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Making a digital library: the contents of the CORE project.

The CORE (Chemical Online Retrieval Experiment) project is a library of primary journal articles in chemistry. Any library has an inside and an outside; in this article we describe the inside of the library and the methods for building the system and accumulating the database. A later article will describe the outside (user experiences). Among electronic-library projects, the CORE project is unusual in that it has both ASCII derived from typesetting and image data for all its pages, and among experimental electronic-library projects, it is unusually large. We describe here (a) the processes of scanning and analyzing about 400,000 pages of primary journal material, (b) the conversion of a similar amount of textual database material, (c) the linking of these two data sources, and (d) the indexing of the text material.

Categories and Subject Descriptors: H.3.2 [Information Storage and Retrieval]: Information Storage; H.3.6 [Information Storage and Retrieval]: Library Automation -- large text archives; H.5.2 [Information Interfaces and Presentation]: User Interfaces -- interaction styles; I.4.5 [Image Processing]: Reconstruction

General Terms: Algorithms, Design, Experimentation, Human Factors

Additional Key Words and Phrases: Image segmentation

1. INTRODUCTION

Rapid evolution of telecommunication networks is feeding the demand for document delivery to the desktop [Birmingham et al. 1994; Kibbey and Evans 1989; Lynch 1989; McKnight 1993; Schatz et al. 1994]. The CORE Project is a model for the conversion of large text and graphics collections to an electronic format that will serve the document delivery needs of scholars in a distributed networking environment. As such it provides a realistic laboratory environment in which to investigate the technological problems associated with full-text retrieval and delivery.

The CORE project has five objectives:

(a) define a suitable architecture for delivery of full-text information in a distributed networking environment with heterogeneous workstations;

(b) convert and mount a critical mass of chemistry journal data in a database format suitable for effective retrieval and display;

(c) study the elements of full-text interface functionality necessary to serve the needs of scholars' in a network document delivery environment;

(d) advance the understanding of suitable document markup for electronic full-text databases; and

(e) investigate information retrieval questions germane to the coming era of full-text delivery.

The most important property of an electronic library, just as of a paper library, is its collection [Miksa and Doty 19941. Selection of quality material is the first important requirement; junk, even presented with a lot of glitz, is still junk. Interviews with Cornell chemists pointed to primary journals as their most important resource; fortunately, the CORE project has been able to obtain its material from the American Chemical Society (ACS). ACS publishes the chemistry journals of highest prestige in the United States, and its journals are a key resource for chemical research. We use the 20 primary journals being published at the start of the project.(1)

The users of our library were students, faculty, and employees at Cornell University, in the chemistry department and in many related departments (chemical engineering, materials science, and so on). Their desktop equipment ranges from PCs and Macintoshes to high-powered Unix workstations. Connectivity, originally, was also varied. In an effort to be able to rely on some minimal capabilities, we (a) accelerated the deployment of an Ethernet in the chemistry department and (b) asked all users to have at least 900 x 700 pixel screens. Some of our data have also been made available (with permission of ACS, of course) to University College London where related experiments have been done, including testing different levels of network access and terminal equipment [Kirstein and Montasser-Kohsari 1995]. Even electronic libraries need librarians. In our case, the staff of the Albert Mann Library at Cornell has had to provide not only program installation, but also user counseling and advice. This has required considerable technical consultation about X-windows idiosyncrasies, e.g., security procedures, and other setup issues.

The CORE system is built on a Unix-based client-server architecture that runs on the departmental Ethernet at the Cornell University Chemistry Department, connected to a server at the Albert Mann Library. Figure 1 illustrates the components of the system, showing the flow of information from the providers, the American Chemical Society and Chemical Abstracts Service (CAS), to the files, to OCLC's Newton search engine, and then to the users. The searchable text and extracted graphics reside on magnetic storage for rapid retrieval. OCLC (the Online Computer Library Center) has provided the Newton retrieval system, which supports a variety of OCLC's database and reference products and which serves as the database engine for both of the user interfaces employed in the project. Newton is a client-server database system that provides substantial flexibility from the standpoint of data design and from a networking point of view.

[FIGURE 1, ILLUSTRATION OMITTED]

Accumulating the database has taken more time and money than coding the viewing software. At the time of writing, our main server has a SONY jukebox with 15OGB of WORM (write-once-read-many) storage plus about 75GB of conventional magnetic disk storage. All of this has to cope with about 400,000 page images, in two sizes, plus about 4GB of text, another 6GB of graphics data, and 7GB of indexing. Some electronic-library projects are text-based and some image-based; the CORE project is unique in having both information sources and in being able to compare the results. We have used the database accumulated by the American Chemical Society as part of their publication process [Love 1986] and have scanned the journal pages. Users can access either format. The size of the system is summarized in Table I; image pages run about 100KB each.(2)
Table I. Size of the CORE Data

             Item                      Count

Pages (complete)                      428,819
Articles                               81,635
Megabytes of Text                   4,360.787
Megabytes of Text Plus Indexing    11,097.770
Extracted Graphics                    399,606
Disk Space for Graphics                   6GB


The text and index are not compressed; compared to the amount of space required for the images, it would not reduce the storage requirement enough to be worth the complexity and delays in access.

2. DATA ACQUISITION -- SCANNING

The image data are obtained both by scanning paper and scanning film. Paper scanning is relatively straightforward and routine; an Improvision scanner (based on a Ricoh paper-handling mechanism) is connected to a Sun workstation. It can scan about 20 pages a minute and costs about U.S. $13,000. An operator fed most of the sheets one at a time, since the ACS paper is fairly thin. The output is 300 DPI, one bit per pixel. The two sides of each sheet are scanned in separate passes, and the page numbers are sorted out later. Quality is very high except for some skew, arising either from improper trimming of the journals or misfeeding of the paper. We scan at one bit per pixel, since virtually all the pages contain only straightforward text and line drawings. About 1% of the pages contain a photograph, and 0.1% of the pages may contain color; these suffer significant degradation.

Film scanning is more complicated. Some was done on a Mekel scanner at Bellcore, but the size of the job caused us to contract out the scanning, to two different vendors. Microfilm scanning is a less mature market, and there have been problems with extra dots introduced into the scans and with stuttering (scanning the same frame twice). Although most of these basic problems have been solved, the quality of scanning from microfilm is still much lower than from paper.

An additional complexity in microfilm scanning was the presence of "supplementary pages" on the film which have to be removed. When an article begins in the middle of a page, the supplementary pages are inserted by cutting the page and filming each half separately. Although one can sort this out fairly well by using the page density, counting the number of columns in the image, or trying to OCR the page numbers, all of these have problems, and in the end we relied on manual identification and cleanup.

Maintaining quality was a constant effort; scanning of even 200-DPI resolution from the microfilm was considered inadequate, and we moved to 300 DPI for both paper and film. Quality was a particular problem with reprinting, which needs better images than screen display, as the users are familiar with the high quality of the normal printed journals and would like our scanned pages to reprint at an equivalent level; they have fewer a priori expectations of screen images.

Although we have discouraged users who had minimal (640 x 480) screens, even the larger screens (1152 x 900) are inadequate for a full page at 300 DPI. We eventually decided to store all images twice, once in full resolution (usually, 2560 x 3328) and once in a 3X reduced version (856 x 1109), with grey scale added. Computing grey scale from the full-size images to the smaller images lets us improve readability (effectively antialiasing). It also takes advantage of the fact that most of the users who have the smallest screens, and who must depend on the small-size images, also have color displays.

Displaying also involves response time issues. Where should operations such as scrolling and Tiff decompression take place? In general, we have been limited by X considerations: we do those operations that can be specified in the window manager (e.g., scrolling) on the user machine, and we do Tiff decompression on the server. This means that fully decompressed images must be sent to the user machine from the server, consuming both network bandwidth and memory on the user machine. As mentioned before, many of the users with small screens also have color displays and can thus use the grey-scale information in the reduced bitmaps to improve legibility; unfortunately these machines also, in practice, have the slowest CPUs.

Response time also depends on image storage. One consequence of the size of our files (about 10OGB total) is that many of our page images are out on the WORM jukebox. This introduces an unavoidable delay into the retrieval of full-page images; fortunately the average time taken to read a page is about 90 seconds, so the throughput for those reading articles thoroughly does not have to be very high. In an effort to minimize the jukebox delays, we have implemented a simple read-ahead cache; the program fetches the first five pages each time it gets a new article from the jukebox; in 5000 page displays, the page requested is within five pages 54% of the time. Nevertheless, for browsers faster response would be desirable.

3. FIGURE EXTRACTION

Although the typesetting and database creation process of the American Chemical Society provides the complete text of the articles, it does not give us the graphic elements of the pages. There is an average of one illustration per page, and about a quarter of the average page is not text (measured in square inches). Half the pages, however, have no graphics at all. The illustrations are essential to the understanding of the article; online full-text systems which leave out the illustrations are often used merely as finding aids, rather than as replacements for the paper. Our only route to these figures is to find them in the page images. Since we wish to find 399,000 figures and schemes in 80,000 articles with 428,000 page images, we have to do this automatically.

There are four kinds of nontext elements in the pages: tables, equations, figures, and schemes. The equations and the tables are in the ACS-provided data, so that we only need to find the figures and the schemes in the page images. Both figures and schemes are visually similar: they are both line drawings which in the case of figures may include chemical structures, spectrograms, diagrams of equipment, and so on; schemes are usually chemical structures. They must be sorted out, however, since the schemes and figures can be moved past each other in the course of typesetting: that is, scheme 1 may be referred to in the article before figure 1, but may appear after it on the page. Figures are always at either the top or bottom of the page, while schemes can appear in the middle of a text column (but often appear at the edge of a page as well). Despite an attempt to avoid OCR in general, the only really reliable way of sorting figures from schemes is to find the caption by looking for the word "Figure" as a bitmap.

Page segmentation is a well-studied problem [Fletcher and Kasture 1987; Srihari et al. 1994; Wang and Srihari 1989], but often the previous work has involved halftones or other material which is locally different from text printing. Nearly all our illustrations are line drawings; and there is a continuum between some tables or chemical equations and some figures or schemes. We therefore wrote our own programs to deal with graphics extraction, based on the regularity of lines in normal text. Figure 2 shows a part of a journal column, and Figure 3 plots the number of black bits per scanline. The column begins with a normal figure (a line drawing), which runs from scanline 200 to 800, with irregular low values of bits per scanline (the bump at 150 is the heading line on the page). A five-line caption follows the five regular bumps from scanline 800 to 1000. There follows an unusual dark figure (1100 to 1700), with a two-line caption (1700 to 1800). The remainder of the column (1800 on) is lines of text, and regular bumps appear in the plot. The regularity of this density plot of ordinary typeset text separates it from the irregular-density characteristic of figures. For speed, we use only overall measures of bit density, not exact character matching (except for the word "Figure" as mentioned earlier).

[FIGURES 2, ILLUSTRATION OMITTED]

[FIGURES 3, GRAPH OMITTED]

The first step in graphics extraction is to align the page accurately on the axes. As mentioned before, the cutting or scanning of the pages can introduce skew. Correcting this is not trivial because the top or bottom of the page may include a large figure with white space around it. We find the left edge of each scanline and find the line down the page pushed as closely as possible up against the text to find the skew. The page must be within one degree of correct orientation for the rest of the software to work. Another method for deskewing was discussed by Baird [1987].

After deskewing, we break the page into regions of single and double column. Normally ACS pages are double column, but there may be one region of full-width material, either the title/author block for an article beginning on the page or a full-width figure or table. The program looks at the vertical density plot (how many black bits in each vertical stripe down the page, taken in thirds), identifies the column boundaries, and locates the transitions between double column and full-width areas. Eventually, the page is cut into at most five regions (two columns at the top, two columns at the bottom, and one full-width section).

Each region is then scanned for figure captions, using exact bitmap matching for the word "Figure" (since the journals use different typefaces, different masks are used for different journals). Each region also has its horizontal densities computed, and the program sets out to compute the autocorrelation function, that is, for density d(i) on scanline i,

F(t) = [Sigma] d(i) x d(i + t)

summed over a range of about a third of the page at the bottom (most likely to be straight text). This function will be maximized at the value of t corresponding to the line spacing. Given the line spacing, it is then computed for the whole page, over a range of about five line spacings, and a threshold is used to select which parts of the page are graphics. Figure 4 shows a sample column with the density function plotted just to the left of the text and the autocorrelation function at the far left. Figure 5 shows the result of the whole process: the figures and schemes have been spotted correctly by the program and marked out (using different boxing to represent the figures and schemes separately).

[FIGURES 4, GRAPH OMITTED]

[FIGURES 5, ILLUSTRATION OMITTED]

4. DATA ACQUISITION -- ASCII

The data which come in ASCII are provided in a formatted representation by ACS.(3) In this format, numerical field labels encode a variety of data types: ISSN, title, sentences, subheads, and the like. Separately a special character set is used. In the ACS format subscripts, superscripts, and font shifts are encoded in the character set. ACS also uses various extra kinds of data representation: some footnote references are marked with ##-delimited strings, and equations have another entire formatting system in the text. ACS was one of the leaders in computer typesetting of primary journals more than 20 years ago, and this format, although not following the recent SGML (standard generalized markup language) philosophy, has been effective for their database maintenance. ACS keeps the database accurate (there are no pasted-in corrections) and linked to the actual journal page numbers.

The ACS' proprietary data markup represents the structure of the data, unlike computerized typographic systems that have become commonplace in the publishing industry as a method of expressing the structure and layout of documents. To produce a single portable database which conforms to current standards and to simplify our software construction problems, we adjusted the ACS' proprietary markup by changing to SGML. SGML is an ISO standard for the description of text in machine-readable form, and converting to it helps feed into software systems that can mount databases of broad scope and diverse sources [Coombs et al. 1987]. CORE text is being translated by Bellcore from ACS' structured files into SGML. The latter marked-up version of the text is then used to build the Newton database that will serve the various user interfaces in the project.

Markup of this database is critical: it defines the structure of the database and differentiates document elements for display purposes. SGML tagging permits interfaces to search for specific document components. The use of SGML greatly enhances the flexibility of a database, but defining the structure of the database in advance requires a thorough understanding of the functionality that the user interface will require. A major activity in the CORE project during 1993 has been a collaborative, iterative refinement of the database model such that the full CORE database could serve the needs identified in previous user studies as well as enhancements that have been added. That a single database is to serve all of the user interfaces adds further to the complexity of this design process.

An SGML document consists of text that is marked up with descriptive tags that specify the function of a given element within the document. As a formal language construct, an SGML document can be parsed against a Document Type Definition (DTD) that unambiguously defines what elements are allowed and where in the document they can (or must) occur. This formalized map of article structure allows the user interface design to be uncoupled from the underlying database system, an important step toward interoperability.

Conversion to SGML produces some complexity. For example, in the character representation used for the ACS publication system font changes and baseline position are handled in the character set, rather than with tags: that is, the same mechanism that would change an "a" into a "b" changes an italic "a" into a bold "a" or a superscripted "a." We must convert this mechanism into SGML tags and make some reasonable decisions about which tags to use and where to use them (e.g., an author name in italics should be tagged as an author, not just as an italic string).

SGML by itself does not define the semantics of the document under description. We began with the AAP Electronic Manuscript Standard (EMS) [AAP 1987], but it does not contain enough tags for some of the more complex parts of the CAS format. For example, there are super-superscripts, tables of definitions of symbols, and a complex set of subfields within the Chemical-abstracts subject indexing. We have defined extensions to the EMS to provide new tags for these complex data. Even many years after CAS defined their database representations, the official standards organizations still have not agreed on how to formulate such complex material. There are currently some 85 tags that are used to identify the function of text elements in the data, including not only the usual article titles, authors, author affiliations, section headings, bibliographic elements, but also the abstracting and indexing elements (especially important in a field such as chemistry where there is an unusual systematized nomenclature for compounds that is very frequently used and important for retrieval). In addition we must include the pointers to several different kinds of graphic elements. On the other hand, there are aspects of the EMS we do not use: the three different ways to specify Greek letters reinforces the old saw "the good thing about standards is that they provide so many choices," and we have used only one.

Tables and equations are handled from the ASCII data, to avoid dependence on bitmap clipping whenever possible. Tables are converted to both monospaced layout and SGML. The monospaced layout simplifies final presentation; the SGML version was provided in case future development produced a desire to write a proper table-formatting program. Tables are particularly hard to manage on a variety of screens: it would be necessary to determine character widths on the final display unit and adjust the table accordingly. It simplified matters considerably to provide a monospaced version.

Equations are more complex. No monospaced version will be legible. We felt there were three choices: (a) implement enough of Postscript to typeset equations, (b) produce an equivalent of Postscript in some other text display format we designed, or (c) send bitmaps to the workstation. Choice (b) seems silly, and choice (a) seemed like too much effort for the benefit. So we opted for choice (c), since the interface software must support bitmap display in any case (for figures). We take the ACS equation format, translate it to Unix "eqn" format, run it through the Unix typesetting tools to get bitmaps, and then treat these as if they had been scanned. The major disadvantage of this process is that one cannot click-search on text within equations, but there is rarely anything in an equation which makes a sensible search term.

Two particular features of AAP/EMS SGML were so inconvenient we avoided them. AAP/EMS relies on nesting to disambiguate certain kinds of tags, e.g., the title of an article cited in a bibliography gets the same tag as the title of the main work. We found it much easier to define separate tags for titles, authors, and so on within a bibliography. Also, AAP/EMS uses character escapes distinguished only by case, e.g., "&Agr;" and "&agr;" for upper and lower case Greek respectively. We use double letters instead to be compatible with searching tools that do not make case distinctions. Since the Greek alphabet is used largely for mathematical or chemical symbols, rather than to include text written in the Greek language, users need to be able to search for the Greek letters in upper or lower case.

Among the more complex aspects of conversion are linking up bibliographic references in the text to their citations. Some of the journals mark this unambiguously in the data input; others use superscript numbers; others italic numbers in parentheses; and others use the form "(Smith, 1990)" (and one journal changed its style during the time period of our collection). The program makes various guesses to try to decide when a superscript is a mathematical exponentiation or chemical atomic number and when it is a footnote.

Formatting text for online display is a complex tug-of-war between the user, the system, and the platform. In systems where all users have one kind of workstation, under the control of the system administrator, this can be fairly straightforward. In our context, users have a variety of machines and run a variety of software, and we cannot realistically tell them to change.

Rather than attempt to describe each user display terminal and configuration, and write typesetting software for every one, we used X as our display standard, running emulators on the Macintosh. Even X, unfortunately, is far from standard: each terminal may (and often does) have different font sets installed, and we were forced to install some standard fonts on the terminals plus a font of our own to provide many chemical and mathematical symbols. To save font design effort, some overstrike approximations are used for accented characters not found in standard Adobe layouts.

Display management raises control issues. Who should set up the arrangement of windows on the screen -- the display software or the user? Even within the group of authors of this article, there is no agreement; in practice this means that some users are willing to place every window separately in order to maintain total control of screen content. The software must thus permit and tolerate this, including people who make certain windows either much smaller or much larger than the original program designer anticipated. The strategy of grabbing one large window and then subdividing it was rejected by some users.

5. DATABASE DESIGN AND USE

Database design and user interface functionality are necessarily closely coupled: effective database design must be informed by the desired interface functionality, and interface functionality is constrained by the practical limitations of database structure. On the other hand, we wished to compare the various interfaces, without duplicating the indexing files if possible. Usability has been the prime concern in all interface design work.

Three interfaces were coded, two of which are being used in the final experiments. Both of these interfaces take advantage of the X-Windows windowing system. X-Windows has several advantages that make it the system of choice for interface development in a distributed network environment. Its most important characteristic is that it provides the ability to create a single application that will run on a variety of hardware platforms without machine-specific dependencies. X-Windows servers are available for virtually all PCs, Macs, and workstations; the server manages all display functions and hence frees the application from machine dependencies. As a client-server application, the use of X-Windows also simplifies distributed networked applications. The user interface code, the database search engine, and the user's display can be running on different machines as appropriate to the environment, including at geographically distant sites.

The page layout of a well-designed publication is important to ease of assimilation and comprehension by the reader. This is one of the areas where computer interfaces fail; it is difficult to convey on a computer display the richness of page layout and data markup that is taken for granted on a printed page [Kling and Elliot 1994].

Figure 6 shows OCLC's SCEPTER (Scientific Electronic Publishing and Text Retrieval) interface. The screen display is formatted "on-the-fly" based on the SGML markup in the database. The user interface interprets the markup in the data it retrieves and lays out the screen display according to a set of display rules. There has been no attempt to emulate the printed page in style, but rather to display the data in a pleasing, logical manner that will enhance users' ability to effectively identify relevant articles and information, either for reading on the display or to print for subsequent usage.

[[FIGUE 6, ILLUSTRATION OMITTED]

The inherently greater flexibility of displaying text on a screen is both boon and bane. On the one hand, the user has the flexibility to navigate in the database in new ways, following links from article to article, or browsing lists of related articles, or figures. The power of hypertext-style searching in a large collection should be an important tool for scholars.

On the other hand, providing intuitive interfaces that are both powerful and easy to learn is a poorly understood art that will evolve with experience. Users can fill a screen with a bewildering array of windows that can be difficult to arrange sensibly, especially given the limited screen real estate of current display technology.

The basic metaphor for the SCEPTER user interface is to provide the user with a variety of ways to construct lists of relevant documents for subsequent viewing:

(a) browsing indexes (a dozen or more specific indexes will facilitate the construction of highly specific queries),

(b) searching for known terms (full boolean capabilities are available to support query construction), and

(c) using date and journal restrictors to limit queries.

Documents may then be loaded into the document viewer portion of the program. The viewer includes a table of contents of the article, always visible on the left-hand side of the "page" display. This table of contents is built dynamically from the headings and fixed lists figures, tables, and references) for each article as retrieved. With the mouse the user can display any of these sections by selecting the table of contents entry.

Small icon buttons are visible within the text; each of these icons represents a link to a particular type of data: a citation, footnote, equation, table, or figure. Selecting a particular icon brings up a separate window containing the particular data item represented by that icon.

Having displayed such a figure or reference, it is possible to cycle through "previous" and "next" elements of that series, as well as to return to the place in the text where such items occur.

It is also possible to jump to a referenced article if that article is represented in the database. This ability to follow citation links through a large database is one of the more powerful features of an electronic document database that will save scholars a great deal of effort.

Another interface is an image interface, now called Pixlook, which displays the scanned images directly and only has to print the titles and authors from the ASCII text. Pixlook is written using John Ousterhout's Tk/Tcl package [Ousterhout 1994], supplemented with C programs to process our various page image formats. A sample display is shown in Figure 7. it only displays the images of retrieved pages, similarly to systems such as RightPages [Hoffman et al. 1993] or Adonis [Stern 1990]. Originally, this program had its own search system, which relied on an inverted file that pointed to approximate locations in the full file; this did not support word adjacency searches, but it was very compact. Subsequently, we converted this program to use the same search engine as SCEPTER. Pixlook offers two basic modes of choosing what to read:

(a) the user may browse through lists of article titles;

(b) the user may search on words or Booleans;

and there is only one way to view the result, namely to look at the page images.

[FIGURE 7, ILLUSTRATION OMITTED]

In the browsing mode, users click through menus of journals, years, and issues to get to the list of authors and titles in any one issue. Clicking on any line in the author-title display brings up the first page of the corresponding article. In the searching mode, the user types a search request into the lookup window, and the system returns the authors and titles of the hits. If there are a great many hits, the system gives the first 50, an estimate of how long it will take to find the rest, and a button to ask for them. Again, clicking on an author-title line brings up the first page of the article.

Each page display starts out in low resolution (100 DPI equivalent, 2 bits per pixel on displays which can support grey scale or color). A set of buttons at the top controls (a) moving to or from a full-resolution 300-DPI image of the page (only a small part of which will fit on the screen, of course);

(b) moving to the next or previous article in the list from which this one was picked; (c) requesting a printout of the article;

(d) obtaining help or closing the window.

Scroll bars at the right and bottom move around a page image which does not fit on the screen (always the case for enlarged 300-DPI images), and a "scrollbar" at the top moves from page to page within the article (in discrete pages only). This last bar also gives the user a cue as to how many pages there are in the article.

We have also, in some experiments, used a version of Bellcore's Super-Book [Egan et al. 1989] interface for evaluation. In an earlier paper we presented the results of this work, which demonstrated the enormous advantages of computer systems for any kind of application involving searching [Egan et al. 1991]. SuperBook is a text display system with a strong linear model of the documents, which must be organized into a hierarchy. Unlike Pixlook and SCEPTER, it does not provide bitmaps except for graphics, and its searching system offers less flexibility than SCEPTER. For these reasons it was not used in the final experiments.

6. NEWTON SEARCH ENGINE

The Newton search engine was designed and implemented at OCLC for access to tree-structured data of arbitrary complexity. The search engine may be used as the basis of a fully functional information retrieval system for structured textual data and images. Text in the CORE data is marked up in SGML, but any formal markup language can be used to define the process for designing and building the search engine database.

6.1 Searching/Browsing Capabilities

Newton provides extensive searching capabilities including keyword and full Boolean retrieval as well as proximity and field restriction retrieval. Range searching, truncation, and wildcard searching are fully supported. All database indexes are browsable.

Because there is a well-defined application programmer's interface to Newton, a variety of user interfaces may be built to use the search engine. This allowed us to support both Bellcore's Pixlook and OCLC SCEPTER with a single database.

6.2 Database Preparation

One of the strengths of Newton is the power and flexibility of the indexing system. The indexing programs are driven by a database description file. This file contains information that describes the structure of the input records and the rules for indexing each of the fields of the record. The language in the database description file is compiled by the indexing programs into tables that are stored in the database files themselves and are used to drive the initial loading of data and all subsequent updates and additions to the database.

Newton recognizes both physical and logical database views. A logical database can grow quite large, on the order of hundreds of millions of terms and millions of records. A logical database is a special way of grouping smaller, and hence more manageable, physical database partitions so that they appear as a single unit from the searcher's point of view. This allows the database to grow to an almost limitless size through the simple expedient of adding additional partitions.

Various retrieval problems, many of them familiar in information retrieval, have turned up during our testing.

(a) There is a large range of user expertise, and many users do not know much about fielded Boolean searching. SCEPTER relies heavily on menus to select journals or years to search to avoid forcing the users to learn how to write complex search language expressions.

(b) The database is very large, and it is easy to write a query which retrieves a staggering number of documents. We retrieve the first 50 documents for such queries and suggest that the user rephrase the query. Another possibility would be to use relevance feedback to do the rephrasing automatically.

7. SUMMARY

After several years, what have we learned from the CORE project about building large digital library systems? One is that the database is more work than the interface; the job of managing large quantities of data, even from only one publisher in only one subject area, should not be underestimated. In particular, data which involve more than flat ASCII are tricky. There is inadequate standardization of special character sets, procedures for subscripting/superscripting, printing equations, and so on; yet if this is not done reasonably well the users will not be able to read the result comfortably. Other lessons from data preparation are that we should have bought enough magnetic disk storage at the start of the project and that one must scan gigabytes, not just a few articles, to find all the special cases for the format conversion software.

Another important lesson is the value of pictures to the users [Ferguson 1992]. They like looking at them, and in fact we prepared at one point an interface (Figure 8) in which articles were represented by merely a list of authors and small-size images of the pictures, which we called the "comic book" version. Anything that can be done to make the pictures easier to view would be helpful. We have also looked at classifying the pictures directly rather than accessing them through the articles, so far without success. The users frequently look first and mostly at the pictures, though, so interfaces based on them will be more successful.

[FIGURE 8, GRAPH OMITTED]

Similarly, the users are very interested in browsing. The search metaphor which dominates the retrieval field is not the only way to look at a digital library, and not even the most important. Further work in this area should emphasize catering to people who do not know what they want until they see it.

REFERENCES

AAP. 1987. Standard for electronic manuscript preparation and markup. Version 2.0. Assoc. of American Publishers, New York.

Baird, H. S. 1987. The skew angle of printed documents. In Proceedings of the SPSE 40th Conference on Hybrid Imaging Systems. Society of Photographic Scientists and Engineers, Rochester, N.Y., 21-24.

Birmingham, W. P., Drabenstott, K. M., Frost, C. O., Warner, A. J., and Willis, K. 1994. The University of Michigan Digital Library: This is not your father's library. In Digital Libraries '94 Proceedings. Texas A & M Univ., College Station, Tex., 53-60.

Coombs, J. H., Renear, A. H., and DeRose, S. J. 1987. Markup systems and the future of scholarly text processing. Commun. ACM 30, 11 (Nov.), 933-947.

Egan, D. E., Lesk, M. E., Ketchum, R. D., Lochbaum, C. C., Remde, J. R., Littman, M., and Landauer, T. K. 1991. Hypertext for the electronic library? CORE sample results. In Proceedings of Hypertext '91. ACM, New York.

Egan, D. E., Remde, J. R., Landauer, T. K., Lochbaumk, C. C., and Gomez, L. M. 1989. Behavioral evaluation and analysis of a hypertext browser. In Proceedings of CHI '89: Human Factors in Computing Systems. ACM, New York, 205-210.

Ferguson, E. 1992. Engineering and the Mind's Eye. MIT Press, Cambridge, Mass.

Fletcher, L.A. and Kasturi, R. 1987. Segmentation of binary images in to text strings and graphics. In Proceedings of SPIE Conference on Applications of artificial Intelligence V. Vol. 786. SPIE, Bellingham, Wash., 533-540.

Hoffman, M. M., O'Gorman, L. O., STORY, G. A., Arnold, J. Q., and Macdonald, N. H. 1993. The RightPages service: An image-based electronic library. J. Amer. Soc. Inf. Sci. 44, 446-452.

Kibbey, M. and Evans, N. H. 1989. The network is the library. EDUCOM, Rev. 24, 3, 15-20.

Kirstein, P. and Montasser-Kohsari, C. 1995. The C-ODA project: Experiences and tools. Comput. J. 38, 8, 670-680.

Kling, R. and Elliot, M. 1994. Digital library design for organizational usability. In Digital Libraries '94 Proceedings. 146-155.

Love, R. A. 1986. Chemical Journals Online (CJO) -- The new full-text database through STN International. In Proceedings Online '86. Online, Inc., Weston, Conn., 149-151.

Lynch, C. A. 1989. From telecommunications to networking: The MELVYL online union catalog and the development of intercampus networks at the University of California. Libr. Hi Tech 7, 2, 61-83.

McKnight, C. 1993. Electronic journals -- Past, present ... and future? ASLIB Proc. 45, 7-10.

Miksa, F. L. and Doty, P. 1994. Intellectual realities and the digital library. In Digital Libraries '94 Proceedings. 1-5.

Ousterhout, J. 1994. Tcl and the Tk Toolkit. Addison-Wesley, Reading, Mass.

Schatz, B., Bishop, A., Mischo, and Hardin, J. 1994. Digital library infrastructure for a university engineering community. In Digital Libraries '94 Proceedings. 21-24.

Srihari, S. N., Lam, S. W., Hull, J. J., Srihari, R. K., and Govindaraju, V. 1994. Intelligent data retrieval from raster images of documents. In Digital Libraries 94 Proceedings. 34-40.

Stern, B. T. 1990. ADONIS -- A vision of the future. In Interlending and Document Supply, G. P. Cornish, and A. Gallico, Eds. British Library, 23-33.

Wang, D. and Srihari, S. N. 1989. Classification of newspaper image blocks using texture analysis. Comput. Vis. Graph. Image Process. 47, 327-352.

(1) These are Accounts of Chemical Research, Analytical Chemistry, Biochemistry, Chemistry of Materials, Chemical Research in Toxicology, Chemical Reviews, Energy and Fuels, Environmental Science and Technology, Inorganic Chemistry, Industrial and Engineering Chemistry, Journal of Agricultural and Food Chemistry, Journal of the American Chemical Society, Journal of Chemical Engineering Data, Journal of Chemical Information and Computer Science, Journal of Medicinal Chemistry, Journal of Organic Chemistry, Journal of Physical Chemistry, Langmuir, Macromolecules, and Organometallics.

(2) Two numbers are given for the page count, since some pages have not yet been processed to extract graphics.

(3) Chemical Abstracts Service (CAS), which is a division of the American Chemical Society, provides composition services to the Publications Division -- the publisher of the primary journals. For the CORE project, CAS provided the corresponding index data (in particular, the controlled vocabulary index terms and the CAS registry numbers) for each article so that users could also retrieve information by searching those parts of the Chemical Abstracts content.
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Title Annotation:Chemical Online Retrieval Experiment
Author:Entlich, Richard; Garson, Lorrin; Lesk, Michael; Normore, Lorraine; Olsen, Jan; Weibel, Stuart
Publication:ACM Transactions on Information Systems
Date:Apr 1, 1997
Words:6965
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