Design and implementation of an experimental cataloging advisor-mapper

Informalion Processing & M~lnagemenr Vol. 28. No. 2, pp. 241-257, 1992 Printed in Great Britain.

0306-4573/5-2 fS.00 + .lxl Copyright 0 1992 Pcrgamon Prcu pk

DESIGN AND IMPLEMENTATION OF AN EXPERIMENTAL CATALOGING ADVISOR- MAPPER

ZORANA ERCEGOVAC and HAROLD BORKO Graduate School of Library and Information Science

University of California Los Angeles, Los Angeles, CA 90024, U.S.A.

Abstract -The main objective of this study is to understand how an experimental, semi- automatic cataloging adviser (Mapper) could be designed to assist the intermittent user in the descriptive cataloging of certain US-produced, single-sheet maps. The study is con- cerned with formalizing experts’ judgment into a set of rules, which are then used to design Mapper’s knowledge base. The questions of user modeling and human-computer compatibility are examined in order to propose a set of user interface characteristics related to Mapper’s design. Mapper is implemented using the Apple HyperCard”’ system.

1. INTRODUCTION

Map cataloging remains an expensive, complex, and important bibliographic activity. Yet, few library and information science school graduates get the knowledge needed to become map catalogers. It follows that potential map catalogers need a long course of apprentice- ship under the guidance of a master. However, masters are rare and there is an understand- able reluctance to invest in long learning time for an occasional activity.

We contend that some form of computer-aided assistance would alleviate the problem in the following two respects. It could (a) make experts’ knowledge accessible to occasional users, and (b) relieve cataloging users from concerns for routine details, allowing them to concentrate on critical tasks.

This article describes the design of such an experimental advice-giving system, Map- per, restricted for practical reasons to the descriptive cataloging of single-sheet maps emanated by three United States publishers. The research reported is based on a three-part study outlined in Fig. 1 (Ercegovac, 1990b). The study was performed to understand how an experimental semi-automatic map cataloging advisor could be designed to make experts’ knowledge accessible to occasional users in solving three significant tasks in the descriptive cataloging of maps.

The study’s first part, completed prior to the design of Mapper, used a multiple-observation approach to identify and analyze instances of expert judgment in solving three cataloging tasks (Ercegovac, in press). Attention was directed toward understanding the meaning of the phrase “chief responsibility,” and distinguishing it from other authorial re- sponsibilities (Ercegovac, 1990a).

This paper, based on a second part of the study, concerns itself with design recommendations and implementation issues for Mapper. The paper asks the following questions: (a) Can novices’ cataloging needs be articulated sufficiently to incorporate them into Mapper’s interface design? (b) Is there an optimal set of boundaries among cataloging decision points for which various assistance modes appear most productive? In order to make experts’ knowledge accessible to novices, this part of the study formalized a body of expert judgment into a set of rules and then entered these rules, along with a set of published rules, into Mapper’s knowledge-base.

The third part of the study evaluated Mapper’s performance in a laboratory setting (Ercegovac, 1990b).

Mapper is a semi-automatic cataloging advisor in the following two senses: First, the

This article is based on the author’s doctoral dissertation (Ercegovac, 1990b) to which the reader is referred for details of the methodology, statistical procedures, and analysis. The study w~~supported in part by the OCLC Library and Information Science Research Grant Program under grant OCLC-40433058771 and by the National Science Foundation under grant SES-8814111.

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242

PART 1

PART 2 LLJ Design and lmplemenfation of Mapper

Fig. 1. Parts of the study and their relationships.

phrase semi-automatic cataloging advisor means that Mapper is designed to perform only some activities automatically for the user, whereas others are conducted in a participatory manner where the user and the system cooperate. Empirical research in the areas of Infor- mation Retrieval (Bates, 1990) and Real-Time Decision Making (Mitchell, 1987) supports the idea that some people want to retain control of the activities in computer-based systems such as online database searching and air traffic control.

Second, Mapper gathers evidence about cartobibliographic features by asking the user questions via menus. It does not use machine-readable records of corresponding maps.

Mapper’s user interface provides the augmented mode of assistance. In contrast to the accelerated or delegated assistance, the augmentation mode (Paisley & Butler, 1977) aids the user in the substance of his or her task and not just in the pace of its execution.

The meaning of the user-computer interface is that of Moran (1981): “any part of the computer system that the user comes in contact with-either physically, perceptually, or conceptually.”

intermittent users in this study included library graduate students who had completed the introductory course in descriptive cataloging and who had no experience in map cataloging.

Descriptive cataloging may be regarded as the phase of the cataloging process that deals with the description of a bibliographic item and the recording of the information in the form of a cataloging entry.

As mentioned above, the descriptive cataloging of maps is an extensive and complex activity. Therefore, to make it feasible we restricted our study to three representative cataloging tasks:

1. To ascertain the chiefly responsible emanator (main entry heading). 2. To determine title and statements of responsibility. 3. To decide on the appropriate values for the publication area.

These three tasks correspond to three data elements, which are given numerical names (e.g., 110, 245, 260) in the Machine-Readable Cataloging (MARC) bibliographic format. We chose these tasks for the following reasons:

Design and impIementation of Mapper 243

The first task, which involves determination of the main entry, may be regarded as es- sential, at least in the Anglo-American cataloging tradition, for collocating, ordering, and citing functions of the library catalog (AACR2R 1988; Carpenter, 1989).

We chose the second task, “title and statements of r~~~ibility~ (AACRZR 1988), because it represents the first area of description of an item. The task is complex and includes recording the chief name of an item (title proper), and names of person(s) or corporate body(ies) that have contributed significantly to the intellectual or artistic content of the cartographic item (statements of responsibility).

The third task, “publication, distribution, etc.” (AACR2R, 1988), represents the fourth area of description of a cartographic item. Besides names for the publication place and the publisher, the task involves identifying the publication date of a map in question.

Three classes of maps under investigation are produced by National Geographic So- ciety (NGS), a private scientific society; Central Intelligence Agency (CIA), a governmental agency; and H.M. Gousha (HMG), a commercial mapping firm. We selected the maps produced by these publishers because the maps require one to solve difficult and interesting cat~oging problems. For example, the NGS maps address problems related to statements of responsibility; the CIA maps address problems of ascertaining the entry and determining data elements of the publication area; the HMG maps address cataloging problems in- volving all cataloging tasks under study.

After this introductory section, and a brief literature review (Sections 1 and 2), this paper first discusses some of the issues in the interface design of Mapper (Section 3). Next, the paper describes the design and implementation (Sections 4 and 5) of the Mapper. The paper closes by summ~i~ng the study’s findings and su~esting directions for further work (Section 6).

2. RELATED WORK

Relatively little has been reported in the literature concerning automated cataloging studies. Studies on some of the issues related to knowledge acquisition and expert systems are cited elsewhere (Ercegovac, in press). No empirical research devoted to studying expertise that exists in descriptive map cataloging has been located, nor have we found any studies that report on the ways to transfer human expertise in map cataloging to various cataloging expert systems, so as to enable cataloging novices to perform certain nontriv- ial cataloging tasks.

It is not known how successful library students having limited formal cataloging training would be in applying their basic cataloging skills from books to maps. Although goals of existing automatic cataloging studies differ from those of this study, some of the research questions and findings in these earlier empirical studies do have implications for this research, and these studies deserve attention.

The earliest work in automated cataloging having implications for this study attempted to examine whether the human intellectual process of selecting main and added entries could be simulated by automatic techniques (Fox-Sandberg, 1972). Among the most recent efforts in the area of automatic cataloging, researchers have analyzed syntactic and seman- tic characteristics of title pages as sources of information for identifying titles proper (Jeng, 1986); and generated bibliographic records of serials in the area of physical sciences from machine-readable images (Endres-Higgemeyer & Knorz, 1987). Other work on expert systems for cataloging directed little or no attention to the user interface issues in the efforts to design experimental programs (Davies, 1987; Hjerppe & Olander, 1989). The approach has been to convert “if-then” algorithms for choice of access points into a series of menus without questioning whether the menu-based style of interface is optimal for a given user; the user characteristics were never given consideration in the design of a system.

Having experimented with several projects in the area of expert systems and descriptive cataloging (e.g., ESSCAPE, HYPERCATalog), Hjerppe & Olander (1989) have suggested the consideration of the following directions for researchers: to focus on one type of bibliographic material; to examine users’ skills and incorporate those into systems design; to capture experts’ knowledge and experience; and to integrate that body of unpub-

244 Z. ERCEGOVAC and H. BORKO

lished knowledge with the body of rules embodied in AACR2R. These guidelines were considered from the outset of our project (1986).

Various elicitation techniques, together with corresponding case studies and types of data that can be obtained validly by using these techniques, have been well documented in the literature (Kidd, 1987). Related work on experts systems in domains other than cataloging has been reviewed elsewhere (Aluri, 1989; Ercegovac, 1984, 1986, 1989; Humphrey, 1989).

3. PROBLEM STATEMENT AND INTERFACE DESIGN OF MAPPER

Given that cataloging is an inherently complicated activity requiring formal training and extensive experience, and that most library students appear unmotivated to invest their time in cataloging courses beyond the prescribed minimum (Ercegovac, 199Ob), there is ev- idently a need to customize interfaces to fit the capabilities of inexperienced users.

To identify interface characteristics related to the design and implementation of Map- per, some of the specific skills and preferences of Mapper’s primary user group, cataloging novices, are exmained. Next, assistance modes for each cataloging decision point for the corresponding three tasks are proposed. Finally, different types of knowledge required to complete cataloging tasks under study are described.

3.1 Questions related to the interface design The following two questions related to the user interface design asked:

Ql . Can novices’ cataloging needs be articulated sufficiently to incorporate them into the interface design?

42. Is there an optimal set of boundaries among cataloging decision points for which various assistance modes appear most productive?

The two questions are discussed in Sections 3.3 and 3.4.

3.2 Map cataloging tasks As mentioned earlier, three important cataloging tasks were selected. The order of con-

ducting these three tasks deserves special attention. We assumed that maps emanated by a given mapping agency in a given time frame

were homogeneous with respect to appearance of cartobibliographic features such as presence or absence of dates, of chiefly responsible names, of a particular cartographic design, etc. Consequently, this study used the data gathered for Task 1 to automatically produce some of the data elements in the statement of responsibility and the publication areas (Tasks 2 and 3).

The tasks were also chosen because the subjects, having passed the introductory course in descriptive cataloging, had been introduced to important principles in descriptive cataloging, including those of corporate body, of main entry, and of standardization, to the understanding of functions of descriptive cataloging, and to some of the issues involved in the machine-readable cataloging. Since the three tasks are covered in a descriptive cataloging course, and the nature of the tasks is analogous for books and maps, there would be no need to introduce the three tasks under study to the experimental subjects.

3.3 User characteristics In keeping with proposed Computer-Human Interface guidelines (Norman, 1986; Card

et al., 1983), which advise consideration of the psychology of the user early in the system design, this study examined the following question: Can users’ needs be articulated sufficiently to incorporate them into the interface design (Ql)?

In order to address this question, first-year Graduate School of Library and Informa- tion Science (GSLIS) students at UCLA who had completed all requirements in the introductory course of descriptive cataloging in the Fall Quarter 1989 were surveyed on (a)

Design and implementation of Mapper 245

technical aptitude variables including academic major, cartographic literacy, and reasoning style, (b) experience in computer concepts and task concepts, and (c) attitudes toward cataloging in general. Earlier research that studied various human-computer issues in the context of using two classes of online retrieval systems, online public access catalogs, and multidatabase commercial retrieval systems, isolated and analyzed similar variables as factors affecting online library retrieval (Borgman, 1984, 1986). While previous studies examined experts’ and novices’ performance in the use of commercial retrieval systems (Penichel, 1981; Howard, 1982; Woelfl, 1984) and Online Public Access Catalogs (Matthews et al., 1983), little is known about just how successful intermittent users would be in applying their basic cataloging skills from books to maps.

Survey findings had identified important personal traits of novices who chose librari- anship as their future professional career, yet appeared unmotivated to learn cataloging, beyond a minimum skill level, and found cataloging difficult but important.

3.4 Assistance modes for cataloging decision points Sampled literature suggests that people are good at encoding unusual and unexpected

events, as well as detecting stimuli in noisy backgrounds. People are also good at incorpo- rating prior experience from properties of items or events and their distribution over time that might not be accounted for in computer models of world knowledge. People seem to forget prior experience progressively, especially under information overload and with tasks that are difficult, novel, critical, and conflicting (Sweller, 1988).

In contrast, computers seem more productive when used for (a) storing quantities of coded discrete units of data, thus reducing short-term memory load on the user’s side; (b) recalling detailed, complete, and precise data; and (c) processing well specified data with speed and consistency.

We identified cataloging decision points for each of the three cataloging tasks, described earlier. We examined each decision point with respect to three assistance modes, as defined by Paisley & Butler (1977). We proposed a set of Mapper’s user interface guidelines based on the nature of decision points and lessons learned from the survey results, described in Section 3.5.

We divided the three cataloging tasks, described in Section 3.2, into 23 decision points. A decision point was defined as a cognitively manageable quasi-independent move that brings one closer to accomplishing a given task. In view of the recent research interest fo- cused on the analysis of human cognitive behavior suggesting various possibilities to en- hance human capability rather than to replace it by the computer (Hollnagel & Woods, 1983), Mapper’s interface design emphasized the augmented assistance mode. Rather than delegating the execution of all decision points to the system, only a subset of decision points has been delegated to Mapper.

A layer of two assistance modes was superimposed on each of the decision points. The augmented mode of assistance was proposed for those cataloging decisions whose answers provide access points to the bibliographic record and describe a map. The examples of the access points are the chiefly responsible emanator and the title proper. The delegated mode of assistance was proposed for those decisions that influence the form of data elements in a bibliographic record. Most of the rules that pertain to syntactical conventions (e.g., punc- tuation, spacing, bracketing) were delegated to Mapper.

3.5 Interface guidelines We recommended the following set of interface design guidelines for this project: Design recommendation No. 1. Knowledge of the use of computers should be as-

sumed and not taught. Design recommendation No. 2. Vocabulary as taught in the introductory descriptive

cataloging course should be kept consistent for map cataloging as much as possible. All terms and concepts specific to maps should be readily available to the user during any point in their problem-solving situation (Ross & Moran, 1983).

Design recommendation No. 3. Conceptual knowledge, including definitions and rules, should be complemented by examples, illustrations, and templates containing empty


values to be supplied by the user. Representative pictorial displays of representative maps should be used as menus.

Design recommendation No. 4. Explanation capability should be designed to substan- tiate Mapper’s advice provided to the user.

Design recommendation No. 5. Mapper’s interface should incorporate context-sen- sitive pull-down menus (Reed, 1982; Norman, 1983).

Design recommendation No. 6. A minimum number of commands should be shown to the user as pull-down menus. The command interface will be supplemented with (a) menus where a menu might be a picture of a representative map under study; and (b) fill-in forms to facilitate gathering data about a map in question. The use of a direct manipulation style of interface allows the user to directly and visually “select,” “deselect,” “shrink,” etc. various objects and menus on a screen without having to remember to use a special command language to do so.

These recommendations were a framework for the design and implementation of Map- per, described in Sections 4 and 5. In order to facilitate a rich learning environment (Carroll & Mack, 1983; Lewis, 1988; Malone, 1982), Mapper’s design interface uses an “entrance- way” as a metaphor. This spatial metaphor suggests an architecture with a transparent membrane between the inside and the outside, with the purpose of articulating the relationship between the two, and unobtrusively showing its inner workings. This approach is very different from a “black box” approach, which relates computer inputs and outputs without specifying intervening processes between the two. In contrast, a “glass-box” approach reveals novel possibilities to show, explain, and teach, thus promoting challenge, learning, and even fun.

3.6 The knowledge base Mapper’s expertise components contains a collection of published rules as explicated

in existing cataloging publications (AACR2R 1988; CM 1982; LCRI), and a body of experts’ judgment known as “personal knowledge.”

Personal knowledge, in this study, was required whenever (a) rules implied catalogers’ cartographic knowledge about map publishing (CM 1982, per rule A.2 f Application); (b) rules did not apply; (c) rules called for something that was not there; (d) application rules were suggested; (e) optional rules were suggested; (f) rules were inconsistent between and among themselves. A detailed procedure used to obtain personal knowledge from expert map catalogers was reported in Ercegovac (1990b, in press).

3.6.1 Personal and public knowledge. The proportion of personal knowledge over public knowledge was computed by dividing a number of rules that call upon catalogers’ judgment with a total number of rules required to complete the three tasks under study.

Forty-one percent of the time a cataloger would encounter a call for applying special knowledge explicated in optional rules, application to rules, as well as the presence of phrases such as “use judgment in deciding . . .” (per Rule 25.3B.[New]), “consider what is known about publication history . . .” (per Rules A.2 Application), and “judge by. . . function” (per Rules lF4, Application; lF5, Application).

Both types of cataloging knowledge, namely cataloging rules selected from the published sources and personal knowledge captured from expert catalogers, were integrated into Mapper’s knowledge base. We describe external and internal representation of the knowledge-base next.

3.6.2 Knowledge representation. External representation of cataloging knowledge refers, in this study, to the way knowledge is represented to the user. Mapper prompts novices through a series of screens starting with Task 1 and ending with the publication date of a given map. Navigating through Mapper’s screens requires either pointing-and-clicking with the mouse on available “buttons, ” “text fields,” applicable statements as shown below (per Design Recommendation No. 6, introduced in Section 3.5), or entering strings of charac- ters representing various cartographic values.

For instance, the knowledge about ascertaining the chiefly responsible corporate name for a map in question was captured in the sequence of questions. The user’s response sets internal variables to true/false, making the use of decision rules possible. In a sample of


58 CIA-produced single-sheet maps published between 1983 and 1987, the following conditions were found: (a) a single corporate name has been found in 4% of the examined maps; (b) no map contained personal name; (c) all maps have been printed on recta only; (d) all maps stated a numeric code such as “504828 (546991) 3-88.” Unique publishing characteristics were identified for maps produced by two other map-making agencies. These conditions then were used to form the statements below.

Beginning at the top, please click all statements that are true: 0 Map is located on one side (recta) 0 Map is folded in ‘faccordionff style

0 There are no personal names 0 There are more than two personal names 0 There is a phrase “produced by”

0 There is a code such as 505064( 545039) 1-82 0 There is a two-letter code part such as MZ

In the above example, the user chooses the conditions that come closest to the map in hand by pointing the mouse cursor on the screen and pressing down and releasing the “button.” In order to obtain Mapper’s advice and furthermore to get the explanation of a given advice, the user selects buttons give advice or explain advice, respectively.

A more detailed description of Mapper’s design features with respect to presentation of questions and answers, help, advice, explanation facilities, and display features is discussed in Section 4.

Internal representation of cataloging knowledge refers, in this study, to the way knowledge is coded into data structures of Apple’s HyperCard - Hypertalk. HyperCard may be thought of as a style of designing and implementing systems for information representation and management around a simple node and link model (Halasz, 1988).

4. DESIGN OF MAPPER

We begin the discussion by introducing design principles and Mapper’s objectives. Then the major roles of the system are presented.

4.1 Design objectives Mapper’s design principles draw upon earlier studies of human factors implications of

expert systems (Boden, 1981; Hopkin, 1984; Michie & Johnston, 1984), and the most recent research, which has shifted its focus away from system performance to analysis of human cognitive behavior (Meadow et al., 1989; Borgman et al., 1989). The metaphor of a dynamic book (Frisse, 1988; Egan et al., 1989; Weyer 8~ Borning, 1985), with a set of features, in part drawn from Hopkin’s study (1984), have provided a framework for Mapper’s design and implementation. These features are summarized below:

1. Mapper should ask questions, offer advice, and explain advice. 2. Mapper should integrate and update frequently changing information from differ-

ent sources, while controlling redundancy of that information. 3. Mapper should facilitate a rich representation of the domain knowledge by, for in-

stance, showing a piece of text, listing a set of definitions, and displaying a picture of a map on a screen.

4. Mapper’s design should be modular to allow for easy extensions. In order to promote active learning and a problem-solving environment, Mapper as-

sumes the role of an interactive tutor and, like a colleague, asks questions, helps, advises, and explains. Thus the major roles of Mapper are:

l to provide different types of help facilities; l to ask questions from the cataloger; l to offer advice to a novice map cataloger;

IpI( 28:2-G

248 ZERCEGOVAC~~~ H.BORKO

l to explain the advice suggested to the cataloger; l to display cataloging results.

In addition to the work cited above, Norman’s analysis of errors (Norman, 1983), as well as research in cognitive engineering (Norman, 1986; Hollnagel & Woods, 1983), provided the theoretical basis for the above set of Mapper’s roles. Other functions of the Map- per system are: (a) to collect other types of online data such as unsuccessful trials during the cataloging process with its users; and (b) to report cataloging answers in the form of a record entry.

These functions were implemented in a HyperCard environment which supports a direct manipulation style of interface. A direct manipulation interface may be defined by the visibility of the objects and actions, rapid reversible actions that can be manipulated visually on a screen by using hardware and software devices, such as mouse and tablets, joystick, touch screen, and the like (Shneiderman, 1987).

4.1.1 Screen design and help facility. Mapper’s screens contain two main parts: buttons for accessing various functions of the system, and text fields for displaying information (Fig. 2).

Mapper provides two types of “help” facilities: (a) general help, and (b) specific help. The Mapper’s “help” screens are described next.

4.1.2 General help. General “help” in Mapper is also of two types. The first type of general help tours the user through cataloging tasks that the system is designed to handle. By clicking the About Mapper button, the user activates a description of Mapper’s capabilities and its overall scope. This capability has been implemented on the premise that in order to use the system intelligently, a user needs to understand the scope of the system (Buchanan, 1979).

The second type of general help, invoked by clicking the Help button, is designed to offer a brief overview of some basic HyperCard tools, such as clicking the button, using the mouse, scrolling the window, and the like.

Both types of the two “general help” facilities are based on the mental models research and in particular on the relationship between conceptual and mental models. Norman (1982) mentions conceptual models that have been devised, typically by teachers, design- ers, scientists, and engineers, as tools to represent a target system t.

fE;*y l-O rTixq’ndex_j Help Definitions Repeat Task -1

Mappers’s advice: Buttons

This is a CIA produced map. The form of the corporate name in the authority name file is:

110 l- United States. *b Central Intelligence Agency

/ The tag 1 10 in the MARC format stands

Text Fields -=-_ for the CORPORATE NAME MAIN ENTRY. The first indicator ” 1” stands for the place or place plus name; the second indicator is blank.

Buttons

IExplain aduicel Ill Illustrate I[ Emp. Find. 1

Fig. 2. The layout of Mapper’s screens.


He makes the distinction between conceptual models, C(t), and mental models or user’s models, M(t), defined as the models that people have in their heads of the t in this study of Mapper. While ideally there ought to be a direct and simple relationship between the C(t) and M(t), in reality this is not the case.

In the attempt to close the potential gulf that might exist between the designer’s mental model and the user’s mental model of Mapper, About Mapper and Help were designed and implemented as the two types of simple “help” facilities.

4.1.3 Specific help. An extension of the general help facility is the option the user has to see a list of terms defined and available for browsing at any time during the cataloging session (Fig. 3).

The terms represented those included in the body of rules related to the three tasks under study.

4.2 User-system interaction This section describes issues involved in format and content of information interchange

between Mapper and the user. 4.2.1 Format. With regard to the format of data gathering, Mapper gathers informa-

tion, facts, or beliefs about a map in question by means of using: (a) A“closed” format interchange in which the user selects the applicable statements

that come closest to the conditions presented on a map, from a series of alternatives (sometimes only two);

(b) An “open” format interchange in which the user is prompted to enter data as data present themselves on a map in question.

These two types of data collection are discussed in Maccoby & Maccoby (1954). A “closed” format of data gathering is designed with the following variations. First,

some of the conditions are of the two-choice type where the user is to answer yes or no, true or false, and the like. Second, some of the conditions are presented to the user in the multiple-choice format and the user chooses the condition closest to the condition on a map in hand.

A modification of this technique of data gathering is based on the idea of pattern rec- ognition. The user attempts to recognize the pattern closest to the one on the map out of multiple choices of pictorial patterns derived empirically and presented to the user in the form of a menu. Patterns, as illustrated in Fig. 4, may show publishers’ devices such as logos or other features extracted by the investigator from maps.

Yapper 1.0 riiiiiqlndeH~Help (

Added Entry Alternatiue Tit11 Cartographer EMANATOR (CM, A.2 Application:1 64)

Chief Source of The emanating corporate body is responsible for the intellectual

content, design, and creation of the material. However, the name of COlleCtilJe Title the body is not always printed prominently on an item and it not Corporate Bod& always accompanied by an explicit statement of responsibility.

Where thereisdifficultyindetermining thedegreeof responsibility for the cartographic item that such a corporate body may have had,

Map Author consider what is known about the publication history of the body and

Other Title Info if it is known to be a map-making organization which normally

Parallel Title originates and issues maps, enter under the corporate body.

Prominently ’ Series (OK Series Title Statement of Responsibility Supplied Title

_ Title Proper Uniform Title

(1

Fig. 3. Terminological assistance.

250 2. ERCEG~VAC and H. BORKO

Yapper 1.0 Task 1

In Task 1, Mapper will try to assist you in ascertaining the principal responsibility of a map-maker (the main entry) for a map in question. Please click the logo that appears on your map:

None of the aboue

Fig. 4. Closed format: Multiple choice pictorial.

An “open” format of data gathering refers to one in which expressions give users little or no guidance as to the form or content of their answers. For example, the user is prompted to enter data elements as these present themselves on a map for the area of title and statement of responsibility and the one for the publication area.

4.2.2 Content. With regard to the content of data gathering, Mapper uses questions drawn from the different information sources by different research methods. Both information sources and methods of analysis are considered next.

Three main information sources were considered as follows:

1. Printed textual material included the following three sources. l Cataloging codes (e.g., Anglo-American Cataloguing Rules, 2nd revised edition,

hereafter AACR2R 1988; Cartographic Materials: A Manual of Interpretation for AACR2, hereafter CM 1982; and Library of Congress Rule Interpretations, hereafter LCRI).

l Glossaries and dictionaries (AACR2R 1988; CM 1982; Meynen, 1973) for both cataloging and cartographic terms.

l A manual for cataloging and entering cartographic records into the Online Com- puter Library Center’s machine-readable database, hereafter OCLC’s MARC Map manual (OCLC 1980).

2. Printed cartographic material included a sample of 178 maps for three mapping publishers (Ercegovac, 1990b).

3. Unpublished material included: l Face-to-face interviews with experts in map cataloging of the Geography and

Map Division, the Library of Congress; l Internal memoranda drafted by members of the cataloging unit of the Geogra-

phy and Map Division; l Face-to-face interview with an expert representing views of the National Geo-

graphic Society; and l Manuscript and telephone communication with Larsgaard of the University of

California at Santa Barbara.

Methods of analysis were as follows: Cataloging codes were first partitioned into chapters relevant to the cataloging tasks

under study. In particular, Chapters 1, 3, and 21 from AACR2R complemented rules from two other sets of cataloging codes. Next, redundancy that exists between and among rules from different published sources was reduced using the following procedure:


l If a rule from AACR2R was identical with regard to text to the one from CM, a CM rule was preferred because the latter publication was richer in cartographic examples and illustrations.

l Non-preferred rules, labeled numerically, were included in parentheses. l Lengthy rules containing repetition were “adapted” and indicated as such.

The identified rules were then parsed into a set of variables, which were included as target conditions in Mapper’s questions/expressions.

Dictionaries, both glossaries in cataloging codes and cartographic materials, were examined for those terms that were included in a body of rules. All terms were sorted alpha- betically and defined, sometimes with several definitions from different sources. Source publications were cited.

An unobtrusive method of content analysis was used to identify and analyze problem areas in a sample of 499 printouts of cartographic entries as found in the OCLC cataloging database, Findings of the content analysis resulted in the questions that demanded explanation from experts.

Unpublished material included data obtained from experts and memoranda. The data were examined by means of a semi-structured face-to-face open interview with a detailed interview schedule in addition to a paper-and-pencil forced-format questionnaire.

4.3 Assistance screens Mapper’s advice is activated simply by clicking the button called GIVE ADVICE.

Mapper offers advice with the following levels of assistance: 1. Mapper tries to ascertain the chiefly responsible emanator for a map in question

based upon the data provided by the user during the data-gathering phase. Both user and Mapper are involved in this process, and the level of assistance is said to be “augmented” assistance (Paisley & Butler, 1977).

2. Mapper than automatically formats the name of the emanator according to formal cataloging procedures. This level of assistance is said to be “delegated” assistance.

3. Mapper is designed to automatically generate a series of specific data elements on the basis of responses provided by the user to earlier questions. This level of assistance is termed in this study “default” assistance. For instance, by knowing that a map in question is produced by the Central Intelligence Agency, we also know by default that there is a high probability that personal names as well as statements of responsibility will be omitted; that the publication place will not be stated on a map; and that both the publication date and the publisher will rarely appear on a map. It then follows that once the emanator is ascer- tained, for instance, other data elements are by default inherited and put in place.

Consistent with the design principles and major roles, Mapper provides evidence and rationale to the advice-giving. It does so by offering explanation at different levels of completeness and by means of integrating different forms of explanation. The explanation can be the abbreviated rule (CM 1982, per rule lF2, p. 36), the expanded rule (CM 1982, per rule lF13, p. 40), or a piece of empirical finding (Fig. 5).

4.3.1 Explanation component. Mapper’s explanation component is activated by “clicking” the button EXPLAIN ADVICE. A set of rules that match variables included in questions is presented on a screen. The explanation component consists of two parts. The first part echoes the data provided by the user and puts the data in the “IF” part of the rule. The second part shows the action portion of a rule or a set of rules that pertain to the stated conditions and put these into the “THEN” part of the explanation. The explanation component is designed to accommodate different levels and preferences of potential users of the system.

With regard to completeness, Mapper’s users have a choice of obtaining a brief explanation or a more complete one. The former is typically represented as a rule or a set of rules in a brief form only (Fig. 6); the latter is typically represented by a more complete information consisting of up to one page. As shown in Fig. 7, the upper portion of the screen holds information from the two published cataloging sources, whereas the lower portion incorporates empirical findings (Ercegovac, 1990b).


Empirically-derived rules and physical dercription of CIA mapi

In a sample of 158 CIA produced single-sheet maps and published between 1983 and 1987, the following conditions have been found:

1) a single corporate name has been found in 4 percent of the examined maps; 2) no personal name has been found; 3) all maps have been printed on recta only; 4) all maps have stated a numeric code. The code is in the form:

504828 (546991) 3-83

The code uniquely identifies a specific map and has been used in the transciption of Bublication date as follows:

[Washington, DC. : Central Intelligence Agency, 19831

(a)

Empirical Findings: Physical Description of NGS Examined Maps

In a sample of 37 single-sheet NGS produced maps and published between 1983 and 1987, the following conditions relevant to Task *l have been found:

1) The National Geographic Society has been prominently named in all examined maps (n=37) as the chiefly responsible map-making agency. An indicative wording of the chief responsibility has been found in the phrase “produced by”;

2) Number of personal names who have participated in the process of map-making has ranged between 1 1 and 27;

3) Number of responsibility functions who have participated in the process of map- making has ranged between 9 and 13;

4) The NGS logo, a yellow rectangle, is present in all single-sheet NGS maps; 5) When printedon both sides, NGS maps typically have a contemporary map(s) on recta

and pictorial map(s) on verso.

(b)

Fig. 5. Empirically derived rules for (a) CIA maps, (b) NGS maps.

With regard to the focus of control, Mapper offers two levels of support. These are (a) user-controlled (solicited) explanation; and (b) computer-controlled (unsolicited) explanation. The user-controlled mode offers the user the option of obtaining the explanation by explicitly asking the system to explain its advice. The user simply “clicks” the button EX- PLAIN ADVICE in order to be shown selectable levels of explanation.

Mapper s’s advice:

This is a CIA produced map.

The form of the corporate name in the authority name file is:

1 10 1_ United States. *b Central Intelligence Agency.

Mapper’s advice is as given because --- the map has the CIA logo. See RULES The tag 1 10 in the MARC format stands

for the CORPORATE NAME MAIN ENTRY.

0 The first indicator “1” stands for the

OK place or place plus name; the second indicator is blank.

Fig. 6. Brief explanation.


TASK 1 Rules 82 General rule for entries under corporate body. . .

Enter e work emanating from one or more-corporete-bodies under the heading for the appropriate body if it falls into the following category:

f) cartographic materials emanating from a corporate body other than a body that is merely responsible for their publtcatton or distribution. (AACR2R:3 13 adapted)

A.2 Application. The emanating corporate body is responai ble for the intellectual content, design, and creation of the material. However, the name of the body is not always printed prominently on an item and is not always accompanied by an explicit statement of responsibility. Where there is difficulty in determining the degree of resDOnsibilitU for the

[CIA may be considered as the chiefly responsible agency with a well established cartographic capability producing maps typically with the following set of characteristics: - a numeric code (see “Illustration” button); the use of recta only; the use of pastel colors; the use of soft paper quality with light ink and weight of lining; distinct symbology in legends; sometimes with thematic ancillary inset maps.]

Fig. 7. More detailed explanation.

In contrast, the computer-controlled mode automatically displays the advice with the corresponding explanation. The explanation typically consists of uncovering a simple al- gorithm and showing the inner workings of the MARC format to the user.

According to the design principles and the main objectives of the system, discussed earlier, Mapper uses rich representation of integrated knowledge explicated in a body of cataloging rules as shown in textual and pictorial examples and describes in empirical findings.

4.3.2 Display component. Mapper supports four types of display of cataloging records. First, the working record is displayed incrementally at the level of completed subtask and of task (Fig. 8). Second, a summary can be both displayed on a screen and printed (Fig. 9). Third, aborted trials are recorded and shown (Fig. 10). For example, the data element “other title information” is replaced by the phrase “not done.”

Finally, one’s feeling of space is maintained in the following two ways: First, Mapper keeps the user posted of his or her current task and subtask level with indication in the top upper part of the screen. The message is updated and reflects a current cataloging activity. Second, Mapper allows the user to move around by making the following buttons ex- ecutable. These buttons are:

BEGIN NEXT TASK REPEAT TASK NEXT SUBTASK

GO BACK

INDEX

allows the user to start off the cataloging session. moves the user to the next cataloging task. allows the user to repeat the current task. moves the user to the next subtask within the same cataloging task. allows the user to go back to the main screen from a support screen where such a screen may be an example, explanation, empirical finding, or definition. allows the user to select the desired task.

Example: other title information

245 10 Florida road map : #b with mini-maps of Fort Lauderdalo, Jacksonvtllo, John F. Kennedy Space Center, Miami-Miami Beach, Orlando-Walt Disney World, Palm Beach-West Palm Beach, St.Augustine, Tallahassee, Tampa-St. Petersburg, plus lists of public recreational areas and major places of interest / *c

Fig. 8. Display of partially completed task.


Yapper 1.0 Task 1

WCS YAPEXAYPLE

110x Nbtionsl Geographic Society (U.S.). Mb Cartographic Division.

245 14 The making of America. *p Northern approaches / *xc produced by the Cartographic Division, National Geographic Society ; John 8. Garver, Jr., chief cartographer ; John F. Shupe, associate chief ; chief editorial consultant, D.W. Meinig, principal regional consultant, L. D. McCann.

260 O_ Washington, D.C. : *b TheSociety, *c c1985. . . . .

700 10 Garver, John B. 700 10 Shupe, John P. 700 10 Meinig, D.W. 700 10 McCann, L.D. . . . 740 01 Northern approaches.

Fig. 9. Example of NGS record.

SHOW FINAL ENTRY

END SESSION

QUIT

allows the user to view all completed tasks for a map in hand. allows the user to end one cataloging session and start a new one without logging out of the system. terminates a session and exists Mapper.

4.4 Other functions Mapper currently provides an exit questionnaire online. The objective of the question-

naire is to measure affective variables of users’ perception of Mapper and their overall sat- isfaction with system performance.

5. SYSTEM IMPLEMENTATION

Although expert system shells are viable alternatives for implementing advice-giving systems, they all have the following drawbacks: (a) because of their generality, shells cause extra effort in adapting them to specific tasks: (b) when a given task does not require certain features, such as backtracking, they add to the overall complexity yet remain unused; (c) shells’ interface capabilities, regardless of how general they might be, cannot provide the power of custom-built interfaces. Therefore, we decided to implement the Mapper using the Apple@ HyperCard’” system on a Macintosh computer.

The HyperCard” system, as an implementation medium, is powerful and easy to use. This is due mainly to capabilities for modular design, an event-driven flow of activities, and an English-like programming language.

All three tasks have been completed. The map’s entries are :

Map No. 23

1 10 1, United States. *b Central Intelligence Agency.

245 10 Horn of Africa *b <not done>.

260 O- I Washington, D.C.1 : *b Central Intelligence Agency, *c 1987.

Fig. 10. Record with aborted subtask in 245 field.


We selected the Apple Macintosh HyperCard”” system for the implementation of Map- per for the following reasons: (a) it provides a simple to use yet powerful programming environment; (b) it is very effective in supporting textual and graphical means of user-system interaction; (c) it is easily expandable to accommodate new functions; (d) it is widely available.

Mapper’s home stack contains five cards and uses 19K words of memory. Mapper stack has 79 cards and takes 193K words of memory. Mapper can be used on any Macin- tosh model with HyperCard” software.

For an in-depth discussion of HyperCard”, the reader is referred to books such as Kaehler’s HyperCard Power (1988) and Goodman’s The Complete HyperCard Handbook (1987).

6. CONCLUSIONS

We started by asking the question whether or not specific users’ needs can be articulated sufficiently to incorporate them into Mapper’s interface design. The survey findings suggest an array of users’ skills, preferences, and limitations. An approach taken in this project is to adapt Mapper’s user interface to fit users’ characteristics and compensate for their limitations rather than change users’ characteristics and design training programs.

The question of human-computer compatibility has been addressed in this study by re- viewing some of the claims in the existing psychological literature. The review provided the basis for suggesting Mapper’s interface guidelines.

Cataloging tasks under study have been divided into a total of 23 decision points and mapped to assistant modes likely to be most fruitful to the cataloger’s decision-making process. Mapper’s implementation in the HyperCard environment supports the augmentation assistance mode, which engages capabilities of people and computers in more symmetri- cal and interesting ways.

Mapper’s design principles draw upon earlier studies of human-factors implications of expert systems, and the most recent research, which has shifted its focus away from system performance to analysis of human cognitive behavior.

The principles used in the design of Mapper are: 1. Mapper asks questions, offers advice, and explains advice in an interactive manner. 2. Mapper integrates and updates frequently changing information from different

sources while controlling redundancy of that information. 3. Mapper facilitates a rich representation of the domain knowledge by, for instance,

showing a piece of text, listing a set of definitions, and displaying a picture of a map on a screen.

The study formalized the unpublished body of knowledge into a set of rules, and along with a body of published rules, coded all rules into the data structure of the Apple’s HyperCard” system.

Mapper was tested to determine whether novice map catalogers could use the system effectively in the descriptive cataloging of certain US-produced, single-sheet maps. The results of the evaluation study will be reported in a subsequent article.

We believe that the approach taken in this study for three main cataloging tasks can be extended to other cataloging tasks in the descriptive cataloging of maps. Activities of such tasks produce the elements of a mathematical data area, which includes statements of scale, projection, and coordinates; and a physical description area, which includes information about medium and size. These elements are given numerical names (e.g., 255,300) in the MARC format.

Mapper could be extended to serve several roles: First, Mapper might be viewed as an electronic book. It might offer a rich set of linkages between different editions of the Anglo-American Cataloging Codes; it might show how a “current” cataloging entry would look like in the contexts of different codes; it might also play the role of experts’ memory board; finally, it might be a cataloger’s reference tool. Second, Mapper might be used as an exploratory instructional tool. In a laboratory setting, Mapper could be applied as a treatment-independent variable to test the assumption that Mapper students would learn

256 2. ERCEGOVAC and H. BORKO

cataloging faster and more easily, and produce higher quality cataloging results than would non-Mapper students.

REFERENCES

Aluri, R., & Riggs, D.E. (Eds.) (1989). Expert systems in libraries. Norwood, NJ: Ablex. Bates, M.J. (1990). Where should the person stop and the information search interface start? Information Pro-

cessing & Management, 26(5):575-591. Boden, M.A. (1981). The meeting of man and machine. In K.P. Jones & H. Taylor (Eds.), The design of infor-

mation systems for human beings; Informatics 6; Proceedings of a conference held by the ASLIB Infor- matics Group (pp. 4-15). London: ASLIB.

Borgman, C.L. (1984). Psychological research in human-computer interaction. In M.E. Williams (Ed.). Annual Review of Information Science and Technology, 19:33-64.

Borgman, C.L. (1986). Why are online catalogs hard to use? Lessons learned from information-retrieval studies. Journal of the American Society for Information Science, 37(6):387-400.

Borgman, C.L., Case, D.O., &Meadow, C.T. (1989). The design and evaluation of a front-end user interface for energy researchers. Journal of the American Society for Information Science, 40(2):99-109.

Buchanan, B.G. (1979). Issues of representation in conveying the scope and limitations of intelligent assistant programs. In J.E. Hayes, D. Michie, & L.I. Mikulich (Eds.), Machine Intelligence 9 (pp. 407-425). Chichester: Ellis Horwood.

Card, S., Moran, T.P., & Newell, A. (1983). Thepsychology of human-computer interactions. Hillsdale, NJ: Law- rence Erlbaum Associates.

Carpenter, M. (1989). Main entry. In E. Svenonius (Ed.), The conceptualfoundations of descriptive cataloging (pp. 73-95). San Diego: Academic Press.

Carroll, J.M., & Mack, R.L. (1983). Learning to use a word processor by doing, by thinking, and by knowing. In J. Thomas & M. Schneider (Eds.), Human factors in computing systems. Norwood, NJ: Ablex.

Cartmell, V., & Parker, V. (Eds.) (1982). Cartographic materials: A manual of interpretation for AACRZ. Pre- pared by the Anglo-American Cataloguing Committee for Cartographic Materials. H.L.P. Stibbe (Gen. Ed.), Chicago: American Library Association.

Davies, R. (1987). Outlines of the emerging paradigm in cataloguing. Information Processing & Management, 23(2):1-10.

Egan, D.E., Remde, J.R., Landauer, T.K., Lochbaum, C.C., & Gomez, L.M. (1989). Behavioral evaluation and analysis of a hypertext browser. Paper presented at the Association for Computing Machinery Special In- terest Group on Computer Human Interface (ACM CHI’89) (pp. 205-210). New York: ACM.

Endres-Higgemeyer, B., & Knorz, Cl. (1987). AUTOCAT Darmstadt: Technische Hohschule. Technical Report DV-11-87-5 (AUTOCAT 87-2).

Ercegovac, Z. (1989). Approaches in the design of augmented assistance in online catalog subject searching. In C. Roysdon & H.D. White (Eds.), Expert systems in referenceservice, No. 23 (pp. 21-40). New York: The Haworth Press.

Ercegovac, Z. (1986, May). Artificial intelligence and cataloging reasoning: Promises and pitfalls. Paper presented at the Seventh National Online Meeting (pp. 109-l 17). Medford, NJ: Learned Information.

Ercegovac, Z. (1984, April). Knowledge-based expert systems: A profile and implications. Paper presented at the Fifth National Online Meeting (pp. 39-46). Medford, NJ: Learned Information.

Ercegovac, Z. A multiple observation approach in knowledge acquisition for expert systems: A case study. Jour- nal of the American Society for Information Science (in press).

Ercegovac, Z. (1990a). Proposed definitional conditions as a basis to study the concept of map-author. Cataloging & Classification Quarterly, lO(4): 19-50.

Ercegovac, Z. (1990b). Research on knowledge-based descriptive cataloging of cartographic publications: An experimental advice-giving system-Mapper. Unpublished doctoral dissertation, University of California, Los Angeles.

Fenichel, C.H. (1981). Online searching: Measures that disciminate among users with different types of experience. Journal of the American Society for Information Science, 32(1):23-32.

Fox-Sandberg, M.A. (1972). The amenability of a cataloging process to simulation by automatic techniques. Un- published doctoral dissertation. University of Illinois at Urbana-Champaign.

Frisse, M.E. (1988). Searching for information in a hypertext medical handbook. Communications of the Asso- ciation for Computing Machinery, 31(7):880-886.

Goodman, D. (1987). The complete hypercard handbook. New York: Bantam Books. Gorman, M., & Winkler, P.W. (Eds.) (1988). Anglo-American Cataloguing Rules, 2nd revised ed. Chicago: Amer-

ican Library Association. Halasz, F.G. (1988). Reflections on notecards: Seven issues for the next generation of hypermedia systems. Com-

munications of the Association for Computing Machinery (ACM), 31(7):836-852. Hjerppe, R., & Olander, B. (1989). Cataloging and expert systems: AACR2 as a knowledge base. Journal of the

American Society for Information Science, 40(1):27-44. Hopkin, V.D. (1984). Comment: Some human-factors implications of expert systems. Behavior and Information

Technology, 3(1):79-83. Hollnagel, E., & Woods, D.D. (1983). Cognitive systems engineering: New wine in new bottles. International Jour-

nal of Man-Machine Studies, 18(6):583-600. Howard, H. (1982). Measures that discriminate among online searchers with different training and experience.

Online Review, 6(4):315-327. Humphrey, S.M. (1989). A knowledge-based expert system for computer-assisted indexing. IEEE EXPERT,

4(3):25-38. Jeng, L.H. (1986). An expert system for determining title proper in descriptive cataloging: A conceptual model.

Cataloging & Classification Quarterly, 7(2):55-70.


Kaehler, C. (1988). HyperCard” power-techniques and scripts. Reading, MA: Addison-Wesley. Kidd, L.A. (Ed.) (1987). Knowledge acquisition for expert systems: A practical handbook. New York: Plenum. Library of Congress Rule Interpretations of AACRZ, 1978-1985 (1985). (Compiled by SC. Tseng; 2nd cumu-

lated edition), Metuchen, NJ: Scarecrow Press. Lewis, C. (1988). Why and how to learn why: Analysis-based generalization of procedures. Cognitive Science,

12(2):21 l-256. Maccoby, E., & Maccoby, N. (1954). The interview: A tool of social sciences. In G. Lindzey (Ed.), Handbook

of social psychology, Vol. I (pp. 449-481). Cambridge, MA: Addison-Wesley. Malone, T.W. (1982, March). Heuristics for designing enjoyable user interfaces: Lessons from computer games.

Paper presented at the conference on Human Factors in Computer Systems, Gaithersburg, MD. Matthews, J.R., Lawrence, G.S., & Ferguson, D.K. (1983). Using online catalogs: A nationwidesurvey. New York:

Neal-Schuman. Meadow, C.T., Cerny, B.A., & Borgman, C.L. (1989). Online access to knowledge: System design. Journal of

the American Society for Information Science, 40(2):86-98. Meynen, E. (Ed.) (1973). Multilingual dictionary of technical terms in cartography. Wiesbaden, Germany: Franz

Steiner Verlag. Michie, D., & Johnston, R. (1984). The creative computer: Machine intelligence and human knowledge. Harmonds-

worth, Middlesex: Penguin. Mitchell, C.M. (1987). GT-MSOCC: A domain for research on human-computer interaction and decision aiding

in supervisory control systems. IEEE Transactions on Systems, Man, and Cybernetics, SMC-I7(4):553-572. Moran, T.P. (1981). An applied psychology of the user. Association for Computing Machinery (ACM) Comput-

ing Surveys, 13(1):1-12. Norman, D.A. (1986). Cognitive engineering. In D.A. Norman & S.W. Draper (Eds.), User centered system de-

sign: New perspectives on human-computer interaction (pp. 31-61). Hillsdale, NJ: Lawrence Erlbaum Associates.

Norman, D.A. (1983). Design rules based on analysis of human error. Communications of the Association for Computing Machinery (ACM), 26(4):254-8.

Norman, D.A. (1982). Some observations on mental models. In D. Hentner & A. Stevens (Eds.), Mental models. Hillsdale, NJ: Erlbaum.

Online Computer Library Center: Maps format (1980). Columbus, OH: OCLC. Paisley, W., & Butler, M. (1977). Computer assistance in information work. Palo Alto, CA: Applied Commu-

nication Research. ERIC: ED 146900. Reed, A.V. (1982). Error-correcting Strategies and human interaction with computer systems. Paper presented

at the Human Factors in Computer Systems Conference (pp. 236-238). New York: Association for Com- puting Machinery.

Ross, B.H., & Moran, T.P. (1983). Remindings and their effects in learning a text editor. Paper presented at the Human Factors in Computing Systems Special Interest Group on Computer and Human Interaction (SIG- CHI) and the Human Factors Society Conference (pp. 222-5). New York: Association for Computing Machinery.

Shneiderman, B. (1987). Designing the user interface: Strategies for effective human-computer interaction. Read- ing, MA: Addison-Wesley.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2):257-285. Weyer, S., & Borning, A. (1985). A prototype electronic encyclopedia. Transaction on Office Information of the

Association for Computing Machinery, 3(1):63-88. Woelfl, N.N. (1984). Individual differences in online search behavior: The effecr of learning styles and cognitive

abilities on process and outcomes. Unpublished doctoral dissertation, Case Western University, Cleveland, OH.

Documents

Design and implementation of an experimental cataloging advisor-mapper