Information Retrieval List Digest 257 (May 29, 1995) URL = http://hegel.lib.ncsu.edu/stacks/serials/irld/irld-257 IRLIST Digest ISSN 1064-6965 May 29, 1995 Volume XII, Number 20 Issue 257 ********************************************************** II. JOBS 1. Texas Instruments: Information Specialist III. NOTICES A. Publications 1. Federal Register on WEB IV. PROJECTS A. Abstracts 1. IR-Related Dissertation Abstracts ********************************************************** II. JOBS II.1. Fr: Lezlie Shell Re: Texas Instruments: Information Specialist POSITION AVAILABLE -- INFORMATION SPECIALIST TEXAS INSTRUMENTS INCORPORATED HOUSTON, TEXAS The Semiconductor Group Library of Texas Instruments Incorporated has an immediate opening for an INFORMATION SPECIALIST. The Information Specialist is responsible for running a one professional library, supervising a library assistant, and participating in Enterprise-wide projects dealing with the distribution of technical reports and other information products. Located in Stafford (one exit south of Beltway 8 on the Southwest Freeway in Houston), the library contains approximately 2000 books and 250 periodical subscriptions as well as CD-ROM databases. The library primarily supports semiconductor research, design, manufacture, and marketing but is open to all TI employees worldwide. The 7 libraries in the TI Information Network have recently begun using the Dynix Horizon client/server automation system. There is no relocation budget associated with this position. Out-of-town applicants will be responsible for their own expenses. REQUIREMENTS: 0 MLS or MLIS from an ALA accredited University 0 2 years post MLS experience 0 Expertise in: DIALOG searching, word processing, Excel 0 Excellent communications and supervisory skills Please send resumes to: MAIL: OVERNIGHT: Helen Manning Helen Manning Technical Information Manager Technical Information Manager Texas Instruments Incorporated Texas Instruments Incorporated P.O. Box 655303 MS 8222 8360 LBJ Frwy MS 8222 Dallas, TX 75265 Dallas, TX 75243 FAX: 214-997-2504 E-MAIL: hmanning@lobby.ti.com Texas Instruments is an equal opportunity employer ********************************************************** III. NOTICES III.A.1. Fr: Genevieve Engel Re: Selected IR-Related Dissertation Abstracts The following are citations selected by title and abstract as being of potential interest to the Information Retrieval (IR), resulting from a computer search, using the CDP/Online system, of the Dissertation Abstracts International (DAI) database produced by University Microfilms International (UMI). Included are accession number (AN); author (AU); title (TI); degree, institution, year, number of pages (IN); UMI order number (DD); reference to the published DAI (SO); abstract (AB); one or more DAI subject descriptors chosen by the author (DE); thesis adviser (AR); and dates associated with the monthly update file (UP). 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: 1-800-343-5299; fax: 313-973-1540. 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 AAI9506220 AU Liu, Geoffrey Zhengfu. TI AN EXPERIMENTAL STUDY OF INCORPORATING THEMATIC ROLE ANALYSIS INTO VSM-BASED TEXT INFORMATION REPRESENTATION AND RETRIEVAL: DESIGN AND EVALUATION OF THE SEMANTIC VECTOR SPACE MODEL. IN Thesis (PH.D.)--UNIVERSITY OF HAWAII, 1994, 270p. DD Order Number: AAI9506220. SO Dissertation Abstracts International. Volume: 55-10, Section: A, page: 3022. AB One of the current trends in the field of information retrieval is to apply artificial intelligence techniques, especially natural language processing and knowledge representation techniques, to the problem of information retrieval. Although this approach is appealing, it is unlikely that the problem can be solved once and for all by completely relying on the semantic processing and knowledge representation techniques and attempting direct retrieval of information from a knowledge base constructed out of a collection of natural language texts. A feasible approach is to use the well-developed IR techniques as the backbone and incorporate some of the NLP techniques to increase the power of content representation without involving sophisticated processes of semantic interpretation and knowledge representation. In this dissertation research, a text representation and searching technique, called "the Semantic Vector Space Model" (SVSM), was developed by combining Salton's Vector Space Model (VSM) with heuristic syntax parsing and distributed representation of semantic case structures. In this model, both documents and queries are represented as semantic matrices. A search mechanism was designed to compute the similarity between two semantic matrices and the similarity value was interpreted as the predictor of relevancy. A prototype system was built to implement this model by modifying the SMART system and using the Xerox P-O-S tagger as the pre-processor of the indexing process. The prototype system, called "SMART++", was used in a series of experiments designed to evaluate the proposed text representation and searching technique in terms of precision, recall, and effectiveness of relevance ranking. The original SMART system was used as the benchmark. Three experimental collections acquired from Cornell University were used in the experiments. The results of these experiments showed that if documents and queries were too short (typically less than 2 lines in length) our technique was less effective than the Vector Space Model. But with longer documents and queries, especially when original documents were used as queries, we found that the system based on our technique had significantly better performance than the SMART system. This suggests that a significant improvement of system performance can be achieved by combining semantic case structure information with the weighted term representation of texts in a situation where longer queries are available. DE Information Science. Computer Science. Language, Linguistics. Artificial Intelligence. AR Osborne, Larry N. UP 9503. Revised: 950331. AN AAI9506067 AU Mostafa, Javed. TI DESIGN AND ANALYSIS OF THE HUMAN-COMPUTER INTERFACE FOR AN IMAGE DATABASE (DATABASE). IN Thesis (PH.D.)--THE UNIVERSITY OF TEXAS AT AUSTIN, 1994, 124p. DD Order Number: AAI9506067. SO Dissertation Abstracts International. Volume: 55-10, Section: A, page: 3023. AB Supporting effective retrieval of images from databases is a difficult problem as sound design techniques for facilitating retrieval are rare. The human-computer interface is the principal component responsible for facilitating retrieval in databases. Hence, image retrieval can be better facilitated by improving the HCI design. An HCI was created that included both visual and verbal means of image retrieval (Visual-Verbal). Another HCI was created, for comparison purposes, that only included verbal means of retrieval and displayed only verbal information (Verbal). An object-oriented environment and client/server design were used to develop the HCIs. A set of digitized moving images from popular films was used as the test data set. Applying both manual and automated techniques a practical yet powerful image indexing procedure was developed. The index contained both verbal and visual information and was fully integrated into the Visual-Verbal HCI as an online aid. Effectiveness of the two HCIs was compared using quantitative and qualitative data collected from search sessions involving 18 users. No significant difference was found, between the two HCIs, on the variables search completion, error, help request and optimum searching. However, in both HCIs search completion rate was high (more than 80%). DE Information Science. Library Science. Computer Science. AR Miksa, Francis L. UP 9503. Revised: 950331. AN AAIMM91237 AU Huang, Yue. TI INTELLIGENT QUERY ANSWERING BY KNOWLEDGE DISCOVERY TECHNIQUES. IN Masters Thesis (M.SC.)--SIMON FRASER UNIVERSITY (CANADA), 1993, 143p. DD Order Number: AAIMM91237. SO Masters Abstracts International. Volume: 33-02, page: 0560. AB Knowledge discovery in databases facilitates querying database knowledge, cooperative query answering and semantic query optimization in database systems. In this thesis, we investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, lazy evaluation, semantic query optimization, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query processing in data- and knowledge-base systems. A prototyped experimental database learning system, DBLEARN, has been constructed. Our experimental results on direct answering of data and knowledge queries are successful with satisfactory performance. DE Computer Science. AR Han, Jaiwei. IB 0-315-91237-5 UP 9503. Revised: 950331. AN AAIMM89705 AU Ould-Brahim, Hamid. TI SAFIR, ANALOGY-BASED SYSTEM FOR DATABASE DEFINITION AND QUERY REUSE. IN Masters Thesis (M.C.S.)--UNIVERSITY OF OTTAWA (CANADA), 1993, 104p. DD Order Number: AAIMM89705. SO Masters Abstracts International. Volume: 33-02, page: 0567. AB This thesis presents a system which employs analogical reasoning to define and manipulate databases. The objective is to model the behavior of naive users who often adapt database relations and queries from documentation examples to the problem at hand. The user has at her disposal a manual that contains examples of database designs with a variety of database queries in some exemplary milieu called the base domain. The manual explains the example queries in simple terms and gives their implementation in a database query language such as SQL. Our user is not a database expert. She can neither define the database nor can she compose query implementations from scratch. At best she can construct simple queries in the base domain by copying them verbatim from the manual. Our system works in two steps: first it defines a target database by building incrementally analogies represented by pairs of object correspondences, base-target. These analogies are built by mapping objects, relationships, and semantic constructs from the base domain into the target domain. The process is then interactive and incremental (i.e. the user can accept or reject the inferred analogies). The second step of the approach consists to derive target domain query's implementation from its specification by reusing queries developed in the base domain. We rely on a weak form of derivational analogy--traces of a planner operating in the base domain are used to derive a target domain query's implementation in SQL from its specification. The very simple planner used builds in the base domain plans that result in particularly simple, prototypical queries. Queries are specified in a high level subset of natural language. DE Computer Science. AR Matwin, Stan. IB 0-315-89705-8 UP 9503. 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