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IRList Digest Volume 3 Number 14
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
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Date: Sat, 20 Jun 87 19:11:08 EDT
From: Susanne Humphrey <humphrey@mcs.nlm.nih.gov>
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]
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END OF IRList Digest
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