinary immunology and immunopathology.

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

[14]  Lois L. Earl,et al.  Experiments in automatic extracting and indexing , 1970, Inf. Storage Retr..

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

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

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

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

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

[20]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

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

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

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

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

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

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

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

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

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

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

[31]  H. 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, J. Am. Soc. Inf. Sci..

[32]  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.

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

[34]  Alexa T. McCray,et al.  Concepts, Issues, and Standards. Current Status of the NLM's Umls Project: The Scope and Structure of the First Version of the UMLS Seoantic Network , 1990 .

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

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

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

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

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

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

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

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

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

[44]  J Courteau Genome databases. , 1991, Science.

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

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