• Other Journals

An important role for librarians over the next five to ten years is to provide access to online library resources—free, open-access, or purchased, all valuable resources—in an intuitive, easy-to-use one-stop shop and not to be afraid of running a continual beta test in which new services and functions can be added when necessary. To fill this role, librarians and electronic resources managers need flexible, interoperable resource-discovery systems based on open-source software. In addition, we must continue to assess users’ needs and reach out by adapting our systems to fit their requirements, rather than expecting them to come to us; indeed, our very future depends on it. 1

Two decades after the advent of electronic journals and databases, librarians are still grappling with ways to best manage e-resources in conjunction with traditional print resources and at the same time explore new purchasing initiatives and practices, such as demand-driven acquisition of electronic books. In addition, these times of economic austerity are creating budgetary pressures at many institutions of higher education, resulting in librarians having to justify their spending on collections and resource management more than ever.

Techniques for Electronic Resource Management (TERMS) began in 2008 after a discussion about electronic resource management (ERM), current ERM tools, and what was lacking both in current practice and with the systems available. TERMS expands on Pesch’s electronic resources life cycle (see figure 1.1 ) and seeks to become a reference point for those who are new to ERM, those who have suddenly shifted job functions to oversee ERM, and those who may want to implement its recommendations of best practice.

TERMS Tumblr blog http://6terms.tumblr.com TERMS Facebook group https://www.facebook.com/groups/174086169332439 6TERMS on Twitter https://twitter.com/6terms
  • TERMS 1: Investigating New Content for purchase/addition, assigned to Ann Kucera (Baker College, Michigan)
  • TERMS 2: Acquiring New Content, assigned to Nathan Hosburgh (Montana State University, Montana)
  • TERMS 3: Implementation, assigned to Stephen Buck (Dublin City University, Ireland)
  • TERMS 4: Ongoing Evaluation and Access, assigned to Anita Wilcox (University College Cork, Ireland)
  • TERMS 5: Annual Review, assigned to Anna Franca (King’s College, London, United Kingdom)
  • TERMS 6: Cancellation and Replacement Review, assigned to Eugenia Beh (Texas A&M University, Texas)
TERMS Wiki: Main Page http://library.hud.ac.uk/wikiterms/Main_Page

In 2012, TERMS was also endorsed by the Knowledge Base + project in the United Kingdom, which has a project deliverable of providing “workflow management tools related to the selection, review, renewal and cancellation of publications” 2 and has also received interest in the United States from GoKB from Kuali OLE (open library environment), “a community of nine research libraries working together to build the first open-source system designed by and for academic and research libraries for managing and delivering intellectual information.” 3

Electronic Resources and Libraries website www.electroniclibrarian.com

One of the first things to note when performing a literature review on ERM is that there are no independent literature reviews solely on this area. Instead, ERM has now become an integral part of standard literature reviews for acquisitions processing, collection development and management, cataloging and classification, and serials management. At the same time, there are areas of ERM that sit outside of these traditional functioning areas in libraries. This makes performing a literature review on ERM more of a challenge.

From the field of library acquisitions, the issues most readily identified in recent years have been the switch from print processing to ERM and the continued struggle to find management tools that work within the local library context. 6

“Simply put, collection management is the systemic, efficient and economic stewardship of library resources.” 7 The term collection development has been with us since the 1960s. 8 However, it is a constantly evolving area, and as the library collection moves from one dominated by print to one dominated by electronic resources, collection development policies may have been patched rather than redesigned to reflect the different emphasis on delivery. In a 2012 study, Mangrum and Pozzebon found that “over half of the libraries tried to address ER [electronic resources] in some way. However, most policies contain traditional language with a section on library ER inserted into the latter portion of the document.” 9

In regard to collection development and management trends, the two biggest growth areas are e-book purchasing and purchase-on-demand or patron-driven acquisition models. There has been an explosion in collection management literature on these two topics over the past three to four years. 10 A single place for best practices, or from which a local library can create its own localized best practices, is definitely needed: “Bleiler and Livingston stressed that a lack of established policies and procedures for assessment puts a library at risk for financial loss and recommended that libraries create selection policies and standardized methods for assessment, train staff for contract negotiation, and share strategies, policies, and best practices.” 11

In addition to changes to the format of delivery of library resources, libraries must also contend with the impact of today’s economic environment. Hazen suggests that libraries need to rethink their collection development in light of these issues and move from collection to collection and content, where content is “a category that encompasses everything to which a library enjoys ready physical or digital access regardless of ownership status [and] is central to all that we do.” 12

In 2001, Jewell reported on the selection, licensing, and support of online materials by research libraries and concluded that several libraries had developed local systems for acquiring, managing, and supporting electronic resources. 13 Jewell’s report was followed in 2004 by a report from the Digital Library Federation’s (DLF) Electronic Resource Management Initiative (ERMI), which “was organized to support the rapid development of such systems by producing a series of interrelated documents to define needs and to help establish data standards.” 14 The report went on to provide a road map for ERM.

On the back end, we continue to rely on methods developed when we had 250 rather than 25,000 eresources. Information on our electronic resources is currently kept in paper files (license agreements), Excel spreadsheets (vendor contact information and administrative passwords), staff web pages (usage statistics), small databases (trial and decision tracking, divisional library resources, technical problem reports), SFX (ejournal holdings), and our ILS (acquisitions and payment data). Few of these systems are connected to each other; in some cases, information is readily accessible only to one or a few individuals, not by intent, but by the limitations of the storage mechanism. Many procedures are not documented and rely on informal channels of communication. 15

More recently there have been a number of open-source and community ERM systems, such as CORAL 16 and CUFTS, developed by Simon Fraser University (SFU) and implemented by SFU and the University of Prince Edward Island, which view this “technology not necessarily as a way of spending less money, but spending money more wisely.” 17

Another growing area of ERM is the work being performed to develop a suite of standards to support the vast amount of access and management knowledge and myriad of tools needed to maintain adequate access to electronic resources. 18 According to Sarah Glasser, “KBART and IOTA are both working to decrease OpenURL link failures that are caused by metadata deficiencies.” In addition, “PIE-J differs from KBART and IOTA because it is not focused on link resolver errors. Formed by NISO in 2010, PIE-J addresses access barriers that arise from the manner in which electronic journals are presented on provider websites.” 19

There has been a lot of discussion about the implementation of ERM systems in recent years. 20 However, use of these systems is still far from ubiquitous, and many academic libraries have yet to implement or even purchase a system. “A risk of ERMS implementations, more talked about than written about … was that the costs (in added work) to maintain a new system would outweigh the value of the added functionality.” 21 Despite early expectations, Collins and Grogg see the current crop of ERM systems as “less like a silver bullet and more like a round of buckshot.” 22

One of the most time-consuming parts of an ERM implementation is analyzing licenses and inputting them into the relevant fields of an ERM system in order for them to be meaningful to librarians and patrons. The University of Northern Colorado has developed an in-house system to perform license mapping that “makes information that is often deeply embedded within a license readily available to library personnel who could use such information in the daily operations of the library. This information is useful to any library that maintains license agreements for electronic resources.” 23

A panel session at the 2010 NASIG conference concluded that the “ERM system at UC has not solved all their problems, but some improvements have been realized. Budget tracking and staffing continue to be challenges. A final determination of the effectiveness will not be evident until the system becomes a part of the general staff workflow and not considered as something extra.” 24

Collins and Grogg cited workflow management as number one in librarians’ top six ERM priorities. They found that “over a third of librarians surveyed prioritized workflow or communications management, and they called it one of the biggest deficiencies (and disappointments) of ERMS functionality.” 25 This area has also been highlighted by the National Information Standards Organization (NISO), which has created a working group, ERM Data Standards and Best Practices Review, to undertake a gap analysis regarding ERM. 26

In the United Kingdom, the Managing Electronic Resource Issues (MERI) project at the University of Salford aimed “to produce a use case of ERM systems and a preliminary set of requirements for an electronic resource management system, for use by the University of Salford and other HE institutions and system suppliers.” 27 The requirements document from this project went on to inform the SCONUL shared ERM requirements project. An output of these projects was a set of workflows that describe the various processes involved in managing electronic resources. 28 The University of Huddersfield was one of the sixteen UK universities to take part, and like others, had never actually recorded these workflows until asked to do so by the project. All project members found that by recording workflows, they were able to take advantage of efficiencies discovered as part of documenting the process.

One of the objectives of the TERMS blog and wiki was to collect a number of e-resource workflows from a variety of different types of libraries. Both the University of Huddersfield and Portland State University shared their workflows as part of TERMS. The release of the six TERMS via the blog also encouraged other universities to share their workflows and discuss efficiencies; indeed, “rethinking e-resources workflows and developing practical tools to streamline and enhance various inelegant processes have become the priorities.” 29

Since the launch of the first draft of TERMS, the project has now attracted interest in various workflows from different libraries around the world, including the University of Cork, Duke University, Florida Gulf Coast University, and Texas A&M University.

A recent press release by Jisc in the United Kingdom suggested that international collaboration is needed to transform ERM in libraries—“Many of the concerns libraries have in the management of electronic resources are the same across the world”—and that projects such as GoKB and the Knowledge Base + service in the United Kingdom “are exploring community-based solutions.” 30

TERMS Wiki Investigating New Content for Purchase/Addition http://library.hud.ac.uk/wikiterms/Investigating_New_Content_for_purchase/addition Acquiring New Content http://library.hud.ac.uk/wikiterms/Acquiring_New_Content Implementation http://library.hud.ac.uk/wikiterms/Implementation Ongoing Evaluation and Access http://library.hud.ac.uk/wikiterms/Ongoing_Evaluation_and_Access Annual Review http://library.hud.ac.uk/wikiterms/Annual_Review Cancellation and Replacement Review http://library.hud.ac.uk/wikiterms/Cancellation_and_Replacement_Review
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International Conference on Information Computing and Applications

ICICA 2013: Information Computing and Applications pp 488–497 Cite as

Library Management System Based on Recommendation System

  • Fu Jia 4 &
  • Yan Shi 5  
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Part of the Communications in Computer and Information Science book series (CCIS,volume 392)

With wide application of management system, information data grows rapidly. On one hand, people have a large number of information resources. On the other hand, the time cost and difficulty of people finding the proper information increases. In recent years, recommendation system attracts the attention of many researchers including management science, informatics and physics. From the application of recommendation system to library management system, the paper analyzes the key technology, makes application research on book recommendation system based on content filtering and collaborative filtering, and proposes collaborative filtering algorithm which is an improved recommendation algorithm. From the perspective of system application and design, the paper designs the structure, function module and user interface of book recommendation system.

  • recommendation system
  • book recommendation system
  • recommendation algorithm
  • content filtering

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Ma, Y.: Study on an adaptive collaborative filtering book recommendation system. Journal of Information 5, 105-106+109 (2008)

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Zhu, Q., Hao, H., Liu, J., Jiao, P.: Application of DARVM to book recommendation system. Library and Information Service 15, 78–81+87 (2012)

Kun, D.: Study on book recommendation system based on collaborative filtering algorithm. Modern Library and Information Technology 11, 44–47 (2011)

Ma, D.: Construction of book reputation in university libraries. Library Works and Researches 3, 119–121 (2012)

Jing, M., Yu, Y.: Application of collaborative book recommendation model based on borrowing time score. Library and Information Service 3, 117–120 (2012)

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Jia, F., Shi, Y. (2013). Library Management System Based on Recommendation System. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 392. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53703-5_50

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Intelligent libraries: a review on expert systems, artificial intelligence, and robot

Library Hi Tech

ISSN : 0737-8831

Article publication date: 30 June 2020

Issue publication date: 21 June 2021

This paper reviews literature on the application of intelligent systems in the libraries with a special issue on the ES/AI and Robot. Also, it introduces the potential of libraries to use intelligent systems, especially ES/AI and robots.

Design/methodology/approach

Descriptive and content review methods are applied, and the researchers critically reviewed the articles related to library ESs and robots from Web of Science as a general database and Emerald as a specific database in library and information science from 2007–2017. Four scopes considered to classify the articles as technology, service, user and resource. It is found that published researches on the intelligent systems have contributed to many librarian purposes like library technical services like the organization of information resources, storage and retrieval of information resources, library public services as reference services, information desk and other purposes.

A review of the previous studies shows that ESs are a useable intelligent system in library and information science that mimic librarian expert’s behaviors to support decision making and management. Also, it is shown that the current information systems have a high potential to be improved by integration with AI technologies. In this researches, librarian robots mostly designed for detection and replacing books on the shelf. Improving the technology of gripping, localizing and human-robot interaction are the main concern in recent librarian robot research. Our conclusion is that we need to develop research in the area of smart resources.

Originality/value

This study has a new approach to the literature review in this area. We compared the published papers in the field of ES/AI and robot and library from two databases, general and specific.

  • Library system
  • Intelligent systems
  • Artificial Intelligent (AI)
  • Intelligent library
  • Smart library
  • Expert System (ES)

Asemi, A. , Ko, A. and Nowkarizi, M. (2021), "Intelligent libraries: a review on expert systems, artificial intelligence, and robot", Library Hi Tech , Vol. 39 No. 2, pp. 412-434. https://doi.org/10.1108/LHT-02-2020-0038

Emerald Publishing Limited

Copyright © 2020, Asefeh Asemi, Andrea Ko and Mohsen Nowkarizi

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at: http://creativecommons.org/licences/by/4.0/legalcode .

1. Introduction

Understanding the nature of the information needs and defining this need for the system,

Identifying information resources that are relevant to information needs,

Evaluation of existing information resources, evaluation of retrieved information,

Organizing existing information resources, organizing selected information from items retrieved,

Managing existing information resources, managing retrieved information,

Using existing information resources, using retrieved information,

Information and knowledge analysis,

Converting information to knowledge,

Dissemination and transfer of information and knowledge,

Interaction and exchange of information and knowledge.

In WoS, which articles did authors write about ES/AI and robots’ application in the library?

In Emerald Insight, which articles did authors write about ES/AI and robots’ application in the library?

2. Artificial intelligence

AI applies to different sciences. We can say in the library and information science, it more uses in scientific databases and library systems. Such as behavioral science, social sciences, psychology, management and library science and information science. It is related to some of the systems that apply different forms of intelligence such as learner systems, inferior systems, systems with natural language understanding or natural language interpretation, systems with visual scene perception and systems that perform other types of feat that require human types of intelligence ( Bavakutty and Salih, 2006 ). In this branch of the science that involves machines, solutions are utilized to solve complex problems of human behavior. We can present computer-based algorithms based on human behavior and knowledge in using systems. “It is an interdisciplinary field making use of concepts from various fields like cybernetics, information theory, psychology, linguistics, logic, etc. it can use to simulate human behavior and for computer ailed instruction, ES, robots and for NLP. It can also use for Intelligent Retrieval from databases” ( Bavakutty and Salih, 2006 ). In this way, computer software and the use of various computer-based products help in the operation of various types of libraries and their public services and the generation of output products. Automation implies the degree of mechanization where the routines and receptive jobs or operations are left to be performed by machines with little or no intervention by human beings. Lesser the degree of human intervention, greater the degree of automation; this does not mean that automation does away with human beings. On the contrary, human beings are relieved of routine chores, giving them more time for tasks, which require their intelligence. In view of the various features of a modern computer system, we find that it has been applied in several areas of library work. Book acquisitions, cataloging, serials control, and circulation, information retrieval and dissemination, interlibrary loan, cooperative acquisition and cataloging have been automated in the library ( Lakshmikant and Vishnu, 2008 ).

3. Intelligent systems

Intelligent systems (ISs) are defined as any formal or informal system that is able to obtain and process data, to interpret the data by applying technologies of artificial intelligence and business intelligence and to provide reasoned judgments based on that to decision makers as a basis for action ( Sharda et al. , 2017 ).

ISs are computer-based systems that help in the task of subject indexing can be thought of as an ES ( Lancaster, 1997 ). Lancaster has a clear statement relating to the scope of AI: “Computer programs have been developed, which exhibit human-like reasoning, which may be able to learn from their mistakes and which quickly and cleverly perform tasks normally done by scarce and expensive human experts.” AI has a wide application area. Figure 1 gives a good idea of this coverage.

Technologies that are frequently used in intelligent systems: machine learning, case-based reasoning, genetic algorithms, fuzzy logic and natural language processing (NLP).

NLP is another facility of an intelligent system that can use to retrieve information needs from different scientific databases. In the information retrieval process, the user can state his information requirement in natural language, making the searching more easily and fruitful this allows users to state complex retrieval languages ( Bavakutty and Salih, 2006 ).

Business intelligence (BI) as is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis/decision support purposes ( Sharda et al , 2017 ). BI solutions include data access, storage, data analysis and visualization technologies to support better decision making.

4. Expert systems

Expert systems (ESs) are computer-based systems that simulate human decision making. They can integrate with information systems to improve their accuracy and performance ( Singh et al. , 1996 ). Various librarian ES has been developed. Waters (1986) designed the National Agricultural Library’s microcomputer-based ES to help users obtain answers to simple reference questions. In general, they ask questions from the user and take the user’s answer as input, then explain the rationale for decision results. In general, these systems consist of two main elements: A knowledge base and inference engine. The knowledge base encompasses all the information needs that human/librarian experts are using to decide. This information is present in the knowledge base as facts and rules. ESs can make much better decisions than librarian decision makers because their knowledge base can involve the experiences of a team of the best experts. The manner of librarian experts to make decisions is emulated for the design rules of the knowledge base. The rules are consisting of two main phases: “if phase” and “then phase.” The “if phase” is consisting of conditions, and the “then phase” is consisting of results. ESs are distinguished from other computer systems with the application of reasoning through the inference engine. The inference engine simulates human decision makings based on the knowledge base and a rule base ( Figure 2 ).

Knowledge-based indexing ( Amin and Razmi, 2009 );

Natural Language Processing and abstracting ( Albayrak and Erensal, 2004 );

Reference work ( Amin and Razmi, 2009 );

Cataloging ( Weiss, 1994 ) and ( Amin and Razmi, 2009 );

Online information retrieval ( Bellman and Zadeh, 1970 ), ( Sacchanand and Jaroenpuntaruk, 2006 ) and ( Bavakutty and Salih, 2006 );

Using intelligent interfaces in online information storage and retrieval systems;

Information needs analysis and representation, including different services, such as classification, indexing and abstracting;

Reference services;

Development of collection;

Hypertext and hypermedia ( Bavakutty and Salih, 2006 ).

5. Methodology

Descriptive and content review methods are applied to the study. The researchers critically reviewed the articles related to ES/AI and robots in the library. According to this review, the application of ES/AI and robots classified as the following:

Technology : The articles surveyed and evaluated the information management systems in the libraries belongs to this group. These articles relate to usability and implementation. They do not propose or propose an information system or model.

Resource : These articles related to information resources. This category may include the selection, acquisition and use of information resources.

User / End-user : Existing information and knowledge systems/models are usually working based on the opinion of experts/users and end-user behavior. Therefore, applying ES technologies such as inference engine and fact/rule base will improve the performance and accuracy of considered systems.

Service : The articles in this group have proposed an ES or related technology and methods that can be connected and included in ESs to present public or technical services. The public services present to end-users to fulfill their information needs and technical services present to the librarians or any professional user in library activities.

6. Findings

The purpose of the study is to review the articles on intelligent libraries and the use of ES/AI and robots in the libraries. Based on the research questions, the findings presented in two sections. The first section is related to the review of the articles in WoS as a general database in different subjects. The second section is related to the review of the articles in Emerald as a professional database in the Library and Information Science. According to this review, the application of ES/AI and robots classified into four classes such as technology, resource, user/end-user and service.

6.1 ES/ AI and robots’ application in the library (WoS)

The topics of “expert system” and “library” were searched in the WoS database on 10th Oct 2017. We found 1,208 documents related to this topic. Then we have refined the topics through “Research Area,” “Document Type.” In the research area, we selected the area of “Information Science, Library Science.” We chose “article” for “document type” and excluded unrelated articles. Finally, found 14 articles as a result, which are shown in Table 1 .

The review of papers shows the fading of the ES/AI in recent studies. It is found that the majority (46%) of the paper worked on the experts’/users’ behavior. This is even though no research has been done on the use of intelligent resources using ESs between the years 2007–2017 on the WoS ( Figure 3 ). However, the studies that are related to information systems have a closed relation with the knowledge and opinion of experts. Using ES technologies such as inference engine and fuzzy rule base may increase the accuracy of them. Therefore, the current information systems can be improved by integration with ES technologies. ESs use in intelligent libraries. In general, the information provided to users in a library leads to a change in the behavior of the user’s knowledge and creates learning. The intelligent library uses an appropriate protocol for the exchange of information. This protocol is unique, and it is designed to help, confirm or perform the terms of the agreement. The terms of the agreement include a series of guidelines that will be carried out automatically. These guidelines relate to information sources, services, and technology for distributing and exchanging information. For operating a smart library, resources and services must be available under the agreement. All users must use the digital signature and agree to the terms of the agreement. Smart libraries can exchange information based on the internet of Things (IoT).

Recently the researchers tried to increase the ability of librarian robots by applying the new methods. We searched for the topic of “Librarian robot” using WoS on 10th Oct 2017. Then we limited the results to the duration of 2007–2017. We excluded unrelated articles and finally found 15 articles and proceeding papers as a result, which is shown in Table 2 . In this table, we determine the research area related to applied methodologies of papers in the “Research area of publication source.” A summary of the applied method is explained in “Method,” and the main contribution of papers is mentioned in “contribution.”

The most recent papers that are related to librarian robots are in the area of service ( Figure 4 ). Improving the technology of gripping, localizing and human-robot interaction are the most discussed issues in librarian robots. Librarian robots can be used in large libraries. This robot reduces a lot of common and duplicate activities in different places of the library, especially at the library’s repository. For example, this robot can be helpful in shelf-reading activity. There are some imaginations that the use of librarian robot creates a gap between information and people. Smart libraries and librarian robots are always faced with this challenge. But not a way out of using new technologies, because the development of information does not coincide with the development of expert human resources. In many libraries, librarian robots can be helpful in solving library problems. Only the small number of the studies are related to resources. It is shown that we need to develop our research in this area.

The library should take special care of every aspect related to the man-machine interface: favoring systems standardization, avoiding the accumulation of different equipment, using a clear, brief and direct language, including images and sound, representing reality and reflecting the human mental patterns ( De Prado, 2000 ). AI techniques such as genetic algorithms, artificial neural networks, ESs, and fuzzy logic or hybrid methods can improve librarian robots to reflect human mental patterns.

6.2 ES and robot’s application in the library using Emerald Insight [1]

Table 3 shows the review of the papers in the field of ES/AI and robots, and library exported from the Emerald Insight as a specific database in the library and information science.

Figure 5 shows the most recent papers exported from Emerald Insight, which are related to ES/AI and robot in the library are in the area of service. The finding is the same as the exported papers from the WoS database.

The following trends show a line graph of the relative frequencies across the main category in the abstracts of the articles ( Figure 6 ). The thematic interaction was observed in the main categories of the articles based on their keywords. Most common categories in the abstracts are digital, information, library, search, and user.

Figure 7 shows a line graph of the relative frequencies across the main category of the keywords of the articles. The thematic interaction was observed in the main categories of the articles based on their keywords. Most common categories in the abstracts are digital, information, Internet, library, and systems.

7. Discussion

The ES should be considered only when development is: “possible,” “appropriate,” and “justified” ( Lancaster, 1997 ). This question must be answered before we initialed an ES project. Waters (1986) gives some good guidelines on when we should consider using ESs. An ES has received a lot of attention from the research community in the 1980s. Unfortunately, much of the writing sensationalized the field expectations dramatically ( Lakshmikant and Vishnu, 2008 ) fueled by public expectations began to over-promise misconceptions about what AI can and cannot do arise and they persist today. Many rushed into the field in search of quick answers and quick profits. Several Al researchers saw what was happing and feared a backlash. Once all the excitement wore off during 1988–90, things did begin to change some of the realities and limitations of the AI techniques became evident. An AI backlash has resulted in ascertaining to an extent, but fortunately, it has not been wide-scaled instead. The optimism remains with a better sense of realism than before, and both the benefits and limitations are better appreciated.

An expert of the problem available;

Experts have the time for the ES development project;

Experts can articulate their knowledge and methods;

The problem is not too complex, but knowledge intensive;

The problem is not poorly understood;

The problem requires cognitive skills only.

Reliable visual localization;

Robust and fault-tolerant force-guided extraction;

Performance adequate for books of different sizes and thicknesses;

Active book searching;

Combine navigation and active vision;

Fault-tolerant probabilistic strategy.

In the context of robot librarians and AI has been investigated by limited number of studies. In the database of Emerald, only one article was found ( Yao et al. , 2015 ). They introduced a collaborative library service based on artificial intelligence. They developed an intelligent robot called Xiaotu (female). The task of this robot is to provide online reference services. Four factors are important in the success of this robot: artificial intelligence, self-learning, vivid logo and language, and modular architecture ( Yao et al. , 2011 ). Yuehu and Yanqing (2012) studied using the internet technology of objects. They have tried to look at smart sets along with the robot librarian. Then compare the smart library with other libraries. Kyrarini et al. (2017) presented a framework called “Skill Robot Library” (SRL). This framework has the authority to store key points of the route. In fact, this robot can store user’s behavior in information retrieval, and it will work based on this stored behavior. Behan and O'Keeffe (2009) designed as a mobile robotic assistant, called “LUCAS” for the University of Limerick. This assistant is a help system that supports users intellectually. Kim and Kohtaro (2009) tried to provide robots based on the structured data. This study introduces a conventional and intelligent environment for a librarian robot. This environment is based on RFID technology for these systems. In another study, reference services were investigated using the instant messaging (IM) smart robot. The Shanghai Jiaotong University Library is presented for example. This library provides the IM robot’s intelligent library service using BotPlatform ( Yi et al. , 2011 ).

8. Conclusion

A review of the articles shows that we can use expert and intelligence systems in different library activities and information services. The main goal is to provide specialized services with the help of librarians and information resources specialists. Library services include technical and public services. Both categories use intelligent systems and ESs. These activities include the provision of information resources, the organization of information resources such as classification, indexing, and abstracting, the storage and retrieval of information from library systems, reference services, and circulation desk. We classified the scopes of the researches into four classes “technology,” “user,” “service,” and “resource.” A review of the articles shows that users’ information behavior is a very good way to design intelligent systems. The storing information in cloud and non-cloud spaces allow for the development of these systems. In big data and social networks where scientific information resources are exchanged, intelligent agents can play an important role. User profiles can be a good source for designing ES algorithms based on user knowledge. ESs are the most useable intelligent system in library and information science, which mimic librarian expert’s behaviors to support decision and management. However, individually using this technology is reduced in recent studies. Most information systems have a closed relation with the knowledge and opinion of experts. Using ES technologies such as inference engine and fuzzy rule base may increase the accuracy of them. Therefore, the current information systems can be improved by integration with ES technologies. The librarian robot can reduce the usual and repetitive activities on library shelves. Almost the third of the articles in Emerald Insight in ES have related to the “user” scope, and in librarian robot (18% in WoS), most of the articles have related to the "service" in Emerald Insight and in WoS as well. One of our conclusion is that we need to further research in the area of smart resources.

research paper for library management system

AI Coverage ( Lancaster, 1997 )

research paper for library management system

ES elements

research paper for library management system

The scope of the articles in the field of ES/AI and library (WoS)

research paper for library management system

The scope of the articles in the field of library and robot (WoS)

research paper for library management system

The scope of the articles in field ES/AI and Robot in the library (Emerald Insight)

research paper for library management system

Relative frequencies across the main categories in the abstracts of the articles and thematic interaction between them in the field of ES/AI and robot and library (Emerald Insight)

research paper for library management system

Relative frequencies across the main categories in the keywords of the articles and thematic interaction between them in the field of ES/AI and robot and library (Emerald Insight)

Articles related to ES/AI and library (WoS)

Articles related to library and robot (WoS)

Articles related to ES/AI and Robot in the library (Emerald Insight)

https://www.emerald.com/insight

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Further reading

Berube , L. ( 2004 ), “ Collaborative digital reference: an ask a librarian (UK) overview ”, Program , doi: 10.1108/00330330410519189?fullSc=1 .

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Acknowledgements

This research has been supported by the “Project no. NKFIH-869-4/2019 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Tématerületi Kiválósági Program 2019 funding scheme.”

Corresponding author

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research paper for library management system

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