Introduction to Semantic Web in Library Services

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Introduction to Semantic Web in Library Services
Dr. Devika P. MadalliDocumentation Research and
Training Center Indian Statistical Institute,
Bangalore
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Introduction

• World Wide Web has emerged as a global medium for
  information exchange after the advent of
  Internet.
• As the technologies evolved, WWW became more
  dynamic and responsive than being merely a static
  collection of web documents.
• As business and service sectors grew, the
  potential of Web, various standards, software
  components written in different programming
  languages were deployed.
• Thus, Web service technology has introduced a new
  abstraction layer over and a radically new
  architecture for software, setting the stage to
  grow exponentially to handle complex web
  services (Sabou, 2006).

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Definition

• Semantic Web is a group of methods and
  technologies to allow machines to understand the
  meaning - or "semantics" - of information on the
  World Wide Web (Wikipedia, 2011)

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WWW to Semantic Web

• As an innovative concept, Semantic Web, develops
  techniques to use the existing Web data with
  logics based formal descriptions of their
  meaning. Here ontologies came into play (Gruber,
  1993).

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Web to Semantic Web

• Majority of the web pages (static) are written in
  HTML
• Even the dynamic web pages wrap information in
  HTML
• Though dynamic web pages are retrieved normally
  from structured databases, but they become
  unstructured in HTML. In any case dynamic pages
  are not indexed by search engines. (Deep web
  problem)

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Web to Semantic Web

• HTML is more a word processor of the web not a
  database of the web
• HTML Tags are non-semantic
• For eg
• lthtmlgt lt/htmlgt
• lttitlegt lt/titlegt
• ltbodygt
• ltpgt
• lt/bodygt

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Semantics

• Machine can handle structured data (XML) but not
  unstructured data (HTML)
• Presently, only humans can handle unstructured
  data
• Eg you have a tooth problem can your web agent
  recommend a dentist to you?

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In essence

• Problem Much of the data/information on the web
  is meant for human understanding and not machine
  processable.
• Challenge How to make data machine processable
• One solution metadata and ontologies
  (Librarians' tools)

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Library Vs Semantic Web

• Given that the library and the Semantic Web are
  cultures devoted to increasing information access
  and knowledge discovery, it makes sense to
  explore the foundations of the library (the more
  established institution) and consider what
  primary functions may help advance the Semantic
  Web initiative (Greenberg , 2007).

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Similarities (Greenberg, 2007)
Examples Library Services Semantic Web
Response to information abundance Library to digital library is developed since the abundance of information increased Semantic Web was initiated as a means to more effectively manage and take advantage of the increased amount of digital data
Missions grounded in service, information access, and knowledge discovery Objectives, goals serve the purpose to facilitate information Semantic Web strives to allow data to be shared and reused across applications, enterprises, and community boundaries. It is a collaborative effort led by W3C and partners, based on the Resource Description Framework (RDF)
Part of societys fabric Part of life, for all walks, in all types, physically and virtually Current Web is any indication of Semantic Webs reach, which seems quite logical, the Semantic Web will surely impact millions of peoples lives daily.
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Examples Library Services Semantic Web
Advancement via international and national standards Libraries consolidated development of cataloging codes formalized classificatory and verbal systems and encoding/communication standards (International Bibliographic Description (ISBD) and MAchine Readable Cataloging (MARC), many metadata schemes, Functional Requirements for Bibliographic Records (1998), and Resource Description and Access (RDA) The Semantic Web has followed a similar path as evidenced by a collection of information standards eXtensible Markup Language (XML), RDF, OWL, Friend Of A Friend (FOAF), and Simple Knowledge Organizations System (SKOS).
Collaborative spirit American Library Association, Association of Library Collections and Technical Services, Cataloging and Classification Section (ALA/ALCTS/CCS), committees review cataloging polices and standards, and interact with international organizations (e.g, IFLA and the Dublin Core Metadata Initiative). All of the enabling technologies/standards listed above (RDF, OWL, FOAF, and SKOS) have been developed through working groups and public calls for comment.. The World Wide Web Consortium (W3C), the home of the Semantic Web, involves academic, research, and industry members
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Semantic Web Development
Traditional Services Semantic Web Services
Collection development Semantic Web selection
Cataloging Semantic metadata representation
Reference Semantic Web reference service
Classification Knowledge representation
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Library Approach

• Compare Web Search Engines with Search facilities
  librarians are familiar with (bibliographic
  databases), like
• CD-databases
• On-line databases
• Library automation packages

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Different Search options

• Nested Boolean
• By Field
• By Date
• By Range
• Proximity

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Context Sensitive Search

• Can we do Context sensitive search?
• LIS has many models
• PRECIS
• POPSI etc.
• Are we overemphasising on Recall?
• In the era of 'information glut/deluge', should
  we emphasize recall rather than Precision?

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DRTC-ISI Semantic Web projects

• Living Knowledge European Commission project,
  Frontier and Emerging Technologies (FET)
• AgINFRA European Commission project,
  e-infrastructure project

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Background LivingKnowledge Project

• Living Knowledge (LK) EU FET project n0
  231126 considers diversity as an asset and aimed
  to make it traceable, understandable and
  exploitable, with the goal of improving
  navigation and searching in very large datasets
  (Maltese, etal, 2009).
• Aims of the project
• study the effects of diversity and time on
  opinions and bias in socio-economic relevance,
  especially for seamless representation and
  exchange of information.
• Intuitive search and navigation tools (e.g.
  search engines) need produce more insightful,
  better organized, aggregated and
  easier-to-understand output.

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Living Knowledge Consortium
1. UNIVERSITÀ DEGLI STUDI DI TRENTO, Trento  -
ITALY 2. FUNDACIÓ BARCELONA MEDIA UNIVERSITAT
POMPEU FABRA, Barcelona SPAIN 3. SORA, Vienna
AUSTRIA 4. CONSORZIO NAZIONALE INTERUNIVERSITARIO
PER LE TELECOMUNICAZIONI, Parma  ITALY 5.
STICHTING EUROPEAN ARCHIVE, Amsterdam
NETHERLANDS 6. UNIVERSITÀ DEGLI STUDI DI PAVIA,
Pavia ITALY 7. UNIVERSITY OF SOUTHAMPTON,
Southampton,  UNITED KINDOM 8. DOCUMENTATION
RESEARCH AND TRAINING CENTRE, INDIAN STATISTICAL
INSTITUTE 9. GOTTFRIED WILHELM LEIBNIZ
UNIVERSITAET HANNOVER, GERMANY. 10. MAX PLANCK
GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN
E.V., Muenchen GERMANY
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Living Knowledge Project
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Aginfra

• European Commission FP7 'research
  infrastructure...' project
• http//aginfra.eu/

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Aginfra
A data infrastructure to support agricultural
scientific communities Promoting data sharing and
development of trust in agricultural sciences
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AgInfra Consortium

• University of Alcala (UAH), Spain
• Food Agriculture Organization of the United
  Nations (FAO), Rome , Italy
• National Institute of Nuclear Physics (INFN),
  Italy
• Salzburg Research Forschungsgesellschaft (SRFG),
  Austria
• Institute of Physics, Belgrade (IPB), Serbia
• Computer and Automation Research Institute,
  Hungarian Academy of Sciences (SZTAKI), Hungary
• Agro-Know Technologies (AK), Greece
• 21c Consultancy (21c), UK
• Escuela Superior Politecnica del Litoral (ESPOL),
  Ecuador
• Chinese Academy of Agricultural Sciences (CAAS),
  China
• The Open University (OU), UK
• Indian Statistical Institute, India

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Glimpses of music ontology
Entity type E Musical instrument
Idophone Struck idiophone Plucked
idophone Friction idophone Membranophone Stru
ck membranophone Plucked membranophone Frictio
n membranophone Singing membrane Chordophone
Simple chordophone Composite chordphone Aeropho
ne Free aerophone Non-free aerophone Electrop
hone
Entity type E Kinds of music Dramatic
music opera Religious music Church
music Sacred instrumental music Vocal
music Sequences Capella music Instrumental
music Symphonic music Ensemble music Popular
music Avant garde Chamber music Instruments
concertante
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Glimpses of music ontology (2)
Relation R Person Study Musicologist Organ
ologist Ethnomusicologist Instrument Pianist
Violinist Keyboardist Contribution Writer
Vocalist Lyricist Work Impresario Choral
director Arranger Recording Recording
engineer Audio-visual technician
Attribute A Musical work First
movement Allegro Presto Second
movement Third movement Last movement Music
form Shorter form Dance form Ballroom dance
Media Utilities Storage media Compression File
format Standard and quality Ceritification Cer
tification ...
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Statistics
Objects Quantity
Entity types 637
Relations 55
Attributes 32

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Can we?

• Can we get precise search results for queries
  like
• Who is the author of Tom Sawyer?
• Who works on ontology engineering in India?
• I have toothache! (fetches list of dentists)
• What are the trains between Mumbai and Delhi?

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Challenges for Semantic Web

• Knowledge modelling
• Domain Ontology Building and Inconsistent
  Ontologies
• Crosswalking
• Interoperability

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Semantic Technology for Libraries

• Richer metadata
• Enhanced user-profiling
• Enhanced searching and browsing
• Displaying results
• Connecting ideas and people

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References

• McIlraith, S., Son, T., and Zeng, H. (2001).
  Semantic Web Services. IEEE Intelligent Systems.
  Special Issue on the Semantic Web, 16(2)46 53.
• Sabou, M. (2006). Building Web Service
  Ontologies, SIKS Dissertation Series No. 2006-4.
• Berners-Lee, T., Hendler, J., and Lassila, O.
  (2001). The Semantic Web. Scientific American,
  284(5) 34-43.
• http//xmlns.com/foaf/spec/

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