Article The contribution of library and information science to expert system development

Margaret O'Neill and Anne Morris Loughborough University of Technology

Abstract: As technological progress races on, the Library and Information Science (LIS) profession is continually faced with new challenges and new technologies to aid in the effective dissemination and use of information. This paper looks at the challenge and implications of expert systems technology for LIS. It will discuss in particular the possible contribution of LIS to expert systems development, in the light of a recent survey of 50 expert systems producers in the United Kingdom, conducted by the authors. It concludes that there is room and need for LIS skills in expert systems development teams, but that these skills may need to be augmented by more specific computing experience if LIS graduates are to have a realistic chance of employment in this area.

1. Introduction New tools and techniques for organising, accessing and using information are constantly emerging and the library and infor­mation science (LIS) profession is obliged to keep itself abreast of new developments, and learn how they can be applied in practice. This is not an entirely passive role; as well as applying new technologies, LIS professionals have often been active participants in the creation and development of new information systems. This article will address the impli­cations for LIS of expert systems, the latest in a long line of technologies which have claimed to change the nature and use of information. What distinguishes expert systems from those that have gone before is that, rather than offering users infor­mation in the form of statistics, references or data, they give quality judgements and advice in a specific area of interest, drawing on a database of human knowledge and experience in that area. This knowledge and experience has to be extracted — from human, textual and other available sources — for­malised, organised, represented in paper and machine form, disseminated, retrieved and applied. The formalisation and organisation of knowledge has always been the business and

concern of the LIS profession, and a valuable collection of skills, experience and expertise has been developed over several hundred years to help in these tasks. This raises the question, can these skills be utilised in the development of expert systems?

To investigate this question further, the Department of Library and Information Studies at Loughborough University was awarded a grant from the British Library Research and Development Department. The precise aims of the investi­gation were to identify roles for LIS professionals in the design and development of expert systems, the demand for those roles, and ways in which LIS graduates might be prepared for them [1]. It was also hoped that project staff might raise awareness about LIS skills amongst those involved in expert system development.

An important part of the study was a survey of expert systems producers, which aimed to identify skills needed in expert systems development and possible openings and opportunities for LIS. This paper discusses the results of the expert systems survey in relation to LIS. It also looks at the way in which LIS schools in Britain have been responding to the expert systems challenge, and attempts to realistically assess the potential demand for LIS skills in this new market.

2. Survey of expert systems producers To establish just how LIS could fit into the expert systems field required an assessment of the state-of-the-art of expert systems work in Britain today. A brief familiarisation survey of software houses specialising in expert systems work was therefore undertaken, followed by an extensive in-depth in­terview survey of 50 expert systems producers from academ-ia, industry and commerce. The survey was designed to obtain detailed information on the practical issues and problems as­sociated with expert systems development and participants were chosen so as to be as representative as possible of the variety of disciplines and industries involved in expert sys­tems work. All the major firms working in the area who were willing to provide information were included, as well as partners in Alvey sponsored projects and a cross-section of developers from academia, industry and commerce. The aim of the survey was to identify the number, nature and scope of expert systems in Britain, the methodologies used in their development, and the background, experience, skills and attributes of those working in the area. The intention was to identify skills gaps and potential opening through discussions with key organisations involved in expert systems develop­ment.

2.1. Size of the expert system market in Britain

It is impossible to be precise about the number of expert sys­tems being produced in Britain because so many development projects are in a state of flux, or entirely experimental. The software houses responding to the initial questionnaire claimed to have produced over 600 expert systems between them, although many of the firms were vague about whether these were actually completed products or investigative trials

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of the technology. A number also counted the sales of their tools as actual systems. The fifty expert systems producers spoken to were more realistic and claimed about three hundred systems between them, of which about a quarter were said to be operational. From these discussions, and from references in the expert systems literature, one can certainly deduce that expert systems are being experimented with and developed in all major institutions in Britain — including banks and finance houses, governmental, academic and other research establishments, all the major utilities, manufacturing and production industries, software houses and computing firms, telecommunications, and the health service. Although much work is still of an experimental nature, the evidence suggests that key organisations are now beginning to put ex­pert systems technology into practical applications in their everyday work. The implication is that expert systems tech­nology will eventually permeate through all major institu­tions, and the expert systems developers interviewed during the course of this project believe that all future systems will incorporate some kind of expert systems component. It is clear, therefore, that LIS professionals will come up against expert systems in their work in the future and will, at the very least, need to know how and where they work, how to use them and when it is appropriate to do so.

2.2 Nature of systems

There is a fairly natural divide amongst those interviewed during the survey, between those developing expert systems using AI languages and expensive, dedicated hardware, and those using commercially available and comparatively cheap PC-based shells. The users of the more advanced tools and techniques were usually advanced research departments and large software houses specialising in AI work. They were generally involved in the design and development of total integrated systems, which included expert systems com­ponents as just one part of a larger whole. Almost a third of the organisations visited were engaged upon this type of work, usually operating in highly technical and capital-intensive areas, such as the oil, gas and defence industries, advanced telecommunications and datafusion and signal monitoring. The rest of the systems seen were being produced by small teams of specialists using shells, and addressing narrow and clearly defined areas — commonly fault diagnosis, technical engineering, management and decision support, finance, legislation, insurance etc. Although many of these developers spoke about their intentions to move on to the use of more expensive and sophisticated tools and equipment, there was no real evidence of this happening. Several felt that there was still a great deal of unexplored potential in shells, and that the challenges they offered should be met before moving on to more complex equipment.

Expert systems shell manufacturers have proved very responsive to the demands and criticisms of their clients, and their products are a great improvement on the tools originally available in the early 1980s. Some of the most successful and useful systems seen during the conduct of the survey of expert

systems producers were built using this low-cost equipment. Most of those spoken to also believed that shells were the ideal starting point for those wishing to understand and learn more about expert systems technology. This is good news for LIS, because it shows that useful systems can be built with limited finances and that cheap, convenient and accessible tools are available for experimentation, teaching and research. Because many LIS schools do use shells for teaching purposes, students are becoming very familiar with the vagaries of different packages, and their evaluations could help identify which shells are best for different purposes. Information on the expert systems market is also a viable and productive area of research. For example, a student and member of staff at the Department of Library and Information Studies at Loughborough have recently published a directory of expert systems tools, which should prove useful to those starting out in the area and wanting objective and non­partisan advice on the choice of equipment [2].

2.3 Scope of systems

Although most expert systems are currently addressing very narrow and primarily technical areas, those interviewed dur­ing the survey expected them to develop and expand, and eventually integrate with existing software and systems. Al­though there will always be room for small systems in discrete areas, the real future was said to lie with large, linked, infor­mation-heavy, distributed expert systems, addressing wider issues and a far more heterogeneous population. This expan­sion of horizons is, in fact, already happening, as evidenced by the recent creation of an 'AI for Society' Club which aims to promote more socially useful applications of AI. As expert systems broaden their scope, far more conceptual knowledge will need to be included to lend context to the expert input and to make it understandable and acceptable to less experienced end users. The basic kernel of expertise will therefore have to be presented in its wider disciplinary context, but the experts involved may not have either the time or the inclination to provide this extraneous material. The LIS profession, how­ever, would be in an ideal position to provide this service; they are the gatekeepers and custodians of the world's information store, and know where to go for information, and how to ob­tain it. They are capable of switching quickly to and from various topics and of becoming 'mini experts' on subject areas of which they have little or no previous knowledge. They also have the advantage of understanding information in its social, as well as its technical context; in order to provide people with relevant information it is necessary to know why they want it, how they want it presented and how they will need to use it. In short, LIS professionals are used to re-pack­aging information for different users' needs.

2.4 Methodologies used in expert systems development

The tasks involved in the development of an expert system include feasibility studies, user studies, knowledge acquisi­tion, knowledge representation, system building, testing and evaluation and eventual delivery. It has been generally ac-

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knowledged that the most time-consuming and difficult part of the expert system development cycle is the process of knowledge acquisition and representation. A number of meth­odologies have been developed over the past few years to help the expert systems builder, or 'knowledge engineer', in these tasks [3-5], but in effect these methodologies are very rarely called upon in practice, and much of the expert systems devel­opment seen during the duration of this project was conducted on a very ad-hoc, trial-and-error basis.

Academic developers were more likely to use the lengthy pyschological tests and techniques to elicit information from their experts than those in commerce and industry, who were usually working under strict time constraints and consider­able commercial pressure. In industry, therefore, the most common way of eliciting information was by interview. In all, 77% of those surveyed used some form of structured or unstructured interview to obtain information. These ad-hoc, informal methods of obtaining information usually worked in small projects, where the developers were working in close collaboration with the expert. In these cases, the expertise was often not written down in any formalised or structured way, but transformed directly into rules, or whatever repre­sentation the particular shell being used demanded, and input directly into the system. The larger, collaborative projects, however, suffered considerably from the lack of structure and co-ordination, and tended to produce confused and dis­organised systems as a result. Larger, team-based projects, therefore, certainly had need of a properly structured and co-ordinated system of information recording and exchange. There is also a case to be made for all expert systems devel­opers tidying up their methodologies and implementing formal procedures for noting and archiving knowledge elicitation sessions in a formal way. This would not only allow information to be passed coherently amongst team members in larger teams, but would also mean that the knowledge obtained could be used for other purposes, such as training or job re-specification, or as a basis for another system if the tool or shell chosen was inappropriate, or if the system failed to perform satisfactorily. A good, tool-independent mediating representation would also help to direct discussions with the experts. The establishment of methodologies for organising and representing knowledge, and setting up properly struc­tured communication and co-ordination channels between experts, developers and users could, therefore, be a job for the LIS profession. Many of the larger collaborative projects seen during this survey were in complete disarray and were in desperate need of someone to perform even the most basic information functions — like literature searching, infor­mation assessment, co-ordination, current awareness etc. There is, therefore, a definite need in this area, and it is up to the LIS profession to prove that they have the skills to meet it.

2.5 Expert involvement

In the systems seen during this survey, single experts were usually consulted for the knowledge input, usually in order to avoid the problems of multiple-expert disagreements. In fact,

a quarter of the systems were actually built by the experts themselves. This does avoid contentious issues, but it also defeats the object of an expert system by making it extremely provincial in effect. Experts who have built their own systems may well be blind to its imperfections, whereas to other users they will be glaringly obvious. Systems built on the expertise of just one expert are therefore only really acceptable if they are intended to reflect the biases and prejudices of one person. Most of the interviewees accepted this, and felt that the large, information-heavy distributed systems of the future would have to draw on a number of external sources — human, com­putational, textual and audiovisual. As systems become larger, so must the system development teams, to reflect the interests of the various different disciplines involved in devel­opment. The problems of eliciting and organising knowledge from different sources and co-ordinating the activities of a multi-disciplinary team will therefore have to be given seri­ous consideration. Again, LIS professionals have skills not only in gathering and organising information from multiple sources, but also of assessing its value in different contexts and presenting it in a coherent and accessible form. They also have discriminatory skills — outlined by one LIS teacher as 'knowing what to use and what to lose' — that could prove invaluable in trying to make sense and shape of masses of amorphous information.

2.6 User involvement

Users were less favoured by expert systems developers than experts, and their position in the development cycle seemed to correlate with their position in the client organisation. Whilst expert or near-expert users were closely involved in expert systems development, users lower down in the organisational pecking order often took a back-seat, their contribution fre­quently being reduced to checking over the HCI design as the project neared completion. This lack of involvement is re­sponsible for the eventual ignorance of user needs and re­quirements, and goes some way to explain why so few systems actually end up in live operational use. An expert systems specialist at a recent meeting claimed that good user interfaces have been developed despite, rather than because of, expert systems developers [6], and the experience of this project confirms this view. Only one third of those inter­viewed actually consulted their users at the outset, and the vast majority could not say how often their systems were used, or by whom, because they conducted no real follow-up procedures. LIS has a long history of research into user needs and user modelling, which could usefully be applied to expert systems work. In particular, they have an interest and capa­bility in the field of human factors in systems design — be­cause most of the systems being used in LIS services are being used by a non computer-literate public, whose needs are wide and disparate. All the signs are that this kind of essential ex­perience is often lacking in expert systems work.

2.7 Updating and extending expert systems

Because most of those working in the expert systems field

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have only been involved in the area for five years or so, most developers seen had not yet had to address the issues of updat­ing and extending systems. However, few seemed to have made any contingency plans for ensuring that the systems they had installed remained current. Almost half of those in­terviewed practised technology transfer— i.e. member(s) of the client organisation were trained in the use and mainten­ance of the system. This usually meant that once the system was installed the developers' relationship with it ended and it was then up to the purchaser to maintain it. Other suppliers intended to issue updates for their systems, but this too caused problems — some suppliers were already having to make choices between keeping their old systems up-to-date and moving on to new work. As expert systems proliferate, many expert systems purchasers may find that they have been left high and dry by their suppliers and do not have the necessary skills to update and extend their systems. Again, LIS person­nel who have been trained in the development and mainten­ance of expert systems may be called upon to provide maintenance services for others.

2.8 Background, skills and experience of those developing expert systems

Most of the systems seen were being developed in the areas of science and engineering, and the majority of those employed upon these projects were therefore drawn from the computing science and engineering disciplines. Most were largely self-taught in expert systems development techniques, and be­cause the area was so new and there was no existing pool of talent to draw upon, people tended to move up the career ladder very quickly. This has left a shortage of personnel with relevant experience to fill the junior expert systems posts. The expert systems community has therefore had to look further afield for suitable staff.

Knowledge engineering and expert systems work require a complex mix of technical, social and organisational skills and these are not necessarily found in raw computing graduates. The developers interviewed during this survey were looking for people with personality, confidence, intelligence, a good analytical and logical mind, business acumen and a com­mercial awareness of organisational goals. Most did not mind which discipline their recruits came from as long as they could show an aptitude for expert systems work, could learn quickly and would fit in with their organisation. It was felt that LIS had a great deal to offer here, as social, technical and organisational aspects of information and its use are all key facets of LIS education. Expert systems producers inter­viewed during the survey were therefore asked to comment on the usefulness and relevance of specific LIS skills to expert systems work.

Many of the traditional skills of LIS were considered useful in this context. For example, classification, indexing and thesauri building are all ways of organising and repre­senting knowledge and creating order out of chaotic infor­mation sources. Interviewing skills, which are also taught on LIS courses, were also considered essential. The majority of

interviewees also felt that experience of the use of infor­mation sources, user studies, an understanding of syntax and semantics, experience in the use of online and manual infor­mation retrieval systems and in database building and management were also useful.

The overall impression was, therefore, very favourable. However, there were some qualifications. Several felt that LIS graduates should demonstrate that these skills had been practised and applied in real situations, rather than just learnt as theoretical exercises. Others also wanted to see some evidence of technical competence and experience of expert systems building. This experience would best be achieved through the development of practical projects to help in LIS work — for example in cataloguing and classification, referral work, adding value to basic information — and by using their own LIS expertise to develop expert systems for client groups whose needs are not being met by current expert systems producers. LIS graduates might thereby gain experience of expert systems building and establish their credibility with the expert systems community.

3. LIS schools and the LIS challenge LIS schools in Britain have risen to the challenge of expert systems. All are including expert systems work in their courses, and several are also taking up research projects in the area. Although the extent and depth of teaching varies, all students can expect to emerge with at least a basic under­standing of the technology, and several from LIS schools run­ning more in-depth expert systems courses will have had practical experience of designing and building their own sys­tems, using shells. LIS educators believe that students at LIS schools should be prepared for a place in the wider infor­mation society. This means teaching them to use whatever tools and techniques are available to help in the design and creation of better information systems. Expert systems, there­fore, are not seen as an end in themselves, but as a means of building better information systems, and of providing users with high quality, relevant information in an understandable, accessible and friendly form.

In addition to encouraging student projects and disser­tations in the area, LIS schools are also engaged in research in, for example, intelligent interfaces, techniques for knowledge-based indexing and abstracting, using expert systems tech­niques in classification and cataloguing and so forth. A useful overview of some of the areas being addressed by LIS can be found in the proceedings of two recent meetings held to discuss the issue of expert systems in libraries [7, 8].

4. Expert systems in library and information science A lot of the research work underway in LIS schools and in­stitutions is still of an experimental nature. However, given the huge advances in computing over the last decade, one can expect must of today's research to be in practical use in the 1990s. This opens up enormous vistas for LIS practitioners to become involved in the creative development of intelligent information systems. One can imagine, for example, library

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and information centres with suites of knowledge bases on various different topics, which could be consulted interactive­ly by users, and which would be capable of providing them with direct answers to their queries, rather than just biblio­graphic references. Perhaps expert systems software could be loaned out to users in the same way as books, records, videos and cassettes are loaned out today. The nature and quality of information being made available is continually improving — database systems are already branching out to include full text, video, speech, graphics and sound — so LIS centres could actually become dynamic storehouses of knowledge in all its forms.

Already visitors to the US Library Corporation are able to use an intelligent CD-ROM based public access catalogue — the BiblioFile Intelligent Catalog — which uses sound and graphics to create a multi-sensory interface. Users can thus 'locate resources even if they do not know how to conduct a search or describe their specific needs' [9]. The BiblioFile Intelligent Catalog will also use a profile of the user's reading tastes to suggest publications which he or she might like to read. This is just one example of the ways in which AI techniques might be practically used to enhance the quality of the information users receive. Similar systems could also be at work 'behind the scenes' to help in such tasks as catalogu­ing, classifying, automatic abstracting and indexing and in the creation of relevent and useful reference services. This not only frees LIS professionals for more interesting work, it also allows them to give more quality time to the public they serve. As the LIS profession is constantly having to cope with ever-increasing volumes of information and dwindling financial and human resources, any technology that might free them from the mundane, but necessary tasks of library housekeeping and management is surely worth investigation.

5. Conclusions A number of potential openings and opportunities for LIS pro­fessionals to become involved in expert systems development have been highlighted during work on this project. However, to date, these openings and opportunities are still only poten­tial. There is certainly a demand for people to undertake ex­pert systems work, and LIS does seem able to supply a number of necessary skills. At the moment, the expert systems field seems to be fairly open to people with the right aptitude and attitude, regardless of discipline, and LIS graduates from Loughborough Department of Library and Information Studies, for example, have gone on to work as knowledge engineers with established companies. These postgraduates had been encouraged to follow up their interest in expert sys­tems whilst on the LIS course, and accordingly built up skills during project work that were attractive to expert systems employers. However, their eventual employment in the expert systems field owed as much to their own enthusiasm, push and determination as to the education they received. The number of other LIS graduates who might want to work in the expert systems area is probably not large enough to warrant a huge commitment to expert systems work within the LIS

curriculum. Expert systems are rightly seen by LIS educators as just one of a wide range of tools which can be used to build better quality information systems. Students should be made aware of this, and encouraged to see what expert systems could help achieve in LIS, for example, freedom from tedious tasks, a better, more consistent service for their users, better quality information and advice. For these reasons, and be­cause students are likely to encounter expert systems in their work, expert systems should be included in the curriculum, but they should not necessarily be seen as of overriding importance.

This is an important point to make, because although those spoken to on a one-to-one basis during this survey were very positive about the skills which LIS has to offer, a later survey [10] showed that those advertising in the press for expert systems and knowledge engineering personnel demanded far more computing experience than would be feasible in an LIS course. According to the latter survey, software firms and systems designers were looking for people with wide general computing experience and technical skills, to enable them to judge when an expert systems would be appropriate and how to incorporate it into existing systems. They also wanted staff who could work on other projects, and with the ability to pick up skills in any other new areas of technology that emerge. To make a direct contribution to expert systems teams in the future, therefore, would probably require LIS graduates to supplement their skills with more specific computing experience.

Nevertheless, there are still a number of openings for LIS graduates to provide their traditional information services — information collection, collation, organisation, represen­tation and co-ordination — in non-traditional settings, such as expert systems development teams. However, there may still be a problem in persuading others with different priorities, not only that these tasks are essential to good system develop­ment, but also that LIS personnel are the right people to undertake them. It is up to the LIS profession, therefore, to market and sell its skills more effectively. This is also true for LIS graduates; LIS involvement in any new area depends upon how graduates want to use what they have been taught. LIS skills are transferable to many different domains, but it requires an intelligent and imaginative mind to see and act upon new possibilities and openings. Ultimately, therefore, the extent of LIS involvement in expert systems development will depend upon the credibility of the LIS profession as a whole, and upon the enthusiasm and commitment of individuals, and their own ability to impress their personality upon potential employers.

References [1] M. O'Neill and A. Morris, "The role of information

scientists in the development of expert systems: interim report', British Library Research Paper 57, 1989.

[2] A. Morris and A. Reed, Directory of expert system tools. Learned Information Ltd, 1989.

[3] A. Hart, Knowledge acquisition for expert systems.

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Kogan Page, 1986. [4] M. Welbank, A review of knowledge acquisition tech­

niques for expert systems, BT Research Laboratories, Martlesham Consultancy Services, 1983.

[5] A.L. Kidd (ed), Knowedge acquisition for expert systems: a practical handbook. Plenum Press, 1987.

[6] B. Oakley, 'Evaluation criteria for expert systems in a manufacturing environment', presentation given at Expert Systems 88, BCS Specialist Group on Expert Systems Annual Conference, Brighton, 13-15 December, 1988.

[7] F. Gibb (ed). Expert Systems in Libraries; Taylor Graham, 1987.

[8] F. Gibb (ed). Expert Systems in Libraries 2, Taylor Graham (forthcoming).

[9] N. Harrison and B. Murphy. 'Multi-sensory public access catalogs on CD-ROM', Library Hi Tech. 19, 5, 1987, pp. 77-80.

[10] K. Walton and A. Morris. 'Manpower requirements and availability in the expert systems field', British Library Research Paper 65, 1989.

The authors Anne Morris

Anne Morris is a lecturer in Information Processing in the Department of Library and Information Studies at Loughborough University of Technology. Prior to her present position she was employed as a visiting Mathematics lecturer in the Department of Human Sciences at LUT and as a Senior Research Officer in the Institute for Consumer Ergonomics. Her current research interests include the human/ computer interface, the organisational and social issues of introducing expert systems into government and industry, the evaluation of expert system tools and applications for library' and information science. Anne Morris is co-editor of Expert Systems Tools. which will be published by Learned Information in 1989.

Margaret O'Neill

Margaret O'Neill graduated from Sheffield University with a degree in Political Theory and Institutions. She spent several years as Research and Information Officer for the International Labour Office in London, before taking an MSc course in Information Studies at Loughborough University of Technology. She is now a research associate in the Depart­ment of Library and Information Studies at Loughborough University, where she is employed upon a British Library funded project, investigating possible roles for information scientists in the development of expert systems.

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