Information Retrieval List Digest 044 (January 16, 1991) URL = http://hegel.lib.ncsu.edu/stacks/serials/irld/irld-044 IRLIST Digest January 16, 1991 Volume VIII, Number 1 Issue 44 ********************************************************** I. NOTICES A. Calls for papers/Meetings announcements 1. Lexical Semantics and Knowledge Representation June 17, 1991 University of California, Berkekely, California 2. The Eighth International Workshop on Machine Learning June 27-29, 1991 Northwestern University, Evanston, Illinois II. QUERIES B. Requests for information 1.Information needed on data generation IV. PROJECT WORK B. Bibliographies 1. Selected IR-related dissertation abstracts ********************************************************** I. NOTICES I.A.1. Fr: Dave Lewis Re: The Eighth International Workshop on Machine Learning June 27-29, 1991 Northwestern University, Evanston, Illinois [To IRLIST readers: note in particular the workshop on learning in IR. The organizers are hoping to have many submissions from the IR community. Best, Dave Lewis] On behalf of the organizing committee, and the individual workshop committees, we are pleased to announce submission details for the eight workshop tracks that will constitute ML91, the Eighth International Workshop on Machine Learning, to be held at Northwestern University, Evanston, Illinois, USA, June 27-29, 1991. The eight workshops are: o Automated Knowledge Acquisition o Computational Models of Human Learning o Constructive Induction o Learning from Theory and Data o Learning in Intelligent Information Retrieval o Learning Reaction Strategies o Learning Relations o Machine Learning in Engineering Automation Please note that submissions must be made to the workshops individually, at the addresses given below, by March 1, 1991. The Proceedings of ML91 will be published by Morgan Kaufmann. Questions concerning individual workshops should be directed to members of the workshop committees. All other questions should be directed to the program co-chairs at ml91@ils.nwu.edu. Details concerning the individual workshops follow. Larry Birnbaum Gregg Collins Northwestern University The Institute for the Learning Sciences 1890 Maple Avenue Evanston, IL 60201 phone (708) 491-3500 ----------------------------------------------------------------- AUTOMATED KNOWLEDGE ACQUISITION Research in automated knowledge acquisition shares the primary objective of machine learning research: building effective knowledge bases. However, while machine learning focuses on autonomous "knowledge discovery," automated knowledge acquisition focuses on interactive knowledge elicitation and formulation. Consequently, research in automated knowledge acquisition typically stresses different issues, including how to ask good questions, how to learn from problem-solving episodes, and how to represent the knowledge that experts can provide. In addition to the task of classification, which is widely studied in machine learning, automated knowledge acquisition studies a variety of performance tasks such as diagnosis, monitoring, configuration, and design. In doing so, research in automated knowledge acquisition is exploring a rich space of task-specific knowledge representations and problem solving methods. Recently, the automated knowledge acquisition community has proposed hybrid systems that combine machine learning techniques with interactive tools for developing knowledge-based systems. Induction tools in expert system shells are being used increasingly as knowledge acquisition front ends, to seed knowledge engineering activities and to facilitate maintenance. The possibilities of synergistic human-machine learning systems are only beginning to be explored. This workshop will examine topics that span autonomous and interactive knowledge acquisition approaches, with the aim of productive cross-fertilization of the automated knowledge acquisition and machine learning communities. Submissions to the automated knowledge acquisition track should address basic problems relevant to the construction of knowledge-based systems using automated techniques that take advantage of human input or human-generated knowledge sources and provide computational leverage in producing operational knowledge. Possible topics include: o Integrating autonomous learning and focused interaction with an expert. o Learning by asking good questions and integrating an expert's responses into a growing knowledge base. o Using existing knowledge to assist in further knowledge acquisition. o Acquiring, representing, and using generic task knowledge. o Analyzing knowledge bases for validity, consistency, completeness, and efficiency then providing recommendations and support for revision. o Automated assistance for theory / model formation and discovery. o Novel techniques for knowledge acquisition, such as explanation, analogy, reduction, case-based reasoning, model-based reasoning, and natural language understanding. o Principles for designing human-machine systems that integrate the complimentary computational and cognitive abilities of programs and users. Submissions on other topics relating automated knowledge acquisition and autonomous learning are also welcome. Each submission should specify the basic problem addressed, the application task, and the technique for addressing the problem. WORKSHOP COMMITTEE Ray Bareiss (Northwestern Univ.) Bruce Buchanan (Univ. of Pittsburg) Tom Gruber (Stanford Univ.) Sandy Marcus (Boeing) Bruce Porter (Univ. of Texas) David Wilkins (Univ. of Illinois) SUBMISSION DETAILS Papers should be approximately 4000 words in length. Authors should submit six copies, by March 1, 1991, to: Ray Bareiss Northwestern University The Institute for the Learning Sciences 1890 Maple Avenue Evanston, IL 60201 phone (708) 491-3500 Formats and deadlines for camera-ready copy will be communicated upon acceptance. ----------------------------------------------------------------- COMPUTATIONAL MODELS OF HUMAN LEARNING Details concerning this workshop will be forthcoming as soon as possible. ----------------------------------------------------------------- CONSTRUCTIVE INDUCTION Selection of an appropriate representation is critical to the success of most learning systems. In difficult learning problems (e.g., protein folding, word pronunciation, relation learning), considerable human effort is often required to identify the basic terms of the representation language. Constructive induction offers a partial solution to this problem by automatically introducing new terms into the representation as needed. Automatically constructing new terms is difficult because the environment or teacher usually provides only indirect feedback, thus raising the issue of credit assignment. However, as learning systems face tasks of greater autonomy and complexity, effective methods for constructive induction are becoming increasingly important. The objective of this workshop is to provide a forum for the interchange of ideas among researchers actively working on constructive induction issues. It is intended to identify commonalities and differences among various existing and emerging approaches such as knowledge-based term construction, relation learning, theory revision in analytic systems, learning of hidden-units in multi-layer neural networks, rule-creation in classifier systems, inverse resolution, and qualitative-law discovery. Submissions are encouraged in the following topic areas: o Empirical approaches and the use of inductive biases o Use of domain knowledge in the construction and evaluation of new terms o Construction of or from relational predicates o Theory revision in analytic-learning systems o Unsupervised learning and credit assignment in constructive induction o Interpreting hidden units as constructed features o Constructive induction in human learning o Techniques for handling noise and uncertainty o Experimental studies of constructive induction systems o Theoretical proofs, frameworks, and comparative analyses o Comparison of techniques from empirical learning, analytical learning, classifier systems, and neural networks WORKSHOP COMMITTEE Organizing Committee: Program Committee: Christopher Matheus (GTE Laboratories) Chuck Anderson (Colorado State) George Drastal (Siemens Corp.) Gunar Liepins (Oak Ridge National Lab) Larry Rendell (Univ. of Illinois) Douglas Medin (Univ. of Michigan) Paul Utgoff (Univ. of Massachusetts) SUBMISSION DETAILS Papers should be a maximum of 4000 words in length. Authors should include a cover page with authors' names, addresses, phone numbers, electronic mail addresses, paper title, and a 300 (maximum) word abstract. Do not indicate or allude to authorship anywhere within the paper. Send six copies of paper submissions, by March 1, 1991, to: Christopher Matheus GTE Laboratories 40 Sylvan Road, MS-45 Waltham MA 02254 (matheus@gte.com) Formats and deadlines for camera-ready copy will be communicated upon acceptance. ----------------------------------------------------------------- LEARNING FROM THEORY AND DATA Research in machine learning has primarily focused on either (1) inductively generalizing a large collection of training data (empirical learning) or (2) using a few examples to guide transformation of existing knowledge into a more usable form (explanation-based learning). Recently there has been growing interest in combining these two approaches to learning in order to overcome their individual weaknesses. Preexisting knowledge can be used to focus inductive learning and to reduce the amount of training data needed. Conversely, inductive learning techniques can be used to correct imperfections in a system's theory of the task at hand (commonly called "domain theories"). This workshop will discuss techniques for reconciling imperfect domain theories with collected data. Most systems that learn from theory and data can be viewed from the perspective of both data-driven learning (how preexisting knowledge biases empirical learning) and theory-driven learning (how empirical data can compensate for imperfect theories). A primary goal of the workshop will be to explore the relationship between these two complementary viewpoints. Papers are solicited on the following (and related) topics: o Techniques for inductively refining domain theories and knowledge bases. o Approaches that use domain theories to initialize an incremental, inductive-learning algorithm. o Theory-driven design and analysis of scientific experiments. o Systems that tightly couple data-driven and theory-driven learning as complementary techniques. o Empirical studies, on real-world problems, of approaches to learning from theory and data. o Theoretical analyses of the value of preexisting knowledge in inductive learning. o Psychological experiments that investigate the relative roles of prior knowledge and direct experience. WORKSHOP COMMITTEE Haym Hirsh (Rutgers Univ.), hirsh@cs.rutgers.edu Ray Mooney (Univ. of Texas), mooney@cs.utexas.edu Jude Shavlik (Univ. of Wisconsin), shavlik@cs.wisc.edu SUBMISSION DETAILS Papers should be single-spaced and printed using 12-point type. Authors must restrict their papers to 4000 words. Papers accepted for general presentation will be allocated 25 minutes during the workshop and four pages in the proceedings published by Morgan Kaufmann. There will also be a posters session; due to the small number of proceedings pages allocated to each workshop, poster papers will not appear in the Morgan Kaufmann proceedings. Instead, they will be allotted five pages in an informal proceedings distributed at this particular workshop only. Please indicate your preference for general or poster presentation. Also include your mailing and e-mail addresses, as well as a short list of keywords. People wishing to discuss their research at the workshop should submit four (4) copies of a paper, by March 1, 1991, to: Jude Shavlik Computer Sciences Department University of Wisconsin 1210 W. Dayton Street Madison, WI 53706 Formats and deadlines for camera-ready copy will be communicated upon acceptance. ----------------------------------------------------------------- LEARNING IN INTELLIGENT INFORMATION RETRIEVAL The intent of this workshop is to bring together researchers from the Information Retrieval (IR) and Machine Learning (ML) communities to explore areas of common interest. Interested researchers are encouraged to submit papers and proposals for panel discussions. The main focus will be on issues relating learning to the intelligent retrieval of textual data. Such issues include, for example: o Descriptive features, clustering, category formation, and indexing vocabularies in the domain of queries and documents. + Problems of very large, sparse feature sets. + Large, structured indexing vocabularies. + Clustering for supervised learning. + Connectionist cluster learning. + Content theories of indexing, similarity, and relevance. o Learning from failures and explanations: + Dealing with high proportions of negative examples. + Explaining failures and successes. + Incremental query formulation, incremental concept learning. + Exploiting feedback. + Dealing with near-misses. o Learning from and about humans: + Intelligent apprentice systems. + Acquiring and using knowledge about user needs and goals. + Learning new search strategies for differing user needs. + Learning to classify via user interaction. o Information Retrieval as a testbed for Machine Learning. o Particularities of linguistically-derived features. WORKSHOP COMMITTEE Christopher Owens (Univ. of Chicago), owens@gargoyle.uchicago.edu David D. Lewis (Univ. of Chicago), lewis@cs.umass.edu Nicholas Belkin (Rutgers Univ.) W. Bruce Croft (Univ. of Massachusetts) Lawrence Hunter (National Library of Medicine) David Waltz (Thinking Machines Corporation) SUBMISSION DETAILS Authors should submit 6 copies of their papers. Preference will be given to papers that sharply focus on a single issue at the intersection of Information Retrieval and Machine Learning, and that support specific claims with concrete examples and/or experimental data. To be printed in the proceedings, papers must not exceed 4 double-column pages (approximately 4000 words). Researchers who wish to propose a panel discussion should submit 6 copies of a proposal consisting of a brief (one page) description of the proposed topic, followed by a list of the proposed participants and a brief (one to two paragraph) summary of each participant's relevant work. Both papers and panel proposals should be received by March 1, 1991, at the following address: Christopher Owens Department of Computer Science The University of Chicago 1100 East 58th Street Chicago, IL 60637 Phone: (312) 702-2505 Formats and deadlines for camera-ready copy will be communicated upon acceptance. ----------------------------------------------------------------- LEARNING REACTION STRATEGIES The computational complexity of classical planning and the need for real-time response in many applications has led many in AI to focus on reactive systems, that is, systems that can quickly map situations to actions without extensive deliberation. Efforts to hand code such systems have made it clear that when agents must interact with complex environments the reactive mapping cannot be fully specified in advance, but must be adaptable to the agent's particular environment. Systems that learn reaction strategies from external input in a complex domain have become an important new focus within the machine learning community. Techniques used to learn strategies include (but are not limited to): o reinforcement learning o using advice and instructions during execution o genetic algorithms, including classifier systems o compilation learning driven by interaction with the world o sensorimotor learning o learning world models suitable for conversion into reactions o learning appropriate perceptual strategies WORKSHOP COMMITTEE Leslie Kaelbling (Teleos), leslie@teleos.com Charles Martin (Univ. of Chicago), martin@cs.uchicago.edu Rich Sutton (GTE), rich@gte.com Jim Firby (Univ. of Chicago), firby@cs.uchicago.edu Reid Simmons (CMU), reid.simmons@cs.cmu.edu Steve Whitehead (Univ. of Rochester), white@cs.rochester.edu SUBMISSION DETAILS Papers must be kept to four two-column pages (approximately 4000 words) for inclusion in the proceedings. Preference will be given to submissions with a single, sharp focus. Papers must be received by March 1, 1990. Send 3 copies of the paper to: Charles Martin Department of Computer Science University of Chicago 1100 East 58th Street Chicago, IL 60637 Formats and deadlines for camera-ready copy will be communicated upon acceptance. ---------------------------------------------------------------- LEARNING RELATIONS In the past few years, there have been a number of developments in empirical learning systems that learn from relational data. Many applications (e.g. planning, design, programming languages, molecular structures, database systems, qualitative physical systems) are naturally represented in this format. Relations have also been the common language of many advanced learning styles such as analogy, learning plans and problem solving. This workshop is intended as a forum for those researchers doing relational learning to address common issues such as: Representation: Is the choice of representation a relational language, a grammar, a plan or explanation, an uncertain or probabilistic variant, or second order logic? How is the choice extended or restricted for the purposes of expressiveness or efficiency? How are relational structure mapped into neural architectures? Principles: What are the underlying principles guiding the system? For instance: similarity measures to find analogies between relational structures such as plans, "minimum encoding" and other approaches to hypothesis evaluation, the employment of additional knowledge used to constrain hypothesis generation, mechanisms for retrieval or adapation of prior plans or explanations. Theory: What theories have supported the development of the system? For instance, computational complexity theory, algebraic semantics, Bayesian and decision theory, psychological learning theories, etc. Implementation: What indexing, hashing, or programming methodologies have been used to improve performance and why? For instance, optimizing the performance for commonly encountered problems (ala CYC). The committee is soliciting papers that fall into one of three categories: Theoretical papers are encouraged that define a new theoretical framework, prove results concerning programs which carry our constructive or relational learning, or compare theoretical issues in various frameworks. Implementation papers are encouraged that provide sufficient details to allow reimplementation of learning algorithms, and discuss the key time/space complexity details motivating the design. Experimentation papers are encouraged that compare methods or address hard learning problems, with appropriate results and supporting statistics. WORKSHOP COMMITTEE Wray Buntine (RIACS and NASA Ames Research Center), wray@ptolemy.arc.nasa.gov Stephen Muggleton (Turing Institute), steve@turing.ac.uk Michael Pazzani (Univ. of California, Irvine), pazzani@ics.uci.edu Ross Quinlan (Univ. of Sydney), quinlan@cs.su.oz.au SUBMISSION DETAILS Those wishing to present papers at the workshop should submit a paper or an extended abstract, single-spaced on US letter or A4 paper, with a maximum length of 4000 words. Those wishing to attend but not present papers should send a 1 page description of their prior work and current research interests. Three copies should be sent to arrive by March 1, 1991 to: Michael Pazzani ICS Department University of California Irvine, CA 92717 USA Formats and deadlines for camera-ready copy will be communicated upon acceptance. ---------------------------------------------------------------- MACHINE LEARNING IN ENGINEERING AUTOMATION Engineering domains present unique challenges to learning systems, such as handling continuous quantities, mathematical formulas, large problem spaces, incorporating engineering knowledge, and the need for user-system interaction. This session concerns using empirical, explanation-based, case-based, analogical, and connectionist learning techniques to solve engineering problems such as design, planning, monitoring, control, diagnosis, and analysis. Papers should describe new or modified machine learning systems that are demonstrated with real engineering problems and overcome limitations of previous systems. Papers should satisfy one or more of the following criteria: o Present new learning techniques for engineering problems. o Present a detailed case study which illustrates shortcomings preventing application of current machine learning technology to engineering problems. o Present a novel application of existing machine learning techniques to an engineering problem indicating promising areas for applying machine learning techniques to engineering problems. Machine learning programs being used by engineers must meet complex requirements. Engineers are accustomed to working with statistical programs and expect learning systems to handle noise and imprecision in a reasonable fashion. Engineers often prefer rules and classifications of events that are more general than characteristic descriptions and more specific than discriminant descriptions. Engineers have considerable domain expertise and want systems that enable application of this knowledge to the learning task. This session is intended to bring together machine learning researchers interested in real-world engineering problems and engineering researchers interested in solving problems using machine learning technology. We welcome submissions including but not limited to discussions of machine learning applied to the following areas: o manufacturing automation o design automation o automated process planning o production management o robotic and vision applications o automated monitoring, diagnosis, and control o engineering analysis WORKSHOP COMMITTEE Bradley Whitehall (Univ. of Illinois) Steve Chien (JPL) Tom Dietterich (Oregon State Univ.) Richard Doyle (JPL) Brian Falkenhainer (Xerox PARC) James Garrett (CMU) Stephen Lu (Univ. of Illinois) SUBMISSION DETAILS Submission format will be similar to AAAI-91: 12 point font, single-spaced, text and figure area 5.5" x 7.5" per page, and a maximum length of 4000 words. The cover page should include the title of the paper, names and addresses of all the authors, a list of keywords describing the paper, and a short (less than 200 words) abstract. Only hard-copy submissions will be accepted (i.e., no fax or email submissions). Four (4) copies of submitted papers should be sent to: Dr. Bradley Whitehall Knowledge-Based Engineering Systems Research Laboratory Department of Mechanical and Industrial Engineering University of Illinois at Urbana-Champaign 1206 West Green Street Urbana, IL 61801 ml-eng@kbesrl.me.uiuc.edu Formats and deadlines for camera-ready copy will be communicated upon acceptance. ********** I.A.2. Fr: Nancy M. Ide Re: Lexical Semantics and Knowledge Representation June 17, 1991 University of California, Berkeley, California A workshop sponsored by the Special Interest Group on the Lexicon (SIGLEX) of the Association for Computational Linguistics TOPICS OF INTEREST: The recent resurgence of interest in lexical semantics (LS) has brought many linguistic formalisms closer to the knowledge representation (KR) languages utilized in AI. In fact, some formalisms from computational linguistics are emerging which may be more expressive and formally better understood than many KR languages. Furthermore, the interests of computational linguists now extend to include areas previously thought beyond the scope of grammar and linguistics, such as commonsense knowledge, inheritance, default reasoning, collocational relations, and even domain knowledge. With such an extension of the purview of "linguistic" knowledge, the question emerges as to whether there is any logical justification for distinguishing between lexical semantics and world knowledge. The purpose of this workshop is to explore this question in detail, with papers addressing the following points: a. Possible methods for determining what is lexical knowledge and what is outside the scope of such knowledge. b. Potential demonstrations that the inferences necessary for language understanding are no different from supposed non-linguistic inferences. c. Arguments from language acquisition and general concept development. d. Cross-linguistic evidence for the specificity of lexical semantic representations. e. Philosophical arguments for the (impossibility of the) autonomy of lexical knowledge. f. Theoretical approaches and implemented systems that combine lexical and non-lexical knowledge. FORMAT OF SUBMISSION: Authors should submit four copies of a position paper describing the work they have done in this area and specifying why they would like to participate in the workshop. Papers should be a minimum of four pages and a maximum of ten single-spaced pages (exclusive of references). The title page should include the title, full names of all authors and their complete addresses including electronic addresses where applicable, and a short (5 line) summary. Submissions that do not conform to this format will not be reviewed. Send submissions to: James Pustejovsky Computer Science Department Ford Hall Brandeis University Waltham, MA 02254-9110 USA (+1-617) 736-2709 jamesp@chaos.cs.brandeis.edu SCHEDULE: Papers must be received by 1 March 1991. Authors will be notified of acceptance by 5 April 1991. WORKSHOP INFORMATION: Attendance will be limited to 35-40 participants. The workshop is held in connection with the 29th Meeting of the ACL (18-21 June). Local arrangements are being handled by Peter Norvig (Division of Computer Science, University of California, 573 Evans Hall, Berkeley, CA 94720, USA, (+1-415) 642-9533, norvig@teak.berkeley.edu). TENTATIVE ORGANIZING COMMITTEE: Branimir Boguraev Ulrich Heid Peter Norvig James Pustejovsky Robert Wilensky ********************************************************** II. QUERIES II.B.1. Fr: Himanshu Gohel Re: Information needed on data generation Hello, I am looking for any published papers/articles/texts regarding generation of english-type data for a database for program test purposes. If anyone has any pointers, please E-mail to gohel@screamer.csee.usf.edu Thanks. - -Himanshu J Gohel - -Watch the USF BasketBulls beat up Western Kentucky this Saturday!!! ********************************************************** IV. PROJECTS IV.B.1. Fr: Susanne Humphrey Re: Selected IR-Related Dissertation Abstracts The following are citations selected by title and abstract as being related to Information Retrieval (IR), resulting from a computer search, using BRS Information Technologies, of the Dissertation Abstracts Online database produced by University Microfilms International (UMI). Included are UMI order number, title, author, degree, year, institution; number of pages, one or more Dissertation Abstracts International (DAI) subject descriptors chosen by the author, and abstract. 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. Dissertation titles and abstracts contained here are published with permission of University Microfilms International, publishers of Dissertation Abstracts International (copyright by University Microfilms International), and may not be reproduced without their prior permission. AN University Microfilms Order Number ADG90-14039. AU WANG, PETER CHUNG-TZI. TI USAGE DEPENDENT CONCEPTUAL CLUSTERING AND THE DYNAMIC REORGANIZATION OF DISTRIBUTED DATABASES. IN The University of Michigan Ph.D. 1989, 131 pages. DE Computer Science. AB In order to achieve the goal of good response time performance in an environment of rapidly changing applications, database systems should have the ability to modify their underlying data structure to reflect such change in usage. We model the learning behavior of the fragmentation level distributed database reorganization and develop a new learning algorithm called Conceptual Clustering with Usage Dependency (CLUD) to perform the distributed database reorganization dynamically. CLUD acquires new knowledge from the usage information and reflects it on the current fragmentation schema to obtain a new high performance fragmentation schema. CLUD uses the quality measurement, fitness, to measure how well the current fragmentation schema fits the usage need, and it uses another measurement, information entropy, to measure the complexity of the current fragmentation schema. By utilizing the combination of these two measurements, CLUD can determine when to perform reorganization and what needs to be done. CLUD uses procedure SPLIT to perform knowledge acquisition by splitting a fragment into several smaller fragments, and it uses procedure COMBINE to perform knowledge forgetting by combining fragments into a larger fragment. CLUD is capable of performing incremental learning, because it has a flexible knowledge representational scheme, NOC tree. A modified version of CLUD is developed to filter out the noise of the usage patterns, and to increase the flexibility of the fragmentation schema so that usage trends and cycles can be captured more accurately. A more complex triggering mechanism is also included in order to determine a more cost effective reorganization point. AN This item is not available from University Microfilms International ADGC1-28185. AU YANG, JIANHUA. TI COMPUTER-BASED DOCUMENT PROCESSING IN OFFICE INFORMATION SYSTEMS. IN Universitetet I Trondheim (Norway) Dr.Ing. 1990, 304 pages. DE Computer Science. IS ISBN: 82-7119-137-3. PU NTH-TRYKK, N-7034 TRONDHEIM-NTH, NORWAY. AB The field of Office Information Systems (OIS's) is a diverse one and it is still in the formative stage. There is yet no comprehensive theoretical foundation of the field. The thesis gives a survey of the field and presents various OIS research endeavors, such as OIS modeling approaches, OIS design methodologies, computer-based OIS design environments and office support tools and systems. The thesis also identified the need for emphasizing maintenance aspects of OIS's and discusses the requirements this will put upon the OIS analysis, modeling, design and implementation endeavors. The main barriers to fully gain all the benefits of advanced Information Technology are the problem of representing information objects of different media types with the framework of a single document and the problem of incompatible document structures. General issues related to the standardization of multimedia document architectures are discussed in the thesis. The thesis also introduces the IBM standard DIA/DCA (Document Interchange Architecture/Document Content Architecture) and the ISO standard ODA/ODIF (Office Document Architecture/Office Document Interchange Format). The thesis also discusses the issues related to the concept of intelligent documents. The thesis contains a proposal for incorporating document processing rules in ODA-standardized documents for thus being able to achieve an internationally standardized intelligent document model. The thesis claims that intelligent documents may play other important roles than the traditional role of "information carriers", such that documents may, for instance, actively participate in the management of the documents themselves and they may transform the information they convey. AN University Microfilms Order Number ADG90-16198. AU NEWCOMER, JANE ELISABETH. TI TOWARD THE IDENTIFICATION OF A BODY OF CLASSIC OR SEMINAL WORKS IN ADULT EDUCATION: A CITATION ANALYSIS. IN North Texas State University Ph.D. 1989, 236 pages. DE Education, Adult and Continuing. Library Science. AB This bibliometric study examined 19,385 citations in the bibliographies, book reviews, and reading lists of 70 volumes of Adult Education Quarterly, Adult Education, Adult Education Bulletin, Adult Education Journal, and Journal of Adult Education, and 13 volumes of the Handbook of Adult Education in the United States to identify books published before 1960 which have been frequently cited before and after 1960. Through citation analysis, an initial list of 434 titles was reduced to a core list of 64 books cited five times or more during the years between 1934 and 1988. The 64 core works were examined by publication date, citation dates, subject area, and publisher to determine those books that might be termed "classic" or "seminal." For the purposes of this study, numbers of citations were taken to mean importance of works by indicating usefulness to subsequent authors. Of the 64 books, 55 had received at least one citation before 1960 and 57 had received at least one citation after 1960. Brunner's An Overview of Adult Education Research (1950), was most frequently cited, followed by Bryson's Adult Education (1936), Clark's Adult Education in Transition (1956), Grattan's In Quest of Knowledge (1955), Lindeman's The Meaning of Adult Education (1926), and Thorndike's Adult Learning (1928). While not all 64 of the core works constitute what might be called "classic works," it is suggested that classic works are likely to be found within the listed works. A majority of books in the list are works of history or description, most useful to historians and some students of adult education. Overall, this study provides one list of classic works useful for comparative purposes and corroborates the apparent utility of specific books, lists of books, and prominent contributors identified by subjective means or popular wisdom within the field of adult education. ********************************************************** IRLIST Digest is distributed from the University of California, Division of Library Automation, 300 Lakeside Drive, Oakland, CA. 94612-3550. Send subscription requests to: LISTSERV@UCCVMA.BITNET Send submissions to IRLIST to: IR-L@UCCVMA.BITNET Editorial Staff: Clifford Lynch lynch@postgres.berkeley.edu calur@uccmvsa.bitnet Mary Engle engle@cmsa.berkeley.edu meeur@uccmvsa.bitnet Nancy Gusack ncgur@uccmvsa.bitnet The IRLIST Archives will be set up for anonymous FTP, and the address will be announced in future issues. These files are not to be sold or used for commercial purposes. Contact Mary Engle or Nancy Gusack for more information on IRLIST. 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