Dr.Devika P. Madalliassets.aims.fao.org.s3-eu-west-1.amazonaws.com... · Dr.Devika P. Madalli...
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Dr.Devika P. MadalliDr.Devika P. Madalli
Indian Statistical Institute
Bangalore, INDIA
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Indian Statistical Institute
• Established in: 1931
• Institute of National Importance: 1959
• First to commission a computer in India• First to commission a computer in India
• Founder: Prof. P.C. Mahalanobis
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DRTC
Documentation Research and Training Centre
• Established: 1962
Semantics for Information Management in Agriculture, UNFAO - Rome, Italy
• Founder: Prof. S.R. Ranganathan
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S.R. Ranganathan
• Father of Indian Library Science
• Father of Faceted Classification (ontology)
• Creator of Colon Classification
• Creator of Classified Catalogue Code• Creator of Classified Catalogue Code
• Creator of Chain Indexing (followed by BNB)
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Research Areas
• Natural Language Processing
• Quantitative Methods in LIS
• Information Retrieval and Data Mining• Information Retrieval and Data Mining
• Knowledge Management
• Digital Libraries
• Multi-Lingual Information Systems
• Classification ( Ontologies)
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Software Developed
Manu For thesaurus construction
Prometheus POPSI based index generation
Semantics for Information Management in Agriculture, UNFAO - Rome, Italy
Panizzi For automatic identification of Bibliographic data elements from the title page
Viswamitra & Vyasa
Automatic construction of Call Numbers, maintenance of Schedules, indexes, etc
Pygmalion Packages for retro-conversions
Ekalavya Computer aided teaching packages
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DL Test-beds
Eprints 2
Fedora
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Fedora
CDSWare
Green Stone Digital Library
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LDL : Librarians’ Digital Library
https://drtc.isibang.ac.in
powered by
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Communities & Collections
Library and Information Science
• Publications / Articles• Publications / Articles• Theses / Dissertations• PowerPoint Presentations• Demo of Multilingual Documents• Photographs of LIS activities• Photographs of S.R. Ranganathan
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Membership From…
• India
• USA
• France
• UK• UK
• South Africa
• Thailand
• Austria
• Italy
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Harvester Service
http://drtc.isibang.ac.in/sdl
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Discussion Forum
DLRG: Digital Library Research Group
• Presently over 250 members
• http://drtc.isibang.ac.in/dlrg
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Indus(a DSpace based harvester)
� 48 Asian Countries
� 26 Countries have repositories (openDOAR)
Around one third of them have exclusive Agricultural � Around one third of them have exclusive Agricultural
repositories
� More OAI-based Agri. Journals
http://drtc.isibang.ac.in/indus
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Indus
� Indus covers both repositories and OAI based Journals
� Presently
− About 10 countries repositories are harvested− About 10 countries repositories are harvested
− 57 Journals on Agriculture
− 8 Digital Repositories
− About 50k records
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Work on Vocabularies
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� Space and Time are the two fundamental dimensions of theuniverse of knowledge
Space is essential to understand the physical universe
GeowordNet-- Biswanath Dutta, Fausto Giunchiglia, VincenzoMaltese
� Space is essential to understand the physical universe
� by “Space”, it is meant, surface of the earth, the spaceinside it and the space outside it
� it can be interpreted by its geographical features includingothers like, buildings and other man-made structures
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Issues� There is a need for supporting semantic interoperability
between people and also between applications
� Definition of entity types and corresponding properties havebecome a central issue in data exchange standards
� Current standards do not address the actual semanticinteroperability problem
� mainly aim at syntactic agreement by fixing the standardterms
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Approach
� GeoWordNet*, a multi-lingual ontology that overcomes thequalitative and quantitative limitations over previousontologies
� It is based well founded methodologies and guidingprinciples for developing the faceted ontologies
*a subset of GeoWordNetis available as open source in plain CSV and RDF formats and can be downloaded from:
http://geowordnet.semanticmatching.org/
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Main contribution
� We proposed here a methodology and a limited set ofguiding principles to construct geo-spatial ontology
� They are based on the notion of facet and analytico-synthetic approach borrowed from Library Sciencesynthetic approach borrowed from Library Science
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[First Introduced by Ranganathan (1930s) in Library and InformationScience]
� “A generic termused to denote any component – be it a basic subjector an isolate – of a compound subject, …” - Ranganathan
Facet
� It is a category that expresses someaspectof the knowledge beingdescribed
� A facet is a hierarchy of homogeneous terms, where each termin thehierarchy denotes a primitive atomicconcept
� E.g., Organ facet, geographical facet, language facet, property facet,author facet, religion facet, commodity facet, etc.
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Facet Example:
Language
by Indo-EuropeanTeutonic
GothicEnglish
American EnglishGerman
LatinItalianFrench
GreekGreek
by DravidianTamilTulu
by Geographic locationAsian language
(collective treatment)Japanese languageIndian language
African language
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Step 1: identification of the atomic concepts � Some of the relevant sub-trees in WordNet are:
� location
� artifact, artefact
� body of water, water
Semantics for Information Management in Agriculture, UNFAO - Rome, Italy
� body of water, water
� geological formation, formation
� land, ground, soil
� land, dry land, earth, ground, solid ground, terra firma
Note: not necessarily all the nodes in these sub-trees need to be part of the space domain. For example, the descendants of artifact, like, article, anachronism, block, etc. are not. 25
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AnalysisRiver
• a body of water
• a flowing body ofwater
• no fixed boundary
• a body of water
• a flowing body of water
• no fixed boundary
Stream
• the well definedelevated land
• formed by thegeological formation,where geologicalformation is a natural
Hill
• the well definedelevated land
• formed by thegeological formation(where geologicalformation is a natural
Mountain
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• confined within abed and streambanks
• larger than a brook
• confined within a bedand stream banks
formation is a naturalphenomenon
• altitude in general<500m
formation is a naturalphenomenon)
• altitude in general>500m
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Body of water
Flowing body of waterStream
BrookRiver
Stagnant body of waterPond
Landform
Natural depressionOceanic depression
Oceanic valleyOceanic trough
Continental depressionTrough
Synthesis
Pond TroughValley
Natural elevationOceanic elevation
SeamountSubmarine hill
Continental elevationHillMountain
* each term in the above has gloss and is linked to synonym(ous) terms in the knowledge base 27July 2 - 3, 2015
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� Space [Domain]
� by geographical features [Entity types]
� by water formation
� by land formation
� by land
� by administrative division
� …
Facets and sub-facets
� by relations [Relation]
� spatial relation
� direction, internal, external, longitudinal, sideways, etc.
� functional relation (e.g., primary inflow, primary outflow)
� …
� by property [Attribute]
� latitude
� Longitude
� dimension
� …28
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Vocabularies to trace
Knowledge Diversity
�Living Knowledge Project
�FP7 FET project
http://livingknowledge.europarchive.org/
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Challenges
� IR Challenges in general
� High recall
� Low precision
� Natural language processing
� Disambiguation problems
INTRODUCTION::KOS::DERA::DEMO::CONCLUSION
Disambiguation problems
� E.g., a word “bass”
� Sense 1: A kind of saltwater fish
� Sense 2: Tones of low frequency
Natural language sentences:
� I went fishing for some sea bass
� The bass line of the song is too weak
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Solution
� A Large Scale, Domain Specific LR based on Facetbased KO is a better Resource for addressing thechallenges of Low Precision and High Recall
INTRODUCTION::KOS::DERA::DEMO::CONCLUSION
challenges of Low Precision and High Recall
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Resources
� Language resources� General purpose language resources
� WordNet (http://wordnet.princeton.edu/)� MultiWordNet (http://multiwordnet.fbk.eu/english/home.php)� EuroWordNet (http://www.illc.uva.nl/EuroWordNet/)� Rogets’s thesaurus
� Domain specific language resources� Dewey Decimal Classification (DDC)� Dewey Decimal Classification (DDC)� Library of Congress Classification (LCC)� Universal Decimal Classification (UDC)� Bliss Bibliographic Classification (BC)� Colon Classification (CC)� AGROVOC� Art and Agriculture Thesaurus
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DERA
[F. Giunchiglia and B. Dutta, 2011]
� Consists of:
� Domain [D]
� Entity [E]
� Relation [R]
� Attribute [A]
INTRODUCTION::KOS::DERA::DEMO::CONCLUSION
� Attribute [A]
� It is a further refined and simplified form of Bhattacharyya’sDEPA
� Has direct mapping to DL
� Emphasis is on the named entities
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Entity� An elementary component that consists of classes (categories) and their
instances, having either perceptual correlates or only conceptual existence in adomain in context
� E = <{e}, {E}>
� e = Entity class - consists of the core classes within a domain
� E = Entity - consists of the real world (named) entities which are instances of the
entity classes “e”
INTRODUCTION::KOS::DERA::DEMO::CONCLUSION
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Attractiveness of Photos• Community-based models for classifying/ranking images
according to their appeal. [WWW09]
Inputs
FlickrPhoto
Content(visual features)
Metadata(textual features)
Community Feedback(photo’s interestingness) Classification &
Regression Attractiveness Models
Generator
InputsPhotoStream
cat, fence, house
#views#comments#favorites...
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Modelling image content as bags-of-visual-terms learnt through hierarchical K-means clustering
Photo Annotation
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Automatically annotating and classifying images using a semantic space approach.
Photo Annotation
Overall Result:* Competitive performance* Low computational complexity compared to other entries
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Languages of India
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~ Courtesy: Swaran Lata (DIT) , Country Manager , W3C India
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~ Courtesy: Swaran Lata , Country Manager , W3C India
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Character Encoding : UNICODE
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Drop Letters in Indian languages
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Underlining of characters
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Major Identified Problems in Styling :
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Approach to be taken for Possible Solution
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Issues for enabling Mobile Web in Indian
languages
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Some of Future Initiatives:
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