Information Retrieval List Digest 098 (January 8, 1992) URL = http://hegel.lib.ncsu.edu/stacks/serials/irld/irld-098 IRLIST Digest January 8, 1992 Volume IX, Number 2 Issue 98 ********************************************************** I. NOTICES A. Meeting Announcement/Call for Papers 1. The Journal of Knowledge Engineering; Special Issue on Knowledge Extraction from Text B. Publications Announcements 1. Journal of Educational Multimedia and Hypermedia C. Miscellaneous 1. Japanese Artificial Intelligence Industry 2. University of Maryland will retain its College of Library and Information Services II. QUERIES B. Requests for Information 1. Law Databases IV. PROJECT WORK C. Abstracts 1. IR-Related Dissertation Abstracts ********************************************************** I. NOTICES I.A.1. Fr: Steven Leo Lytinen Re: Heuristics: The Journal of Knowledge Engineering Special Issue on Knowledge Extraction from Text Call for Papers The Journal of Knowledge Engineering Special Issue on Knowledge Extraction from Text Much valuable information is contained in large corpora of natural language texts. This information could potentially be used in many ways, such as automatic or semi-automatic construction of knowledge bases of more conventional databases; tracking of trends in information sources such as newswires; or automatically browsing corpora to identify and extract potentially relevant information for particular human users, given those users' interests. This issue will explore techniques in knowledge extraction, or the task of extracting relevant information from collections of free-form text, and converting this information into a standardized fixed format. Papers are solicited which present innovative approaches to knowledge extraction, or apply existing techniques in novel ways to a knowledge extraction task. In particular, the following issues are of interest: - Application of natural language processing (NLP) and/or information retrieval (IR) techniques to a knowledge extraction task - New techniques in NLP and/or IR, including the combination of NLP and IR approaches - The use of machine learning or other techniques to learn properties of a corpus, thereby improving the performance of a knowledge extraction system - Application of the results of knowledge extraction in some other knowledge engineering task SUBMISSIONS: Submit four (4) copies of an original, unpublished paper to the guest editor. All submissions should be double-spaced, and each copy should begin with a short (less than 200 word) abstract. Submissions should also include camera-ready copies of any figures, as well as a 5 1/4" disk containing the text of the manuscript in either WordPerfect (version 4.2 or higher) or ASCII format. Submissions should be received by the guest editor by MARCH 1, 1992. Acceptance notices will be issued by JUNE 15, 1992, and the deadline for submitting final manuscripts and accompanying materials will be JULY 15, 1992. Questions regarding the special issue should be directed to the guest editor, phone (313) 763-5632, e-mail lytinen@caen.engin.umich.edu. Steven L. Lytinen Guest Editor, Heuristics Artificial Intelligence Laboratory The University of Michigan 1101 Beal Ave. Ann Arbor, MI 48109 Heuristics is the journal of the International Association of Knowledge Engineers. ********** I.B.1. Fr: Assoc. Advancement Computers Ed. PREMIERES FALL 1991 . . . JOURNAL OF EDUCATIONAL MULTIMEDIA AND HYPERMEDIA Published by the Association for the Advancement of Computing in Education Editor: David H. Jonassen (University of Colorado-Denver) Associate Editor: Scott Grabinger (University of Colorado-Denver) The Journal of Educational Multimedia and Hypermedia is designed to provide a multi-disciplinary forum and serve as a primary information source to present and discuss research and applications on Multimedia and Hypermedia in education. The main goal of the Journal is to contribute to the advancement of the theory and practice of learning and teaching using these powerful technological tools that allow the integration of images, sound, text and data. Reviewed by leaders in the field, this international quarterly Journal is published for researchers, developers, professors, teachers, teacher educators, curriculum coordinators, and all interested in the educational research and applications of Multimedia and Hypermedia at all levels. Journal articles include any educational aspect of Multimedia and Hypermedia and take the form of: o Research papers o Case studies o Experimental studies o Review papers o Book/courseware reviews o Tutorials o Courseware experiences o Opinions DEPARTMENTS INCLUDE: Viewpoint - examines ideas and their relationships in the field. Multimedia Projects: Issues and Applications - discusses the practical and theoretical problems and issues associated with current state-of-the-art multimedia/hypermedia projects (Edited by Greg Kearsley, George Washington University) Developers' Dialogue - examines interesting, unexplored, broad themes, issues and decisions faced by developers (Edited Carrie Heeter, Michigan State Univ.) Educational Multimedia/Hypermedia Abstracts - abstracts noteworthy research appearing in journals and databases. Product Reviews - provides in-depth reviews with screen images of multimedia/hypermedia products (Edited by Robert Beichner, SUNY-Buffalo) Book Reviews - provides critical reviews of books in the field (Edited by Philip Barker, Teesside Polytechnic) To request subscription/membership information or Author Guidelines, contact: AACE P.O. Box 2966 Charlottesville, VA 22902 USA E-mail: aace@virginia.edu Phone: (804) 973-3987 The Association for the Advancement of Computing in Education (AACE) is an international, educational organization whose purpose is to advance the knowledge and quality of teaching and learning at all levels with computing technologies through the encouragement of scholarly inquiry related to computing in education and the dissemination of research results and their applications. AACE consists of five membership divisions. And each division provides members with an annual conference and publications. The following respected journals represent the topic areas of these divisions: - Journal of Educational Multimedia and Hypermedia - Journal of Artificial Intelligence in Education - Journal of Computing in Childhood Education - Journal of Computers in Mathematics and Science Teaching - Journal of Technology and Teacher Education (premieres Fall '92) ********** I.C.1. Fr: James W. Reese Re: Japanese Artificial Intelligence Industry I am the editor of AJBS-L@NCSUVM (The Association of Japanese Business Studies List at North Carolina State University, USA). During the next few months, we will be adding Japanese industry analysis files to the existing AJBS-L datafiles. We are seeking contributor(s) for a file called JAPAN AI which will cover the nature and characteristics of the Japanese artificial intelligence industry (e.g. major competitors, market shares, etc.). If you have research which would be useful, please send it to: James W. Reese AJBS-L Editor R505040@UNIVSCVM.CSD.SCAROLINA.EDU (Internet) R505040@UNIVSCVM (Bitnet) The address for AJBS-L is: AJBS-L@NCSUVM.CC.NCSU.EDU (Internet) AJBS-L@NCSUVM (Bitnet) Thank you in advance for your assistance. ********** I.C.2. Fr: Gary Marchionini Re: Notice UNIVERSITY OF MARYLAND WILL RETAIN COLLEGE OF LIBRARY AND INFORMATION SERVICES After a seven-month review of the status and programs of the College of Library and Information Services undertaken as part of a general review of programs on the College Park campus, the University of Maryland has concluded that it should retain the College as separate and autonomous rather than merging it with another campus unit. The University will also retain the College's Ph.D. degree, ensuring the continuing research strength of the College's overall program. The College was identified for review in the wake of severe budget cuts at the University. Two campus committees were charged with studying the College, and their reports to the University's Academic Planning Advisory Committee reaffirmed the College's quality, cost-effectiveness, and centrality to the mission of the University. These reports, along with national and local support from political leaders and from the library and information science community, provided the basis for the University's decision. The decision to preserve the College's structure and doctoral program comes at the end of a decade marked by the closings of a number of prestigious library schools. The result of the University's deliberations affirms both the value of the College and the continuing importance of library and information science education. Further information about the process and the decision is available from the Office of the Dean, College of Library and Information Services. ********************************************************** II. QUERIES II.B.1. Fr: Octavio Juarez Espinosa Re: Looking for references Hi, I'd like to receive some references about works the Databases with Laws. I am working in my Bachelor thesis. Thanks in advance for your attention. Sincerely, Octavio Juarez ********************************************************** IV. PROJECT WORK IV.C.1. Fr: Susanne M. Humphrey 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 ADG91-18964. AU COLLINS, J. STEPHANIE MELNYK. TI A DESIGN FOR DYNAMICALLY CONSTRUCTING CONCEPTUAL MODELS OF USERS' MENTAL MODELS TO PROVIDE ADAPTIVE SYSTEM RESPONSES TO INDIVIDUAL USERS. IN The University of Wisconson - Milwaukee Ph.D. 1990, 247 pages. DE Business Administration, Management. Computer Science. AB In order for users with different levels of skill to effectively use a computer system, they must have accurate mental models of the structure and processes which make up that system. These mental models are used to predict and understand system behavior. In order for these models to be useful, they should correspond to the system designers' conceptual model, the accurate, consistent, and complete representation of the system. Users experience difficulties in using systems when their mental models of systems are incomplete or inaccurate, indicating a lack of knowledge. Enabling a system to compensate for the deficiencies of a user's mental model implies that the system must be able to deduce how the mental model deviates from the conceptual model. The research described here isolates some factors which could be important in a conceptual model of a user's mental model of a target system. One manifestation of users' mental models are the errors that users make. An adaptive prototype interface system for SQL was constructed to test a method to perform dynamic (runtime) construction of conceptual models of users' mental models by using errors as indicators of how an individual users' mental models deviate from the conceptual model. An experiment is described in which user errors were collected during user-system interaction sessions. These errors were classified by the prototype system as indicating a lack of three kinds of knowledge: task, semantic, and syntactic. These error classifications were used by the system to construct profiles of individual users over time. The profiles were used to tailor subsequent error and help messages to users. The principal finding of the experiment was that the user interface that incorporated dynamic user modeling had a significant effect on the number of errors made by users. There were also effects on the pattern of errors that users made. These findings were taken as evidence that a user interface that is able to adapt its responses to users' mental models will enable users to construct successful data retrieval commands more quickly than one that does not. AN University Microfilms Order Number ADG91-16229. AU LEE, CHOON YEUL. TI A MULTICRITERIA DATA RETRIEVAL MODEL: AN APPLICATION OF MULTIATTRIBUTE PREFERENCE MODEL TO DATA RETRIEVAL. IN The University of Michigan Ph.D. 1990, 201 pages. DE Business Administration, Management. Computer Science. AB This dissertation proposes a new data retrieval model as an alternative to exact matching. While exact matching is an effective data retrieval model, it is based on fairly strict assumptions and limits our capabilities in data retrieval. A new category of data retrieval, multi-criteria data retrieval, is defined to include many-valued queries, (which require partitioning of data entities into more than two, possibly infinite, subsets), and multi-derived data, (which are derived by non-homogeneous multiple rules). A metric-based preference model is proposed as a referential model for multi-criteria data retrieval. The model is based on the idea that we human beings prefer outcomes close to an ideal alternative (the "positive anchor") and far removed from the worst imaginable alternative (the "negative anchor"). A "relative distance metric" is proposed to operationalize the concept of closeness in matching. Many-valued and multi-derived data retrieval queries are formalized within the framework of the metric-based preference model. Query interpretation is defined as measuring the relative distances of data entities from the (positive and the negative) anchors. The viability of the proposed data retrieval model is proved by analyzing its logical properties and by evaluating its performance against the current data retrieval models for both exact matching and non-exact matching. The multi-criteria data retrieval model is proved to satisfy the De Morgan logic and therefore has the same query interpretation values as the exact match data retrieval model for the conventional data retrieval queries. With regard to many-valued query interpretation, the proposed relative distance metric is proved to better represent a user's actual preferences for data entities than the current fuzzy metric or the Euclidian distance metric. With regard to retrieval of multi-derived data, the proposed model is proved to result in fewer errors than current exact matching. These findings show that, both at the logical level and at the performance level, the proposed multi-criteria data retrieval model retains all the desirable features for data retrieval. AN University Microfilms Order Number ADG91-15171. AU TSAI, YAO-CHUAN. TI STRUCTURED MODELING QUERY LANGUAGE. IN University of California, Los Angeles Ph.D. 1990, 193 pages. DE Business Administration, Management. Computer Science. AB Model query languages are important in today's modeling systems, but are as yet underdeveloped. Most previous research on model query languages either represents models in the data definition language of a database management system (DBMS) and uses the DBMS's data query language to perform queries, or represents models in predicates within a PROLOG system and uses PROLOG's inference engine to perform queries. This research proposes the use of a 'semantic' model definition language so that the semantics of model definitions can be exploited to achieve an easy to use model query language. We take two steps in order to create a simple and powerful model query language. First, we use SML as the model definition language. Second, we internally represent SML models in relations, and then build Structured Modeling Query Language (SMQL) on top of the SQL offered by a relational DBMS by using the semantics of the SML model definitions. The proposed SMQL is significant not only because of its retrieval capability, but more importantly because of its remarkably easy user interface. AN University Microfilms Order Number ADG91-15451. AU LEE, REI-CHI. TI QUERY PROCESSING WITH DATABASE SEMANTICS. IN University of California, Los Angeles Ph.D. 1990, 145 pages. DE Computer Science. AB Two issues of database query processing are addressed in this thesis: optimization and representation. Optimization is concerned with query processing techniques to quickly obtain answers from the database; and representation is concerned with query and answer representation between the users and the database systems. Conventional query processing takes a domain-independent approach to determine the optimal access plan for retrieving the answer. Semantic query optimization (SQO) uses knowledge and semantic reasoning to transform queries into more efficient representations for processing. Integrity constraints (IC) have been used as semantic knowledge for SQO. However, since the purpose of IC is to ensure database integrity, these constraints are often specified in a very general way. As a result, they are of limited value for SQO. Furthermore, acquiring the set of integrity constraints is also a problem. The first part of the thesis presents a model-based learning technique, based on a Knowledge-based Entity-Relationship (KER) model and rule induction techniques, that learns a set of If-then rules from the database contents. Our experimental results show that not all the semantic knowledge is useful for query improvements. The second part of the thesis presents a database restructuring technique, based on type hierarchy and induced rules, which restructures databases to provide a more effective environment for SQO. Conventional query answers usually are in the form of listing all the instances that satisfy the query. An intensional answer provides characteristics that characterize the extensional answers which gives a summarized description about the answers. The third part of the thesis presents an approach that uses induced knowledge and type inference to derive intensional answers. In a conventional query processing environment, queries have to be rigidly specified and only data satisfy the query will be considered as answers. Cooperative query processing allows vague queries to be specified and approximates data to be provided as answers when the exact answer is not available. The last part of this thesis presents an approach of using type abstraction hierarchy to provide cooperative answers. ********************************************************** 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 or calur@uccmvsa.bitnet Nancy Gusack ncgur@uccmvsa.bitnet Mary Engle engle@cmsa.berkeley.edu or meeur@uccmvsa.bitnet The IRLIST Archives will be set up for anonymous FTP, and the address will be announced in future issues. To access back issues presently, send the message INDEX IR-L to LISTSERV@UCCVMA.BITNET. To get a specific issue listed in the Index, send the message GET IR-L LOG ***, where *** is the month and day on which the issue was mailed, to LISTSERV@UCCVMA.BITNET. 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