IRList Digest Tuesday, 23 June 1987 Volume 3 : Issue 14 Today's Topics: Abstracts - IR-Related Dissertation Abstracts (part 1 of 2) News addresses are ARPANET: fox@vtopus.cs.vt.edu BITNET: foxea@vtvax3.bitnet CSNET: fox@vt UUCPNET: seismo!vtisr1!irlistrq ---------------------------------------------------------------------- Date: Sat, 20 Jun 87 19:11:08 EDT From: Susanne Humphrey Subject: dissertation abstracts for SIGIR Forum Ed, appended is edition of Selected IR-Related Dissertation Abstracts. There are 10 of them... Selected IR-Related Dissertation Abstracts Compiled by: Susanne M. Humphrey, National Library of Medicine, Bethesda, MD 20894 The following are citations selected by title and abstract as being related to Information Retrieval (IR), resulting from a computer search, using the BRS Information Technologies retrieval service, of the Dissertation Abstracts International (DAI) database produced by University Microfilms International. Included are the UM order number and year-month of entry into the database; author; university, degree, and, if available, number of pages; title; DAI subject category chosen by the author of the dissertation; and abstract. References are sorted first by DAI subject category and second by author. Citations denoted by an MAI reference do not yet have abstracts in the database and refer to abstracts in the published Masters Abstracts International. [Note: I have added the SO line from another file sent by Susanne, so you also have the DAI reference for finding the abstract in the published DAI. - Ed] Unless otherwise specified, paper or microform copies of dissertations may be ordered from University Microfilms International, Dissertation Copies, Post Office Box 1764, Ann Arbor, MI 48106; telephone for U.S. (except Michigan, Hawaii, Alaska): 1-800-521-3042, for Canada: 1-800-268-6090. Price lists and other ordering and shipping information are in the introduction to the published DAI. An alternate source for copies is sometimes provided at the end of the abstract. The dissertation titles and abstracts contained here are published with permission of University Microfilms International, publishers of Dissertation Abstracts International (copyright 1985) by University Microfilms International), and may not be reproduced without their prior permission. AN University Microfilms Order Number ADG87-02684. AU BELEW, RICHARD KUEHN. SO DAI v47(10), SecB, pp4216. IN The University of Michigan Ph.D. 1986, 328 pages. TI Adaptive information retrieval: machine learning in associative networks. DE Computer Science. AB One interesting issue in artificial intelligence (AI) currently is the relative merits of, and relationship between, the "symbolic" and "connectionist" approaches to intelligent systems building. The performance of more traditional symbolic systems has been striking, but getting these systems to learn truly new symbols has proven difficult. Recently, some researchers have begun to explore a distinctly different type of representation, similar in some respects to the nerve nets of several decades past. In these massively parallel, connectionist models, symbols arise implicitly, through the interactions of many simple and sub-symbolic elements. One of the advantages of using such simple elements as building blocks is that several learning algorithms work quite well. The range of application for connectionist models has remained limited, however, and it has been difficult to bridge the gap between this work and standard AI. The AIR system represents a connectionist approach to the problem of free-text information retrieval (IR). Not only is this an increasingly important type of data, but it provides an excellent demonstration of the advantages of connectionist mechanisms, particularly adaptive mechanisms. AIR's goal is to build an indexing structure that will retrieve documents that are likely to be found relevant. Over time, by using users' browsing patterns as an indication of approval, AIR comes to learn what the keywords (symbols) mean so as use them to retrieve appropriate documents. AIR thus attempts to bridge the gap between connectionist learning techniques and symbolic knowledge representations. The work described was done in two phases. The first phase concentrated on mapping the IR task into a connectionist network; it is shown that IR is very amenable to this representation. The second, more central phase of the research has shown that this network can also adapt. AIR translates the browsing behaviors of its users into a feedback signal used by a Hebbian-like local learning rule to change the weights on some links. Experience with a series of alternative learning rules are reported, and the results of experiments using human subjects to evaluate the results of AIR's learning are presented. AN University Microfilms Order Number ADG87-01283. AU YODER, CORNELIA MARIE. SO DAI v47(09), SecB, pp3858. IN Syracuse University Ph.D. 1986, 383 pages. TI An expert system for providing on-line information based on knowledge of individual user characteristics. DE Computer Science. AB In many interactive systems which provide information, such as HELP systems, the form and content of the information presented always seems to satisfy some people and frustrate others. Human Factors textbooks and manuals for interactive systems focus on the need for consistency and adherence to some standard. This implicitly assumes that if the optimum format and level of detail could be found for presenting information to a user, interactive systems would only need to adhere to the standard to be optimum for everyone. This approach neglects one of the most important factors of all--differences in people. If these individualizing differences in people could be identified, a system could be designed with options built into it to accommodate different users. The role of the intelligent active system should be more like that of a human expert or consultant, who answers questions by first interpreting them in terms of the user's knowledge and the context of his activities and then recommending actions which may be otherwise unknown to the user. The HELP system developed in this study is an Expert System written in PROLOG which uses logic programming rules to intelligently provide needed information to a terminal user. It responds to a request with a full screen display containing information determined by the request, the user's cognitive style and the user's experience level. The investigation studies the relationship between some cognitive style and experience level parameters and individual preferences and efficacy with an interactive computer information system. These factors are measured by the ability of an individual user to perform unfamiliar tasks using a HELP function as information source. The format of the information provided by the HELP function is varied along three dimensions and the content of the information is varied by three levels of detail. Experiments were performed with the system and experimental results are presented which show some trends relating cognitive style and individual preferences and performance using the system. In addition, it is argued that an Expert System can perform such a function effectively. AN University Microfilms Order Number ADG87-01260. AU EISENBERG, MICHAEL BRUCE. SO DAI v47(09), SecA, pp3219. IN Syracuse University Ph.D. 1986, 324 pages. TI Magnitude estimation and the measurement of relevance. DE Information Science. AB A study was designed to investigate the use of the scaling technique of magnitude estimation for the measurement of relevance judgments. Relevance is fundamental to the information process and to the purpose, design, and use of information systems. The relevance judgment is a focal point in system evaluation and research. The method of magnitude estimation, an open-ended scaling technique, was developed in the field of psychophysics for the direct measurement of human response to various sensory stimuli. Magnitude estimation has been successfully applied to a wide range of situations requiring human judgments, often resulting in the development of new viewpoints and understandings. Questions were raised regarding (1) the use of scaling procedures, (2) the distribution of scaled responses, (3) biases in scaling, and (4) whether relevance could be viewed within a stimulus-response framework. Four experiments were designed to test magnitude estimation under different conditions and in comparison to a standard 7-point category rating procedure. The major results indicate that magnitude judgments can be used for the measurement of relevance. Furthermore, relevance judgments seem to behave as do other quantitative continua. When category rating judgments are plotted against magnitude estimation judgments of relevance, a predictable, concave downward pattern is observed. AN University Microfilms Order Number ADG87-02080. AU RITTENHOUSE, ROBERT JOHN. SO DAI v47(10), SecA, pp3598. IN Case Western Reserve University Ph.D. 1986, 298 pages. TI A composite measure for weighting databases in defense, engineering, and science. DE Information Science. AB The primary problem of this dissertation is to propose a composite measure as a technique for measuring the relevancy of databases. The databases are characterized as single units by the measure of closeness, C(,M), values. The measure of closeness consists of two weighted factors: (1) a relevance factor, and (2) a descriptive factor. The relevance factor is the sum of the recall and precision ratios. The descriptive factor is the sum of the weighted properties of each file as follows: (1) subject coverage, (2) thesaurus strength, (3) technical level, (4) subject coding, and (5) length of years searched retrospectively. Two experiments were conducted to test if the measure of closeness may be utilized to select the relevant databases in DIALINDEX searches in the general areas of defense, engineering, and science. Databases from Dialog Information Services, Inc., Defense Logistics Studies Information Exchange, Defense Technical Information Center, Mead Data Central Nexis, NASA/RECON, and DOE/RECON were also used. Searches were conducted in seven sample topics: (1) composites, (2) missiles, (3) rockets, (4) sonar, (5) torpedoes, (6) underwater acoustics, and (7) underwater weapons. For each of the seven topics, online searches were performed on a group of databases. These databases, ranked according to C(,M) values, were compared with their corresponding databases ranked by retrievals from DIALINDEX, a Dialog multidatabase file. The first experiment compared six randomly selected Dialog files and Dialog files subjectively selected for their expected higher relevance to the topics. While randomly selected files retrieved some relevant citations, these files generally did not contain many relevant citations. The second experiment compared the DIALINDEX method and the measure of closeness, C(,M), technique. Mann-Whitney two rank and Spearman Rho rank correlation tests failed to indicate conclusively that the DIALINDEX method is different from use of the weighted measure of closeness alone. The tests did indicate DIALINDEX term frequency retrievals appear to result in ranking relevant databases. Possible artificial intelligence designs may further enhance the future modelling of weighting schemes for more effective multivendor and multidatabase online search techniques. Only unclassified terms, titles and/or abstracts were discussed in order to conform to U.S. national security requirements. [Note: rest will be in Issue 15 - Ed] ------------------------------ END OF IRList Digest ********************