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International Journal of Computer Sciences and Engineering Open Access
Review Paper Vol.-7, Special Issue-16, May 2019 E-ISSN: 2347-2693
Applications of Artificial Intelligence in Academic Libraries
S. Vijayakumar1*, K.N. Sheshadri2
1,2Centre for Research in Library and Information Science, Presidency University, Bangalore, India
Corresponding Author: vijaykumars@presidencyuniversity.in, Mob: +91-9980352646
DOI: https://doi.org/10.26438/ijcse/v7si16.136140 | Available online at: www.ijcseonline.org
Abstract— The application of artificial intelligence involves the areas such as artificial intelligence, expert system, artificial
neural network, fuzzy logic, image processing, natural language processing, speech recognition, robotics etc. Though these
areas are not separate, at times two or more applications are contributes to enrich the library services. In this article, the authors
have explored the various possible applications of artificial intelligence as mentioned above. In addition, authors explain the
possible areas where few of these applications can be implemented which enhances the quality of services and thereby create
the potential impact of AI on library services.
Keywords— Artificial Intelligence, Academic Libraries, Expert systems, Robotics, Machine Learning.
I. INTRODUCTION
An attempt to replace human power with the machines was
the creation of the first industrial revolution. The impact of
artificial intelligence and advanced computer technology on
the nature of future libraries will be enormous, and the
quality differences will be different from what our current
work expects. Most library-oriented artificial intelligence
applications developed until today or currently under
development are basic business aids of the runtime because
they built today. Potential applications include systems that
help perform the different tasks for the library such as
people, budget, collection development, scheduling etc.
These applications include systems for enhancing user
services, such as ready references and information storage &
retrieval.
II. REVIEW OF LITERATURE
Norman Jacknis (2017) expressed in his blog that though
librarians have acquired many skills to organize the
information and making it accessible anywhere, libraries can
ensure the application of the tools for the new generation of
knowledge, which surpasses Google search has been
developed for academic purposes. It is explained in the
Merriam-Webster (2019) that, artificial intelligence is “a
part of computer science that deals with giving ability to the
machines to look like they have natural human intelligence.”
Artificial intelligence (AI) is perhaps most familiar to the
public in many ways today. According to Kristin Whitehair
(2016), with many AI applications, it gives libraries the
opportunity to change the emphasis and attention. The way
we navigate the information is kept altering. AI gives a very
useful shortcut to apply this knowledge and produce better
outcomes. Libraries focus on enhancing the access to content
with the application of AI. We have been watching the
evidence of this transformation toward AI application with
many libraries initiating and providing Makerspace
competences. The libraries are positioning themselves to take
advantage of the application of cognitive computing in
general and artificial intelligence in particular for their
potential utility as a tool for refining the quality of library
services. Liu (2010) in her articles provide a comprehensive
literature review on the utilization of intelligent agent
technology in the library environment. The researcher here
expressed that both AI and librarians reinforcing each other
in the interest of providing the best service to patrons. Hence,
application of AI can never be a threat for librarians, rather
supplementary.
III. ARTIFICIAL INTELLIGENCE
Artificial Intelligence focuses on non-algorithmic methods
for solving problems and symbols. AI depends on the skill of
mapping the symbols. New applications have created great
opportunities for informational researchers, such as
multimedia systems, digital libraries, GISs and e-commerce.
As the application becomes increasingly powerful,
diversified, pressing, several known problems in finding
information became even more important in this
technological era
The basic technique in the IR includes the identification of
key characteristics in the object. For example, natural
language processing and automatic indexing used to
distinguish meaningful words. In general, image
International Journal of Computer Sciences and Engineering Vol. 7(16), May 2019, E-ISSN: 2347-2693
© 2019, IJCSE All Rights Reserved 137
identification uses texture, color or shape - based indexing
and segmentation techniques. They used to identify
meaningful descriptions in their streams for applications such
as audio, video, speech recognition and scene segmentation.
In order to perform semantic analysis on multimedia objects
or text, several types of techniques are used.
Machine learning, graph-based clustering and classification,
statistical-based multivariate analysis, artificial neural
networks, and evolution-based programming are popular
techniques. These technologies are good alternative for
summarizing, analysing and processing a large number of
multimedia messages, which is different and rapidly
changing. The results of the semantic analysis process
expressed in the form of semantic networks, decisions, rules,
or predicate logic. Activation-based, Propagation-based
reasoning methods commonly used to negotiate various
structures of large-scale knowledge.
All search engines for text, images and videos increase user
expectations for presenting and manipulating information.
Recent advances in language and platform development, for
example VRML, Java, OpenGL, and the provision of
affordable high quality graphic workstations have also made
visualization of information perspective in the field of
research.
Although artificial intelligence is a young discipline, it
makes society beyond imagination.
Figure 1. Pictorial diagram of AI Components
AI sub-areas, namely expert systems, natural language
processing, pattern recognition and robotics, aim to simulate
human intelligence with computers. Some recent computing
techniques and areas for artificial intelligence development
discussed below:
A. Expert System: The Expert System is a computerized
knowledge system that serves as a gateway or interface
for providing access to the database and obtaining
relevant information. It ranges from simple regulatory
systems with flat data to very large-scale, integrated
development that takes many years to develop. An
expert system is a computer program that offers expert
advice, decisions or solutions to a particular situation.
Knowledge base, inference engine and user interface are
the various components of the expert systems.
B. NATURAL Language Processing: One of the long-
standing goals of CS is to teach computers to understand
the language we are talking about today. The ultimate
generation of computer language is Natural Language.
Artificial intelligence scientists have been able to build a
natural language interface using a limited vocabulary
and syntax. The computer can understand the key
language concepts within a question and solution
through the natural language process. It aims to design
and create a computer that analyzes the language that a
person uses, understands, and generates. Speech
synthesis, machine translation, linguistic approaches,
information recovery, information extraction and speech
recognition are the various elements of natural language
processing.
C. Pattern Recognition: The new stimulus and the pre-
stored stimulus coincide closely by this process. This
process takes place continuously through the lives of all
living beings. Pattern recognition is being studied in
many areas, including psychology, ethology, cognitive
science, and informatics. Pattern recognition based on
prior knowledge or on data from the patterns. Classified
patterns typically consist of groups of dimensions or
observations that define points in a multi-dimensional
space. Components for pattern recognition are data
collection, pre-processing, selection of characters,
selection of models and training, and evaluation.
D. Robotics: The field robotics is frequently describes as an
AI subfield, which deals with motor and perceptive
tasks. Robot is a mechanical device, which carries out
automation tasks using artificial intelligence techniques,
either directly human control or a predetermined
program.
E. Machine learning: Arthur Samuel, an American pioneer
in computer gaming and artificial intelligence, invented
the term 'machine learning' in 1959 and defined it as “it
gives computer the ability to learn without explicit
programming”. Depending on the nature of the "signal"
or "responses" to the learning system, machine-learning
applications divided into the three primary categories,
i.e., (a) Supervised learning (b) Unsupervised learning
International Journal of Computer Sciences and Engineering Vol. 7(16), May 2019, E-ISSN: 2347-2693
© 2019, IJCSE All Rights Reserved 138
(c) Reinforcement learning (d) Semi-supervised
learning.
F. HAMLET: The system is HAMLET (How about
Machine Learning Enhanced Theses) currently a
developer at the Berkman Klein Center for Internet and
Society at Harvard. HAMLET uses the doc2vec
algorithm. This is an algorithm for estimating the
similarity in meaning between different documents,
based on a widely used algorithm word2vec, which
estimates the similarity between words. It explores the
results online at the URL in the gray box. HAMLET has
three prototype interfaces: a recommendation engine, an
uploaded file oracle, and a literature review buddy.
IV. APPLICATION OF AI COMPONENTS IN
LIBRARY SERVICES
A. Expert System in Library Services
Library activities related to reading materials, users and staff.
Application of expert systems where dialogue between staff
and users, users and databases is promising. The expert
system will help the librarian to understand the need for
improvement in productivity. A well-programmed expert
system will also improve quality.
1) Reference service is the foremost activity of any library
and the expert system will serve as a substitute for
reference librarians. REFSEARCH, POINTER, Online
Reference Assistance (ORA), AMSWERMAN,
PLEXUS all of these systems are advisory systems for
locating reference resources and factual data.
2) Cataloging is one of the oldest library techniques.
Recent attempts to automate cataloging through expert
systems have focused on descriptive cataloging because
it is rule-based (AACR2). There are two ways to apply
artificial intelligence techniques in cataloging: (a)
Human-machine interfaces, where intellectual work is
divides between the intermediary and the support
system. (b) An expert system with full cataloging
capabilities associated with electronic publishing
systems. Since the cataloging text is generates online, it
can be passed through a knowledge-based system, and
the intermediary does the cataloging process without any
intellectual input.
3) Classification is the basic activity of a knowledge
organization. Therefore, it is prominent in all systems
that organize knowledge in libraries and information
centers. The application of expert systems in the field of
library classification includes Coal SORT, EP-X, and
BIOSIS.
4) Indexing of periodicals is another area where expert
systems are developed. Indexing a periodical article
involves identification of concepts, to translate the
concepts into verbal descriptions, & selecting and
assigning controlled vocabulary terms that are
conceptually equivalent to verbal descriptions. The
reason for automating the intellectual aspects of
indexing is to improve indexing consistency and quality.
Based on the information provided by the indexer, the
systems can arrive at appropriate preferred terms
automatically to assign relevant subdivisions. The
system can make inferences & based on the inference, it
can take appropriate action. The 'Med Index' is the best
example of the library indexing system. As there is a
lack of exposure to these expert system oriented services
in many libraries, very few library users have interacted
with knowledge-based systems. In addition, most of
these expert systems oriented services are evolving over
the period and undergoing many improvements to suit
the needs of the library patron.
5) Acquisition: The users of the library have a significant
role to play in building library collection and online
resources in particular. Several systems have been
incorporates for the acquisition of these resources.
Monograph Selection Advisor, a pioneering effort in
applying this emerging technology is another area of
building library collection. Specifically, the task
modeled is the item-by-item decision that a subject
bibliographer makes in selecting monographic. The
prerequisite is that the knowledge base has to be broad
enough and the interfacing aspect must be easy enough
for the library to get the desired information from the
machine.
B. Natural Language Processing in Library Services
When we think of the term NLP, the first thing that comes to
mind is the ability to speak or write a complete sentence and
have a machine process of requesting and speaking. NLP can
applied to many disciplines, including libraries. When apply
to the field of library and information science, more
specifically, to search databases such as the Online Public
Access Catalogue (OPAC), indexing is the basis of document
retrieval. The purpose of the index is to improve the
precision of retrieving parts of the relevant documents; and
to reduce the proportion of recalls and related files retrieved.
C. Machine learning in Library Services
One specific challenge that is ripe is the improvement of
library metadata generation. Libraries, through various
vendors as part of the purchasing and acquisitions process,
acquire thousands of pieces of metadata for print and digital
resources made available to their library users. In cases
where an e-book platform does not include metadata,
libraries they generate their own. For the increasing majority
International Journal of Computer Sciences and Engineering Vol. 7(16), May 2019, E-ISSN: 2347-2693
© 2019, IJCSE All Rights Reserved 139
of born-digital resources, machine learning provides an array
of possible tools to help libraries generate metadata for
digital resources, allowing cataloguing to not only increase
the speed of metadata generation but also vastly improve the
depth and breadth of subject terms.
D. Robotics in the Library Services
The robot is "A reprogrammable, multipurpose manipulator,
automatically controlled, programmable in three or more
axes, which can be fixed on the location or portable for use
in automation applications". Libraries providing an increased
variety of services and resources for digital libraries, they are
still acquiring a great number of printed documents. This
combined pressure to provide electronic and printed
resources and services has caused serious space constraints
for many libraries, especially academic libraries and
research. The objective of CAPM (comprehensive approach
to printed material) is to build a personalized robotic
scanning system based on a series, which allows the
browsing of imprints in real time via the web interface. The
user includes a CAPM system that, in turn, starts a robot that
recovers the item requested. This item iss delivered to
another robotic system, which opens the item and rotates
pages automatically.
E. Intelligent Interfaces to Online Databases
Online access to databases is still difficult for many potential
users. The user may need to know different communication
protocols, master language control, search techniques,
database file structures, and terminological terminology. The
aim of the intelligent interface is to facilitate the access to the
construction of some of the necessary knowledge in the
front-end software used to test the online search system. This
goal does not coincide with the goal of creating an intelligent
search system. The interface of access to existing online
systems, with all their limitations and disadvantages, so it
can be equally successful as an on-line search system. The
interface does not solve the problem of restructuring the
database, but rather allows the search system itself to make
the approach more intelligent.
Searching online databases can be helpful in these ways:
select the appropriate hosts and databases;
allow the seeker to state an information want in their
own terminology;
determine the level and access to the information
requested;
adapt the extent of the information to be retrieved;
formulate the vocabulary query used in the selected
databases;
express a search query in the format required; and
Present search results in a helpful way, e.g. ranking in
the order of probability of relevance.
V. CONCLUSION
There are a number of possible applications of Artificial
Intelligence implemented and they have been creating a
positive impact on libraries. This has proved that applications
of AI saves time and money in almost all sectors in the
society. The application of AI in the academic libraries have
been increasing in very high speed. As authors of this paper
discussed, implementation of AI in libraries has triggered the
discovery of many new ideas. The development of expert
system libraries greatly benefited, sometimes it appears like
“Librarianship is at stake” and now it is challenging to ensure
the values of librarianship. Artificial intelligence (AI)
systematically tops popular lists of the most imperative
emerging technologies. With a mixed feeling of fear and
eagerness, readers seem to agree that the AI shapes the future
libraries.
REFERENCES
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Library Systems in Iran: A taxonomy study," Library Philosophy
and Practice, vol. 2, March-April 2018.
[2] S. S. Mogali, "Artificial Intelligence and its applications in
Libraries," 2014.
[3] J. Charles W. Bailey, "Intelligent library systems: artificial
intelligence technology and library automation systems,"
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1991.
[4] W. Kristin, "Libraries in an Artificially Intelligent World," Public
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[Accessed Feb 2016].
[5] B. Johnson, "Libraries in the age of artificial intelligence,"
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[6] G. Liu, "The application of intelligent agents in libraries: a
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[7] N. Jacknis, "The AI-Enhanced Library," [Online]. Available:
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[8] Merriam-webster, "Definition of Artificial Intelligence," 2019.
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2019].
Authors Profile
Mr. S Vijayakumar working as Assistant
Librarian in Presidency University,
Bangalore since Aug 2017. He pursed
Bachelor of Arts from University of
Mysore in 2014 and Master of Library and
Information Science from University of
Mysore in the year 2016. He is currently
pursuing Ph.D. in Department of Library & Information
Science, University of Mysore. He received 4 Gold Medals
and One Cash Prize from University of Mysore for securing
First Rank in MLISc in 2016. He is qualified NET & KSET.
International Journal of Computer Sciences and Engineering Vol. 7(16), May 2019, E-ISSN: 2347-2693
© 2019, IJCSE All Rights Reserved 140
He has published 8 research papers in national &
international journals, conferences and edited books.
Dr. K N Sheshadri is the Senior Librarian at
Presidency University, Bengaluru since Jan
2016. He has over 16 years of professional
experiences. He worked as Librarian in
BITS Pilani Dubai Campus in Dubai, UAE
for 12 years. Prior to join BITS, he worked
as information officer for one year at
Jubilant Biosys (Bioinformatics Company) at Bengaluru. He
received his doctoral degree from Mangalore University in
the year 2013 under the supervision of Prof. Dr. D.
Shivalingaiah. His topic of research was “Networking of
Technical Libraries in UAE: A Consortium Model”. He has
more than 20 research articles in national & international
journals and international conferences. He has guided 4
MPhil dissertations in Library and Information Sciences.
Also he has served as examiner for 2 PhD candidates in
Library and Information Sciences. Presently guiding two
PhD candidates in Library and Information Science,
Presidency University.
