Chapter 10

Future Directions inInformation Retrieval

0

PREVIEW

This concluding chapter considers new developments which may be expectedto ^affect the information retrieval world in the not too distant future. In a few
years the text entry problem will have been solved either by using character
recognition equipment to recognize textual materials or through the widespreaduse of word processing. It will also be possible to store full documents digitallyor in microform, and to use automated graphics equipment to handle illustra­tions and pictures. Various advanced technologies that may be helpful for theprocessing of full documents are briefly introduced. This is followed by a sum­mary of theoretical approaches for the representation and analysis of document
content, including fuzzy set theory, term dependency analysis, and composite
document representations. A number of sophisticated automatic document
processing systems are also described that will be capable of processing fulldocuments and of servicing large user populations.
Eventually paperless electronic systems may be created which will offer awide variety of individually tailored information services to the users. Suchsystems could include many facilities that are not yet currently available, in408

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eluding natural language recognition, graphics processing, speech recognition,and inexpensive point-to-point communications.
1

INTRODUCTION

The material contained in the previous chapters covers the existing theory andpractice in information retrieval, as well as various extensions that could in
principle be implemented at once in the proper environment. This last chapter
deals with new ideas and technologies that may be just beyond the current stateof the art. Technological innovations are mentioned which should significantly
alter the search and retrieval process as it exists today, and theoretical develop­
ments are discussed that may provide new insights into the information searchand retrieval functions.
One of the characteristics of the existing operational retrieval systems is
the large investment in manpower and resources necessary to provide even rel­
atively simple retrieval services. The design and implementation of retrieval
programs constitute major tasks in themselves; in addition, substantial re­sources must be devoted to the generation or acquisition of the data bases to be
manipulated and searched. In these circumstances, even minor adjustments inprocedures require careful consideration; more far-reaching innovations areoften out of the question because of the large resources that are required. This
may explain the tendency among many observers to think of the ideal retrieval
facility as a simple extension of the currently existing systems and procedures.
Thus a good deal of attention is devoted to the implementation of sophisticateduser-system interfaces permitting users and search intermediaries to carry out
the retrieval operations without some of the limitations that hamper the existing
search efforts. From time to time new search protocols are proposed for con­ducting iterative searches in such a way that earlier search results are utilized
to formulate improved query statements usable in subsequent search opera­
tions. The relevance feedback process described earlier is an example of such asystem. Efforts are also made to extend the retrieval operations to several dif­ferent data bases while merging the respective search output.
The future directions in information retrieval may be considered by re­viewing theories that are currently under active consideration and in studying
technologies that are likely to be prevalent in the foreseeable future. The
theories of most interest deal with natural language processing systems using
extended representations of information content. The new technologies include
special processing “chips,” microprocessors, optical character readers, opticalmemories, and micrographic devices. One may expect that new theoretical dervelopments could in time be coupled to the new technologies, leading to theimplementation of flexible, new user support systems capable of controlling
many different file processing activities. Sooner or later, the conventional in­formation processing systems may be replaced by “paperless” systems inwhich machine-readable entities are processed instead of hard-copy products,
and all information flow operations are carried out electronically.

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410

It is premature to submit a definitive design of the information system of
the future. However, it is not too early to study the developments that may be
expected to form the basis for the design of the information handling systems of
the future.
2 TECHNOLOGICAL DEVELOPMENTS
A

Automatic Document Input

The existing information retrieval systems have enjoyed increasing popularity
in the last few years. As more and more items are added to the files, one may
expect that an ever larger proportion of the population will become interested
in using the automatic search and retrieval facilities. Unfortunately, many of
the items that need to be added to the files are not currently available in a form
which allows incorporation into existing data bases. In principle, it is possible
to charge a typist, keypuncher, or data entry clerk with the task of converting
paper documents into machine-readable form. An exceedingly proficient typist
might produce errorless copy at the rate of 100 words per minute. This would
generate 192 double-spaced pages of machine-readable text in an 8-hour work­
ing day, assuming no coffee breaks, lunch hours, errors, or corrections. A morereasonable rate for experienced typists may be 50 typed pages per day. As amechanism for information input into an automated retrieval system, such a
process is inconvenient and labor-intensive, particularly if one considers thatthe data input operation constitutes a second typing operation, following initial
document preparation in many cases.
Fortunately, the data input problem may be on the way to a solution. A
typing operation may still be initially required to create the original documents.
This typed copy is now increasingly produced with equipment capable of cap­turing the information in machine-readable form. Word processing machines
are used in many places to produce edited, machine-readable text that can beconverted into a final printed product. In the foreseeable future, one may ex­pect that increasing quantities of text will become available in a format accept­
able for input to automated information retrieval systems [1-3].
The word processing concept was initially introduced as a means of sim­
plifying certain secretarial tasks— notably the typing of multiple copies ofletters. A standard typewriter would be used supplemented by a paper or mag­netic tape storage unit. The information being typed by the human operator
could then be punched out or recorded automatically on the auxiliary storage
equipment. When additional copies of previously typed materials were needed,
the recorded tape could be used as input without additional keyboarding.
At the present time, far more sophisticated word processing stations areused, including the following components:
1 A keyboarding unit
2 A storage unit, often consisting of a floppy (soft) disk capable of stor­ing, inexpensively, between 50 and 1,000 pages of text

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411

3 A video display unit capable of displaying any page of stored or typed
text
4 A print unit that can print out a final ouput product5 A connection to a computer or to communications lines capable of join­
ing several word processing stations.
The enhanced word processing equipment can now be used not only forthe basic typing and further reproduction of text but also for changing, revising,and editing text. The video display unit is particularly helpful in this respect
because appropriate editing commands can be supplied from the keyboard.
Final copies of text can also be obtained, in a format ready for printing. Fur­thermore, the materials generated by word processing can be disseminated to
the recipients electronically using the available communications lines. Word
processors could then function as originating and receiving stations in an elec­
tronic mail system. Since a large proportion of the correspondence in a busi­
ness organization is internal to the company itself, it is not hard to see that elec­
tronic word processing systems can in principle take over the vast majority of
the routine communications in an office. The effect of word processing on the
publishing and information retrieval world is equally far-reaching: the multiple
typing operations normally needed to produce final versions of books and docu­
ments may soon be a thing of the past; instead the word processor output canbe used to drive automatic typsetting equipment, and the material stored inword processors can be taken over directly by an information storage and re­trieval system.
Word processing machines do not of course help in converting materials
that may already be available in standard, printed form, nor do they solve theinput problem for users who may not have access to machine-readable inputthat may already exist elsewhere. In that case it may be useful to consider char­acter reading equipment using optical reading methods to convert printed in­formation directly to machine-readable form. Optical character readers are
now available that convert text printed in a variety of type fonts to machinereadable form at a rate of about 80,000 characters per hour, producing over 500
double-spaced pages in a standard 8-hour day [4].
Character readers do not require coffee breaks; however, the existing de­vices are error-prone, the quality of the output being dependent on the charac­
teristics of the printed input. For example, the input character “t” represented
in Fig. 10-1 may be interpreted as a “ c ” by a standard optical character reader
when the upper portion of the character is misformed or exhibits less contrast
than the lower portion of the character. The most sophisticated available char­acter reading equipment is now able to recognize correctly about 90 percent of
the characters, and about 70 percent of the full words, contained in arbitrary
printed input texts. When a machine-aided, human posteditor is added to“clean up” the output of the character recognition system, close to 100 percent
correct output may be produced.
The postediting process is typically carried out as a two-step process:CHAPTER 10

412

Typical input to character
reading equipment.
Figure 10-1

1 An automatic process is used to highlight each character falling below agiven threshold of recognition acceptability.
2 A manual character replacement phase then allows a human operator
to correct the output of the optical recognition system.
The automatic highlighting process determines a level of certainty for eachcharacter as a function of the degree of agreement between features recognizedin the input and the patterns stored by the recognition system. Thus, for thecharacter “M” represented in Fig. 10-2 the recognition characteristics may in­clude certain identified vertices as well as line segments (vectors) with appro­
priate directions.
Given an input character such as the one used as an example in Fig. 10-3,
it may be seen that only the vector information matches. Such a character may
then be recognized as an “ M” with a certainty level of *h, or 0.571. Assumingthat the threshold for acceptable characters is 0.75, the corresponding input
would be submitted to a human operator for appropriate action during the post­
editing phase. Obviously, the postediting task increases the cost and decreases
the efficiency of the character recognition process.
The recognition process described earlier may furnish a serviceable solu­
tion to the text input problem. Alternatively, a training system could be used
where the characteristics of some sample input are used by the system to set
appropriate recognition procedures capable of handling new text whose fea­tures are similar to that of the training sample. Unfortunately, some aspects ofthe text input problem are still not treated satisfactorily. A few text characters
may be left unrecognized even when a human editor is available, because the

Figure 10-2

Recognition characteristics for letter “ M.”

FUTURE DIRECTIONS IN INFORMATION RETRIEVAL

Figure 10-3

413

Character recognized as “ M” with certainty level of 0.571.

recognition threshold may be erroneously met for misinterpreted characters.
Thus, the input of Fig. 10-1 may be recognized as a “ c ” without ever being
shown to the editor. Second, special portions of the text, such as author names,
document titles, and descriptive indexing information, may require separate
treatment from the remainder of the text, and the character recognition equip­
ment may not necessarily be adapted to the identification of these special text
elements. Finally, the equipment does not treat pictorial and graphical portionsof documents that may also need to be processed. Special graphics equipment
is needed to handle illustrations and pictures.
An automatic character recognition system might also be supplemented byan automatic voice input system. With such a system, information could be dic­tated, or read, directly to an automated typewriter or display input equipment,
thereby eliminating the distinction between readability and machine readabil­ity. Some voice input systems already exist, for example, children’s games are
sold which properly recognize spoken input words such as “ stop,” “right,”“left,” or “go,” provided the words are properly uttered in isolation. Certain
computer systems in fact accept up to 1,000 individually specified words.
Unfortunately, a substantial distinction must be made between the recog­
nition of distinct spoken words in a given language uttered by speakers towhich the equipment has been properly adapted, and the recognition of normal
running speech in arbitrary dialects by unknown speakers [5]. A solution to thelatter problem appears to be as difficult as the natural language analysis prob­
lems previously discussed in Chapter 7. Whether the remaining character rec­ognition problems can be handled effectively in the foreseeable future to solve
the information input problem without any keyboarding remains to be seen.
B

Optical Storage

In addition to the input problem, the storage problem is a primary concern in
information retrieval. In situations where only limited data classes are pro­
cessed— for example, in systems where the retrieval activity is based on docu­
ment citations and keywords only— the conventional magnetic disk technology
normally proves satisfactory. Magnetic devices are erasable and lend them­selves easily to most file updating and maintenance requirements. Furthermore
auxiliary index files can then be constructed and maintained to guarantee rea­
sonably rapid access to individual information items.
In many cases, it may, however, be desirable to access or display for theuser’s attention copies of the full contents of articles. In that case it becomes

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necessary to handle different type sizes, pictorial information, signatures andgraphical data. A photographic or other optical storage medium may be neededthat is capable of storing digital as well as video information. Videodiscs, holo­grams, and micrographic storage devices are of main concern in this connec­
tion.
Videodiscs A videodisc is a picture storage device that can be connectedto the home television set. If it is used in the home, the videodisc can provide acolor TV movie for the $15 purchase price of the disk. The player for the video­
disc was priced at approximately $500 in 1981, but this price may decrease intime. The importance of the videodisc for information retrieval is not necessar­
ily to keep the viewer entertained but rather to store large quantities of informa­tion at little cost [6,7].
Consider first the manner in which information is stored on a videodisc.
Figure 10-4 shows graphically the method of “burning” information represen­tations onto the disk. As information is received as input, it is translated
through a series of electronic devices to control parameters which guide a laser
beam. The beam in turn creates pits in the surface of a rotating master disk.
These pits may then be “read” to reproduce the information.
Videodiscs are not currently capable of rewriting. That is, once the infor­
mation is put onto the master disk, it cannot be erased and rewritten. However,
duplicates of the master disk may be created. This process is similar to creating\ /Recordingsurface\ /\lMasterdisk

Figure 10-4 Videodisc recording.

FUTURE DIRECTIONS IN INFORMATION RETRIEVAL

415

Servo control
PhotodiodeA

Tracking
m irror j

\ V 0
I \
I

I

\

L

------- j

Playback
electronics
and servos

O utput signal

Playback
lens

\ /
V
1

Figure 10-5

1
1 Plastic replicated disk

Videodisc playback.

duplicate copies of phonograph records. One rotation of the videodisc (onetrack) produces one visual image (one television picture). There are 54,000 con­centric tracks on most current videodiscs. In the more sophisticated videodisc
readers, these tracks may be accessed in any order. Figure 10-5 represents thesystem used to recover the information from the videodisc. Note that the play­back system uses reflected laser light to encode the information.
Initial work with videodiscs indicates that 10 billion bits or approximately
1 billion characters of information may be stored on a single disk. Reproduc­tions of a videodisc in quantities of about 1,000 will cost less than $20 per disk.
This includes the cost of the disk and of the reproduction process but not of theinformation contained on the disk. Thus, given a data base of information items
each averaging 1,000 characters, 1 million of these records can fit on a video­
disc. In addition, work is currently underway to increase the capacity of a sin­gle videodisc to 10 billion characters.
An unknown factor is the error rate associated with the stored information.
For the home entertainment application that is not important; however, for the
storage of digital information it may be critical. That is, if one watches a TV
picture and a single blue dot suddenly turns red for V30 second, there is little
concern. However, if that mistaken blue dot is the difference between CAN­CEROUS and NOT CANCEROUS in a medical data base, then it could
amount to catastrophic error. Mechanisms for assuring the integrity of digitally
encoded information on videodiscs remain to be worked out as of this writing.
When this problem is resolved, the videodisc technology may actually produce
significant changes in the processing of large data bases with periodic updates.
Using videodiscs may, for instance, reduce the need for large communication
networks to remote data bases. Instead, institutions such as the National Li­
brary of Medicine may simply mail periodic updates of their data bases to geqgraphically dispersed sites. These sites will own inexpensive videodisc readers

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C H A P T E R

10attached to inexpensive computers for immediate and reliable access to the in­formation.
Additional applications may also become economically attractive. For ex­
ample, if it were feasible to store digital information on the videodiscs in addi­
tion to pictorial data, the combination of the two might be applicable to thestorage of museum information, photography retrieval, etc. In addition, one
may also opt to intersperse audio information among the recorded video anddigital information. Time and thought will be required to ensure the success ofthe videodisc technology in the information retrieval environment.
Holographic Devices Another approach for information storage consists
in using holographic devices. A hologram is a picture showing a three-dimen­
sional object. The hologram is created by illuminating an object with lasergenerated light from two angles. As the light reflected from the object hits one of
the laser beams (the reference beam) an interference pattern is created. Thispattern is recorded on film as the hologram. The recording operation is repre­
sented in Fig. 10-6. When a laser beam is passed through the hologram, it isdeflected so that a viewer perceives the original object as shown in Fig. 10-7.
An interesting feature of holographic recording is that the holographic
image is literally dispersed across the entire hologram. That is, if a hologram is
cut in half and illuminated, the entire object may still be perceived. The resolu­
tion of the object is reduced, however.
If the object photographed by the holographic device is replaced by a digi­
tal pattern representing characters or other digitally encoded data, then the dig­ital data will also be redundantly stored in the hologram. Reproduction of thedata requires that the display of the hologram be focused on a photodetector
device to convert the picture back to digital signals.
Holographic storage of information is “ safe.” Because of the natural re-

Figure 10-6

Holographic recording.

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417

Object as
seen fromdifferent
perspectives

Figure 10-7

Holographic viewing.

dundancy, a substantial portion of the hologram may be destroyed without ac­
tually losing the information. The hologram is also capable of huge quantities ofstorage. It has been stated that a single 4 by 6 inch piece of film can hold up to
20,000 individual holograms or 20 million characters of information. A device
for reading this information may cost as little as $5,000 [8].
The holographic storage devices, like the videodiscs, are not capable ofrewriting. However, reproduction costs of $1 or even less for a 4 by 6 inch film
(fiche) is possible, and as for videodiscs, holograms may be interspersed withvideo images, making new applications a real possibility.
Micrographic Storage Micrographic storage devices, such as microfilm
or microfiche, provide another solution to the storage problem of bulk materials
in retrieval. Micrographic devices have been available for many years and have
proved to be cost effective in many applications [9], Micrographic storage, like
video equipment, is used to store vast quantities of information, the storage ef­
ficiency being achieved by using a substantial size reduction (typically by a fac­
tor of 24) for the recorded information. A standard 4 by 6 inch microfiche thus
normally stores 98 pages of digital or pictorial information.
Unfortunately, the microform technology does not easily permit erasing or
modification of the recorded information. Its application in information re­trieval is normally found in situations where the existing file remains largely
unchanged, and new film or fiche is used when additional documents are to beincorporated into the file. An additional disadvantage of this equipment is thesubstantial discomfort felt by many users when confronted by this technology.
The size reduction renders necessary the use of special reading equipment
which magnifies the recorded information before viewing by the user. Most
people would prefer hard-copy document output that can be carried around anddirectly manipulated to the somewhat remote microforms.
For storage and retrieval purposes special fiche storage devices are usedfrom which a particular fiche can be extracted on demand. It is customary to
use the fiche number as the main identification, although in principle, the con­tents of a particular fiche can be specified by using conventional keywords andindex terms. However, since a single fiche may contain many pages of dispar­
ate material in addition to pictures and graphs, the full content of a fiche is not

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easily captured by standard methods. A particular fiche extracted from thestorage device may be automatically sent to a microfiche reader for viewing
purposes. Alternatively a microfilm copy can be made for the user’s personal
attention, or paper copies can be produced of certain documents using a microform-to-hard-copy conversion device.
A typical document processing system using micrographics equipment isshown in Fig. 10-8. Optical character recognition equipment is provided in theillustration for input purposes using the semiautomatic process previously de­scribed in which a human operator resolves input ambiguities using a special
editing terminal. A combined magnetic and optical storage system is then used
for storage and retrieval. Specifically, a standard search is conducted using the
document content descriptions stored on conventional disk equipment. Assum­
ing that the standard search produces the location identifications (fiche num­bers) of the corresponding full document texts, the corresponding fiches canthen be extracted from the fiche storage device for further processing.
In the combined magnetic and optical storage system of Fig. 10-8, the mi­crofilm processing system is completely mechanized. Furthermore, all thegraphics equipment is currently in existence and can be bought off the shelf[10]. However, the physical movement of the microforms from the storage area
to the viewing position requires a mechanical transport device which is cum­bersome and expensive. More generally, the marriage between the magnetic
and graphic storage technologies represented in the system of Fig. 10-8 is some­what forced. Different accessing keys are used in the two systems and the pic­
torial portion of the information stored on the microforms is not directly usedin retrieval. For this reason, systems such as the one represented in Fig. 10-4
will not provide long-range solutions to the existing information retrieval prob­
lem.
Efforts have recently been made to permit a digitization of micrographic
images following a normal optical scan. That is, as each image is scanned, theimages are converted into digital form; the converted image can then be dis­played on a normal cathode-ray tube display screen, or entered into computerstorage. The perfection of these techniques may solve the interface problem
between the micrographic and digital storage equipment and produce a harmo­
nious system involving both technologies [11].
Following a description of certain novel retrieval theories, various ex­
tended information systems are considered whose design is based in part on the
microprocessing, character reading, and graphics equipment previously men­tioned. Such extended systems may then provide a solution to the existing in­formation problems.
3

INFORMATION THEORIES AND MODELSANatural Language Processing

In attempting to predict the design of future information systems, it appears
best not to concentrate on the existing retrieval practice but to identify featuresthat are generally considered desirable but are currently beyond the state of the420

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art. Without a doubt, the most immediately important problem of this kind is
the understanding and processing of the written and spoken natural language[12]. If user queries could be submitted using a conventional, natural language
formulation, and texts could be automatically analyzed, abstracted, translated
where necessary, and properly classified according to the document content,
then the main difficulties which currently hamper the information system user
would automatically disappear. No longer would it be necessary to worry about
the design of controlled indexing languages; the need for well-trained, consist­ent indexers no longer arises; and the burden currently placed on users andsearch intermediaries in the formulation of useful queries is lifted.
Unfortunately, the discussion in Chapter 7 on natural language processing
indicates that the free manipulation of unrestricted natural language data is nota likely prospect for the foreseeable future. In particular, no agreement exists
about the best way for formalizing document content, about the world knowl­edge (above and beyond the specialized knowledge in a given subject area) that
may be needed to understand texts and interpret natural language statements,
and about reasoning strategies, inferences, and deductions that may be neededin order actually to respond to user inquiries [13-14].
The approach in information science has been to use specialized tech­niques in an attempt to provide at least some linguistic input to the standard
document analysis and retrieval process [15-16]. Methods have notably beendeveloped for recognizing and assigning noun phrases as document and query
identifiers, and syntactic techniques have been used for dealing with individual
document sentences instead of complete documents [17-20].
The primary difficulty in natural language processing is due to the flexibil­
ity and by extension the ambiguity of most languages. Many ways exist for ex­pressing the same thought; hence it is easy in principle to generate documents
and to formulate information requests. At the same time, a given utterance may
receive many different interpretations depending on the context in which it ap­
pears; hence it is difficult to generate definite or unique interpretations of natu­
ral language materials. The approach mentioned earlier in the discussion on
character recognition can be followed by considering the use of semiautomatic
language analysis methods. That is, the machine might automatically identify
potential ambiguities, and a human operator could then attempt to resolve them[ 21- 22].

Various other techniques have been used in information retrieval in at­
tempting to cope with the language ambiguity problem. In the vector pro­cessing model, special weights are attached to the terms that identify docu­
ments and search requests, reflecting the degree of importance of each term or
the degree of certainty with which the term describes the document content.
Global similarity computations between weighted term vectors are then used toindicate the relationship between pairs of documents or between queries anddocuments [23-24]. Thus, the degree of affinity between two items can varydepending on the certainty or interpretation of the respective content identi­
fiers.

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Somewhat similar characteristics are exhibited by the fuzzy set and proba­bilistic approaches to retrieval that are briefly introduced in the next few para­graphs.
**B

Fuzzy Set Theory

The basic idea in fuzzy set theory is that elements or entities can be assigned tosets to varying degrees. That is, instead of either including an element in agiven set or excluding it from the set, a membership function is used to express
the degree to which the element is a member of the set. This concept is of inter­
est in language processing, and by extension in information retrieval, because
the assignment of individual words to meaning categories is a fuzzy process; so
is the assignment of documents to concept categories, and hence also the re­trieval of documents in answer to certain queries [25].
In language processing, various attempts have been made to use the fuzzy
set approach to model linguistic vagueness, ambiguity, and ambivalence. For
example, given a set of well-defined meaning categories, the total meaning of agiven word might be expressed as a weighted combination of the membershipfunctions of that word in various meaning classes. Linguistic quantifiers(“most,” “ some,” “ a few”) and hedges (“ sort of”) might also be described byusing fuzzy measurements of some kind [26-28].
In information retrieval, the fuzzy set approach can be used to classify the
documents into fuzzy affinity classes and also to control the actual retrieval
process. Consider first a document DOC and a particular term A. If A denotes
the “concept class” of all items dealing with the subject denoted by A, then themembership function of document DOC in set A may be denoted as fA(DOC).
In the usual terminology fA(DOC) represents the weight of term A in document
DOC. Given a number of concept classes A, B, . . . ,Z representing various
subject areas, it is now possible to identify each document by giving its mem­bership function with respect to each of the concept classes, that is,
D = (fA(DOC), f„(DOC), . . . ,fz(DOC))(1)

Expression (1) thus takes the place of the normal term vector used in the vector
processing model of retrieval.
Given document representations of the kind shown in expression (1), all
the usual vector processing operations are now expressible as fuzzy set opera­
tions. In particular, the distance (or similarity) between two documents or be­
tween a document and a query may be obtained as a function of the differencesin the membership functions of the two items in corresponding concept classes.
Specifically, given T different concept classes, the fuzzy distance between doc­
uments DOC' and DOC" might be computed asd(DOC',DOC") = ^ (fx(DOC') - fx(DOC"))
xeT

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or alternativelyd(DOC',DOC")(fx(DOC') - fx(DOC"))2

Ranked retrieval is achieved by retrieving the documents in order of increasing
fuzzy distance from the query.
One attractive feature of the fuzzy set approach is the possibility of ex­
tending the definition of the membership function from single terms to combi­
nations of terms. Thus, given the membership functions of document DOC withrespect to terms A and B, the following rules apply for Boolean combinationsof terms [25]:f<A and bj(D O C ) = min(fA(DOC), fB(DOC))f(A or b)(D O C ) = max(fA(DOC), fB(DOC))
f(NOT A)(DOC) = 1 - fA(DOC)(3)

i

It is easy to verify that the fuzzy set rules of expression (3) satisfy the normal
rules of Boolean algebra when fA(DOC) and fB(DOC) are restricted to thevalues 0 and 1. The fuzzy set retrieval model thus represents an extension of
the normal Boolean retrieval system to the case where the assignment ofweighted identifiers is possible for the documents but not for the queries.
Since the vector space model effectively supplies term weights for both the
documents and the queries, the fuzzy set model is in a sense intermediate be­
tween a conventional Boolean query system where no term weights are allowed
and a vector processing system. The attractions of the fuzzy set model are its
compatibility with the standard Boolean query processing system and the inter­
pretation of the fuzzy weights as linguistic indicators of term ambiguity or am­bivalence. A number of attempts have been made to use fuzzy set models in
retrieval; however, the resulting systems have never been evaluated, and the
linguistic relationship has not so far been exploited [29-34].
**C Term D ependency Models

The flexibility and ambiguity characteristics of the natural language are re­flected in the fuzzy set retrieval model where terms and other content identi­
fiers apply to some degree in particular cases. A different approach consists inusing as a measure of term importance values expressing the likelihood that agiven term occurs in a particular environment, or the likelihood that the term
should be assigned to a given document. Such probability measures can beused as term weights, and the documents can then be retrieved in ranked orderaccording to the probability of relevance to the given queries.
Given a particular document identified by a binary term vector x = (Xj,
x2, . . . , xt) the optimal retrieval rule in the probabilistic model consists infirst evaluating P(jc), the probability of occurrence of* in a relevant document,

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423

and Q(x), the probability of occurrence of x in a nonrelevant document, andthen in retrieving the document represented by x whenever P(x) > Q(x)[35-36]. The actual values of the probabilities P(x) and Q(x) depend on the oc­
currence probabilities of the individual terms Xj, x2, . . . , xt in the relevant
and nonrelevant documents of a collection, respectively.
It was mentioned earlier that under the assumption that the terms are as­signed independently to the documents of a collection, the probabilistic re­trieval model produces an optimal term weighting function TERMREL, known
as the “term relevance,” representing the logarithm of the proportion of rele­vant documents in which a term occurs, divided by the proportion of nonrele­vant items in which the term occurs [37-38]. The normal linguistic intuition
makes it clear, however, that words or concepts do not occur in the language
independently of each other. Hence one may expect that retrieval models thatare based on the term independence assumption will not reflect reality very ac­
curately. A more realistic treatment would take into account various similar­
ities and dependencies between the terms.
In the fuzzy set model of retrieval, the notion of term dependence isdirectly built in, in that many terms may be assigned to common concept
classes and somewhat different membership functions may be used to reflect
differences between them. In probabilistic retrieval the same effect is obtain­
able by computing term dependence probabilities as a function of the probabil­
ity of co-occurrence of several terms in the documents of a collection, com­pared with the occurrence probabilities of the individual terms alone.
In fact, exact formulations for the occurrence probabilities P(x) and Q(x)of a given document vector x in the relevant and nonrelevant documents of acollection are provided by the Bahadur-Lazarsfeld expansion (BLE). This ex­
pansion takes into account the occurrence probabilities of the individual terms
Xj, as well as all dependencies between term pairs, triplets, and all higher-orderdependencies [39,40], The correct retrieval strategy then consists in using theBLE expansion to compute the probabilities P(x) and Q(x), and in retrieving a
document represented by x whenever the expression log[P(x)/Q(x)] is suffi­
ciently large. Unfortunately this method is not immediately usable in practicebecause an exponential number of pairwise and other higher order dependen­cies exist between the terms, and because no obvious way is provided for esti­
mating all the required probabilities.
Various techniques suggest themselves for constructing approximations to
the exact process. One possibility consists in using a tree dependence model
where each term is allowed to be dependent on only One other term [37,41,42].
Another approach consists in using the BLE expression in a suitably truncatedform. A possible measure of pairwise term dependence is given for two sample
terms x, and Xj as
I(Xi,Xj) =2 P(Xjx,=0,l
Xj=0,l, X j ) log

P(*i,Xj)
PCxOPlx,)

(4 )

424

CHAPTER 10

where P(Xj ,Xj) is the joint probability of occurrence of both terms X, and Xj inthe documents of a collection, P(Xi) and P(Xj) are the individual occurrence
probabilities of the two terms, and the summation is taken over the four combi­
nations of values obtained by assuming values of 0 or 1 for both x{ and Xj [39].
Since the probability of occurrence of a given document vector x [that is, P(jc)and Q(x)] must be computed separately for the relevant and nonrelevant docu­
ments of a collection, the probability values in the term dependence expression(4) must also be obtained separately for the relevant and the nonrelevant docu­
ments of a collection. That is, expression (4) may be assumed to be valid for the
relevant documents, and a similar expression with Qs replacing all the Ps would
apply to the nonrelevant items,
A realistic method for estimating these probabilities consists in Using therelevance feedback strategy introduced in Chapter 6. In relevance feedback,tentative search requests are first submitted to a retrieval system; the relevanceor nonrelevance of initially retrieved items is then used to generate improved
query formulations that are more similar to the documents identified earlier as
relevant than the original queries, and less similar to the documents identified
as nonrelevant. The reformulated queries are then processed in a second search
operation in the expectation that additional useful items may be retrieved. The
query reformulation process may be repeated until the user is satisfied with theretrieval results [43-45]. If the relevance feedback process is used for a given
document collection over some period of time, relevance information should
eventually be obtainable for a substantial number of documents. This implies
that the occurrence characteristics of a large number of terms in the relevant
and nonrelevant retrieved documents should become known.
It remains then to estimate the occurrence probabilities of the terms in therelevant and the nonrelevant documents of a collection by looking at the corre­
sponding occurrence probabilities in the relevant retrieved and in the nonrele­vant retrieved items, respectively. If P(Xj) and Q(Xi) denotes the occurrence
probability of term x, in the relevant and nonrelevant items of a collection, theestimated values could be obtained as

P(Xi) estimatednumber of relevant and retrieved______ items containing term x,______
number of relevant and retrieved items(5 )number of nonrelevant and retrieved
items containing term x(
Q(Xj) estimated =number of nonrelevant and retrieved items
The probability estimation methods of expression (5) have been used ex­
perimentally with good success [41,45]. However, conclusive evidence about
the usefulness of including term dependencies in retrieval is still lacking. In par­ticular, practical methods remain to be worked out for choosing a small numberof the most important term dependencies, and for actually generating the corre­
sponding term dependence factors used in the probability formulas.

FUTURE DIRECTIONS IN INFORMATION RETRIEVAL

*D

425

Composite Document Representations

The fuzzy set and probabilistic retrieval models constitute enhancements of theconventional retrieval environment in the sense that term ambiguities and term
dependencies are taken into account. Another, even more fundamental ques­tion, concerns the proper choice of the initial, basic concepts to be used for the
representation of document content. In principle, the vocabulary included inquery and document formulations must be used as a basis for the choice of con­tent identifiers. However, it is not clear a priori how many basic concepts ought
to be used, and how these concepts are to be defined.
One possible approach consists in taking an indexed document collectionin which term vectors are used to represent the various documents, and in per­forming a factor analysis to derive the few independent concepts from thelarger class of initially available terms. Unfortunately, a factor analysis process
is expensive to carry out, and becomes impractical when more than a few hun­dred documents and queries are involved.
A more efficient method for generating a set of independent basic concepts
may be obtained by first choosing a set of core documents from a sample docu­
ment collection, the core being selected in such a way that core items have nocommon terms. The assumption is then made that the full concept space corre­
sponds to the space initially defined by the core documents. Specifically, thedocuments outside the core are processed one at a time, and an appropriate
place is found in the initial core concept space for each term included in these
documents. That is, each of the new terms is defined as a combination of theoriginally chosen concepts [46,47], Various methods can be used for defining
the mapping of the new terms into the original concept space. So far, no conclu­
sive information is available about the effectiveness of this type of process.
In addition to document content represented by the standard content
terms, or keywords, documents may also be characterized by scope, or extent;
survey articles tend to have greater scope than research notes dealing with spe­cific technical subjects. Documents also carry various degrees of influence;
many articles are effectively “lost” in the sense that they are unknown and arenever referred to by other items in the literature; other documents become fa­
mous and serve as starting points for additional developments. Individual docu­
ments may also be characterized by citing other items in the literature that may
exhibit similar or related information content. For example, a document deal­
ing with a legal problem may be explicated by citing related cases covering legal
precedents; similarly, a religious article may be better understood by addingreferences to authorized interpretations of the text.
This suggests that the normal vector representations of document content
in which a term represents a particular content identifier be extended to coverthese other factors. In particular, a document might be identified by using three
types of identifiers:
1
categories

Normal content terms including single words, phrases, and thesaurus

CHAPTER 10

426

2 Factual or objective identifiers covering proper names of authors, pub­lishers, dates, language of publication, and so on
3 Interpretational identifiers representing related documents designating
scope, influence, and other interpretations
It has been suggested that the interpretational aspect of document content be
represented by citations to other related bibliographic items [48,49].
A particular document might thus be represented as
D — (di > • • • >dk, o^^j, . . . , on,cn+j, . . .

, ct)

(6)

where df represents the weight of the fth content term, om represents the mthobjective identifier, and cp represents the pth citation to a related document.
Instead of using simple bibliographic citations to related documents forcontent representation, the cocitation strength could be used, defined as thedegree to which two particular documents are cited in common [50].
The extended vector representations can be utilized in retrieval experi­ments by introducing composite matching coefficients to measure the similarity
between a document and a query as a combination of the similarities for thevarious classes of identifiers [51]. The similarity SIM(D,Q) between a docu­
ment D and query Q might then be computed as
SIM(D,Q) = a[content identifier similarity]+ /3[objective term similarity]+ y [citation similarity](7)

for suitable weighting factors a, /3, and y. Much work is still required in the
future for the generation and evaluation of useful extended content representa­
tions and composite query-document comparison methods.

4

ADVANCED INFORMATION SYSTEMS

A

Mixed Information Retrieval Systems

Bibliographic retrieval is concerned with the processing of books and docu­
ments, available generally in natural language form. In many situations, eventhe most advanced bibliographic retrieval system will not satisfy the users’
needs. Consider, for example, the problem of determining whether a particularchemical substance is toxic, that is, proves harmful to human beings who comein contact with it. In that case, textual data must be coordinated with numeric
information produced by tests conducted with the substance in question. The
user will thus require various resources in order adequately to resolve the tox­
icity question, including bibliographic information related to the chemicals,
health effects, production processes, chemical uses, and so on. In addition ex­
perimental output and test information are needed about the production andFUTURE DIRECTIONS IN INFORMATION RETRIEVAL

427

use of chemicals, and about the known hazards of related substances as theyaffect populations in certain geographic areas.
The factual information relating to chemical characteristics and hazards
differs from normal textual data in the sense that the values of specific attrib­
utes are important rather than the attributes themselves; furthermore, thesevalues require analysis instead of simple retrieval. Data base management sys­
tems are normally designed to offer storage and retrieval capabilities for stored
numeric information, as well as data analysis capabilities to perform statisticalcomputations and to produce output summaries.
The chemical toxicity problem may then be attacked by supplying a vari­
ety of resources including in particular those contained in data management aswell as bibliographic retrieval systems. More generally, an increasing needexists in many situations for sophisticated information systems capable auto­
matically of coordinating related information from many sources. For example,
an article dealing with the development of a chemical information system by aspecific chemical company might be supplemented by information about the
governmental regulations affecting data required from chemical companies,
and by articles dealing with the availability of other chemical information sys­tems, or articles written by the same author or citing the original article.
A system capable of pulling together many different but related resources
would obviously expand the user’s knowledge of a given problem by supplying
a particular piece of information, as well as the context for this information.
The ultimate information system is thus often conceived as a network of differ­ent information facilities including bibliographic retrieval, data base manage­
ment, data analysis, citation indexing, and text processing systems. In order toutilize such a network effectively, the user ought to understand the capabilitiesof each resource and the interactions between them. System aids could be pro­vided in the form of catalogs of available facilities, sample search protocols,
lists of search commands and their effects, tutorial sequences, and so on.
Of particular interest in this connection are common interface systems
which would permit the user to formulate in a common language information
requests which are to be submitted to many different information systems. It isthen up to the interface system to convert the original user statements into thedifferent internal languages required by the various resources [52]. Common
interface systems are helpful in that they render it unnecessary for users to con­cern themselves with the internal details of many different information sys­tems. Of even greater interest might be systems that could themselves decide,given a particular query, which of many resources should be called upon to fur­nish the answer. In this mode, the system “knows” the capabilities of different
resources and decides how best to use the available facilities to answer a given
search request.
The design of such intelligent information systems is still somewhat be­yond the state of the art. However, considerable advances are being made in
developing special-purpose systems that facilitate access to many different re­sources and relieve the user from the operational details that must normally bemastered before file access can be obtained [53-55].

CHAPTER 10

428

B

Personal Computing and Paperless Information Systems

A special class of information system that has been much in the news in recent
times is designed to mechanize the paper handling routines normally taking
place in office environments. An extension of these office automation systems
are the so-called paperless information systems from which the hard-copy
paper products are eliminated [56,57].
Until recently the supply of paper was plentiful and the printing operation
was inexpensive. As a result the production of large journals and the bulk mail­ing of heterogeneous materials to large classes of recipients appeared justified.
Printing and paper costs have been increasing steadily in recent years. At thepresent time, it appears much more important to target the disseminated infor­
mation precisely to recipients who are actually likely to be interested in receiv­
ing each item. This results in the production of smaller, more specialized jour­nals, and eventually in the replacement of journals by electronic distribution
systems where individual items are distributed only to specially intended re­ceivers.
The following main points are often cited in support of the paperless infor­
mation systems of the future:
1 The volume of material to be processed and stored is becoming toolarge to be handled by a full-text, hard-copy paper system.
2 The materials of interest to any one person are becoming increasingly
fragmented over many files, books, and journals, and the time available forfinding and selecting the interesting portions is limited.
3 The cost of paper publication continues to increase at precipitous rates,in part because the publishing industry is labor-intensive and has not so far fully
benefited from automation.
4 The conventional publication system continues to experience increas­
ing delays in publishing research materials in part because the work force thatoriginates the materials increases while at the same time the scope of many
journals is constricted by rising costs and decreased subscription income.
5 An increasing proportion of the information materials is initially avail­able in machine-readable form produced as a by-product of word processing or
automated typesetting systems.
A situation is then postulated in which users, including office workers, sci­
entists, technical personnel, and so on, have access to personal on-line console
terminals. These terminals would be used to receive text, compose letters anddocuments, search for stored information, seek answers to factual questions,build information files, converse with colleagues by sending and receiving mes­
sages, receive mail, and generally conduct many kinds of information pro­cessing activities. Many different files could be maintained in such a system,
including personal files accessible to the owner alone, mail files for incoming
and outgoing messages, central files belonging to a given organization, and ex­
ternal files such as those maintained by outside information banks and libraries.
A summary of various files and file operations of interest in such an environ­ment is contained in Table 10-1.

429

FUTURE DIRECTIONS IN INFORMATION RETRIEVAL

Table 10-1

File Operations and Files for Typical Office Automation System

Typical operations

Typical files involved

Information searchInformation retrieval
Storage of new information
Refiling of old information
File maintenance
Data computations
Text and letter composition
Text processing including correction,
editing, hyphenation, justification, andso onCommunications, that is, sending and re­ceiving of messages

Text files in digital form
Text files in microform medium
Personal files including personal data,
addresses, comments, personal biblio­
graphies
Mail files including data and status of in­coming and outgoing messages
Central business files including informa­tion and status of stored correspon­
dence
External files such as generally accessible
library files
Master index files giving access to otherfiles

In an automated file processing system a particular information item may
be contained in several different files, for example, in certain publicly acces­
sible files as well as in a variety of private files. Users may be allowed to use
their own access points (content identifiers, keywords, etc.) to find a particular
item, and different access conditions may obtain for the different users. Never­theless each item need be stored only once assuming that index files are avail­able to translate the different types of content descriptions into pointers refer­ring to a given common storage location.
The obvious advantages of an automated, personal file processing system
include the possibility rapidly to access a large variety of information products,
including possibly the full text for many items, the convenience of being able tomaintain and search private and public files at various levels of specificity andcomplexity, and the saving in space and paper handling.
The trend toward personal computing is accelerated by the recent startlingadvances in VLSI (very large scale integration) technology which makes possi­
ble the construction of powerful small computers coupled to large inexpensive
storage devices (32-bit microprocessors with 128,000 bytes of storage) at costs
not much in excess of $1,000 [58]. (See Table 10-2.) This technology makes it
possible to expand computer services by simply furnishing small machines tolarge classes of potential users. This possibility is especially attractive in viewof the increasing availability of personalized information services variously
known as teletext or viewdata, where prestored information is disseminated on
demand to individual recipients [59], and by advances in communications andnetworking that may in time allow the transmission of large masses of datausing novel technologies such as fiber optics transmission lines [60].
The use of small, individual computers avoids some of the resource alloca­
tion problems which complicate life when a single large computer is used toservice large user classes. Furthermore, users may prefer being “in charge” oftheir own machines, instead of having to contend with the restrictions normally
imposed by computing center rules and regulations.

430
Table 10-2

CHAPTER 10

Microprocessor Configurations
Configuration

Handheld calculator
Digital readout
Input keyboard
Several dozen storage registers
Arithmetic unit
Automatic sequence control
Minimal processor
16-32 bit address
32,000 to 128,000 characters of data
storage
2 to 5 microseconds add time
Floppy disks for storage
Cathode ray tube display
Full microprocessor configuration
Printer added to minimal processor
Hard disk for bulk storage

Typical use

Typical cost

Small programs andcalculations$50-$200

Word processing capability
Text editing
Program editing
Indexing
Cataloging$1,000-$5,000

Information retrieval
Inverted file processing
Output printing$10,000-$15,000

On the other hand, the future paperless systems also raise difficult legal
and social problems that have not so far been adequately considered: for exam­
ple, methods must be worked out for safeguarding the interests of a variety ofparties in the information chain, including authors, publishers, information
product vendors, and so on. At the present time royalty payments are not madewhen a user withdraws an item from a conventional library. An automated sys­
tem with point-to-point communication facilities is, however, equivalent to anunrestricted, universal photocopying system, where anyone can easily obtainall stored materials. This possibility beclouds the future of the publishing indus­
try and of the conventional library systems [61]. A good deal of thought mustalso be given to the role of the existing copyright legislation that protects own­
ership of information, as well as to the financial arrangements that must be
made between the parties involved in the electronic communications systemand to the provisions required for safeguarding the information stored in the
publicly available files.
One may expect that many of the objections to the institution of paperless
information systems will eventually disappear as the automatic systems be­come more sophisticated and more user-friendly. In time, a large proportion of
the manual transactions currently performed with paper systems should be car­ried out automatically with electronic counterparts. And even if some paper
products were to be maintained indefinitely, a more rational and hopefully
more convenient information handling system will surely be instituted to serve
the growing number of information users.
5 CONCLUSION

After reading some of the foregoing material, it will be obvious that a wealth of
information and know-how exists about the theory and practice of information

FUTURE DIRECTIONS IN INFORMATION RETRIEVAL

431

retrieval. Many insights relating to the information retrieval problem are ob­tainable by studying elements of related areas such as decision theory, artificial
intelligence, software engineering, information theory, combinatorial mathe­
matics, linear algebra, computational linguistics, pattern recognition, scene
analysis, and logic. Viable solutions to the information problem will eventually
be found by combining results derived from these various disciplines.
Many challenges lie ahead for researchers and practitioners in information
retrieval. First, the knowledge and technology already available ought to be in­corporated into the existing retrieval system implementations. The brief sum­mary of technological and theoretical developments contained in this chapter
indicates how much can already be done to improve the current information
handling facilities. Second, there is a continued need for more basic work invarious areas where progress has been relatively lacking, such as language pro­cessing, voice recognition, graphics storage and display, and the performance
analysis of existing or projected systems.
There is little doubt that the pressures of organizations and individuals
who demand a more effective utilization of information will continue to in­crease. The importance of timely and useful information necessary to carry out
almost any task continues to grow, and the penalties in time, effort, and money
to be paid when information resources remain unused or poorly used are fast
increasing. A continued need then exists for high quality work in information
retrieval and for the training of individuals knowledgeable in the various as­
pects of information handling. It is hoped that this text will make a modest con­tribution toward that aim.

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BIBLIOGRAPHIC REMARKS:

Two kinds of materials suggest themselves as additional sources for informa­tion on the future of information retrieval. The first consists of reviews outlin­
ing the current state of the art and including also short-term projections for the
future. Such material can be found in certain conference proceedings and insome of the widely circulated journals in the computer field. There exist yearlyproceedings of a Conference on Office Automation sponsored by the American
Federation of Information Processing Societies (AFIPS). The Institute of Elec­trical and Electronics Engineers (IEEE) and the Association for Computing
Machinery (ACM) jointly sponsor a yearly conference on Very Large Data
Bases. Conference proceedings are also available covering many computerFUTURE DIRECTIONS IN INFORMATION RETRIEVAL

435

technologies of interest such as microprocessors and computer graphics. The
journals that regularly include reviews and projections into the future of infor­
mation science include Computerworld, Datamation and the ACM Computing
Surveys.
Another type of article takes a more frankly visionary approach. In thatarea it is however necessary to distinguish materials with a science-fiction ap­
proach from articles exhibiting a solid technical foundation. The following arti­
cles or books are certainly worth reading:
V. Bush, As We May Think, Atlantic Monthly, Vol. 176, No. 1, 1945, pp. 101-108.
J. G. Kemeny, A Library for 2000 A.D., in Management and the Computer of the Fu­ture, M. Greenberger, editor, MIT Press, 1962, pp. 134-178.
F. W. Lancaster, Toward Paperless Information Systems, Academic Press Inc., New
York, 1978.
J. C. R. Licklider, Libraries of the Future, MIT Press, Cambridge, Massachusetts, 1965.
G. Salton, Dynamic Information and Library Processing, Prentice-Hall Inc., Englewood
Cliffs, New Jersey, 1975, Chapter 10.