Hlava, Davis, Corson-Rikert, and Parr "Control Your Vocabulary: Real-World Applications of Semantic Technology"

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© 2010. Access Innovations, Inc. All Rights Reserved.
The Semantic
Landscape
Marjorie M.K. Hlava
President
mhlava@accessinn.com
www.accessinn.com
NISO Webinar - June 9, 2010
1:00 - 2:30 Eastern time
A Semantic Landscape
Covering the
Semantic Landscape
! What is semantic?
! Semantic approaches
! Semantic process
! Semantic enrichment
! Beyond keyword search
! Social and Web 2.0 options
What is “Semantic” ?
! Process of adding meaning to objects
! We have lots of words
• What do they mean?
! LEAD
• Same spelling
• Different meanings in context
• Something to guide a horse
• An opening in the ice
• An element
• A management technique
• And more

Semantics infers relationships
! Between the words used and the textual
concepts represented
! Between different information objects
! Between different collections
! Supports data mining through the content
strata
! To extract the meaning at many levels
Semantics extraction
Semantic approaches
! Parallel paths
! Theoretical approaches vary widely
! Trouble defining our terms
! Controlled vocabularies
! Many standards bodies
! Funding agencies and approaches *
! Open source versus Off the shelf*
• * issues not covered in this talk
Parallel paths developed
! Computer Science
! Library Science
! Publishing
• Primary and secondary
• Abstracting and Indexing
! All developed structured data
! All added “Semantics” in different ways
! Dublin Core surprised the A&I’s
Theoretical approaches vary
! Independent silos developed
! Libraries - cataloging
! Secondary Publishers
• Abstracting and Indexing Services
! Computer Science
! Computational Linguistics
! Artificial Intelligence
! Translations – Multilingual meaning
! Other academic disciplines
Controlled Vocabularies
! To guide, not restrict
! Disambiguate
• what do you mean?
! Use synonyms for multiple paths to data
! Guide using related and narrower terms
! If a word is not in the CV?
• Type in the word! Use full text
• Add it to the CV
Defining our terms
! Keywords
! (HTML, Google)
! Uncontrolled
vocabulary
! Entities
! Authority terms
! Ontology
! Controlled vocabulary*
! Descriptors
! Thesaurus*
! Taxonomy
! Semantic enrichment
! Metadata
! Dublin Core*
*NISO Standards

Adding Descriptors
! Adding descriptors
• Historically too expensive
• Now we can do it automatically or assisted
! Recent recognition
• Library cataloging adds rich metadata
• Contributes to findability
• Full text is not very accurate
• Needs semantic layers
! Learning from the past practices
Many standards bodies
! ISO, ANSI, NISO (TAG 46) Z39.19
! ISO, BSI
! ISO, Tag 37, and CEBEMA X13
! W3C – World Wide Web Consortium
! Government consortia
! Other groups – MARBI, IFLA, DAMA, etc.
! Terminology Standards
• All developing standards
• Small amounts of cross walks between them
Some Controlled Vocabulary
Standards
! NISO Z39.19 – Controlled vocabularies
• Well formed following the standard parses as a
OWL Full
! ISO - Thesaurus data model and schema in
ISO/DIS 25964
! BSI - BS 8723- Structured vocabularies for
information retrieval
! W3C - OWL - Web Ontology Language
! W3C - SKOS
Structure of
Controlled Vocabularies
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Ways to state
semantic relationships
! XML Elements and their attributes
! Dublin Core and its extensions
! Fields or tables in relational files
! MARC fields in cataloging
! RDF Triples from W3C
! All connect information semantically
! Most do not interact with the others well
A Taxonomy is a
Knowledge Organization System
! Uncontrolled list
! Name authority file
! Synonym set/ring
! Controlled vocabulary
! Taxonomy
! Thesaurus
! Ontology
! Semantic network
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A Taxonomy is a
Semantic Enrichment System
! Uncontrolled list
! Name authority file
! Synonym set/ring
! Controlled vocabulary
! Taxonomy
! Thesaurus
! Ontology
! Semantic network
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Taxonomy - thesaurus -ontology
! Main Term (MT)
! Top Term (TT)
! Broader Terms (BT)
! Narrower Terms (NT)
! Narrower Term Instance
! Related Terms (RT)
" See also (SA)
! Synonym (NP)
" Used for (UF), See (S)
! Scope Note (SN)
! History (H)
TAXONOMY
THESAURUS
ONTOLOGY
Semantic process
! Digitize your data
! Convert to textual strings
! Add metadata
! Add descriptors
! Deposit in repository
! Add search software
! Create a user interface
! Semantically enriched experience!
Semantic Process
Full text, HTML,
PDF, data feeds
Apply terms
Rules Base
User Interface
Web Portal
Client
Taxonomy
Data
Repository
Inline Tagging Search
Software
Metadata
Extractor
Thesaurus
Master
Automatic
Summarization
Semantic process adds value
! Organizes “unstructured” content
! Uncovers relationships
• between materials originating from different media
! Improves website navigation
• Addressing varied needs of visitors
! Suggests terms based
• Use popularity or user profile
! Focuses the search process
• Presents user with related terms
• Narrows broad topics
• Extract the meaning at many levels
Semantic Enrichment
! Start with a well-constructed vocabulary
• Leverage the power of the Knowledge Domain
• Associate terms with your unique brand and products
! Tag the content, people, and activities
• Subjects, names, places
• Automated or human-aided workflows
! Add end-user interfaces
• Enhanced Search: browse by subject; faceted display
• Discovery: Alerts, Visualizations, Related Content

Semantic Enrichment Architecture
DHAPI
Web
Content
Files,
Documents
Databases
Taxonomies /
ontology
WEBServerI
Novelty
Detection
M.A.I.
Rule Bases
M.A.I. Concept
Extractor
Auto
Summarization
Entity
Extractor
DHCONCEPT
EXTRACTIONSYSTEM
Email,
Groupware, etc.
Data Harmony Administrative Module
Thesaurus Master
Dublin Core
METADATA
Rules for
Concept Extractor
SUBJECT
TERMS
ABSTRACT
Bibliographiccitation
withabstract
Library OPAC
Search Server
Web Portals
Database system
Search Software
Search Indexes
Auto-completion
Broader Term
Narrower Term
Related Term
Navigation Tree
Categorization
Inline tagging
Query expansion
using rule base
Fast indexing
Massive data sets
Incremental indexing
Fast query speeds
Search within results
Beyond Keyword Search
! After the single term Google box
! Structured data
• Has metadata
• Could have Semantic enrichment
• Taxonomy terms,
• Entities in authority form
! Structured systems have descriptor search
• Dialog, Ovid = fielded search
• Oracle, SQL, SAP, Access = table driven search
Kinds of search
! All Search is Boolean
! All use an inverted index
! Discovery – finding new trends and patterns
= 5% of search time
! Semantic Search – replicable, additive,
persistent, = 95% of search time
! 5% inspiration / 95 % perspiration
! Reinforce, support, update
Theoretical search divide
! Statistical, Bayesian, neural net, latent
semantic, vector = 50 % accurate so they
add rules and relevance
• Autonomy, Verity, Fast, Google,
! Semantic search rich in metadata, tags,
descriptors = 90 % accurate
• Endeca, Perfect Search, MarkLogic
• RDBMS Oracle, SAP, MS Access
• Dialog, Ovid, BRS, InfoSeek, Transium
! Federated Search – sends query to many
resources. One query – many sources
Measuring accuracy - guiding
! Recall / Precision
• Absolutes against a standard
! Relevance
• A confidence rating
! Semantics provide accurate results
! Speed and guide
• Hierarchies or Narrower terms to pinpoint
• Associated or Related terms to broaden
• Find stuff in other silos
! Combined silos without semantics = MESS
Semantic search options

Dissemination - then and now
! Follow through – get the word out
! Keep up
• RSS, Twitter, subscribe
! Who is linking to your work?
! Who’s referencing your work
! Who’s using your data?
! Who else is working in your space?
! Who’s quoting? What’s the impact factor?
! Who’s funding?
! Who’s implementing?
! Where are they?
Scientific social networking
based on metadata
! Idea has been here - Who is citing who like
! ISI does it with references
! API UniPHY does it using semantics
! Expand your options using
! good metadata and descriptors
Map who is working in the
field and where
See the authors connections
Not longer just “Nice to have”
! Semantic strategy is essential to a digital strategy
! Controlled vocabulary / taxonomy is central
! Strengthens brand
! Supports “unbundled” content
! The economy of the web is based around the
article, rather than the journal
! This trend is unlikely to change
There is a lot under that landscape
Semantic Hierarchy
browsable tree
Related Terms semantic web

Synonyms search foundation
Use your semantics
! Use in search
! Use in discovery
! Use in social networks
! Use in production
! Use in e-commerce
! Use to serve ads
! Use on the web site
! Leverage what you’ve built!
Thank You
Marjorie M.K. Hlava, President,
Access Innovations / Data Harmony
mhlava@accessinn.com
Access Innovations
4725 Indian School NE Suite 100
Albuquerque, NM 87110
www.accessinn.com
(505) 998-0800 office
(505) 256-1080 fax
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Presented by:
Valrie Davis
and
Jon Corson-Rikert
! Cornell University: Dean Krafft (Cornell PI), Manolo Bevia, Jim Blake, Nick Cappadona, Brian Caruso, Jon Corson-Rikert,
Elly Cramer, Medha Devare, Elizabeth Hines, Huda Khan, Brian Lowe, Joseph McEnerney, Holly Mistlebauer, Stella
Mitchell, Anup Sawant, Christopher Westling, Rebecca Younes. University of Florida: Mike Conlon (VIVO and UF PI),
Chris Barnes, Cecilia Botero, Kerry Britt, Erin Brooks, Amy Buhler, Ellie Bushhousen, Linda Butson, Chris Case, Christine
Cogar, Valrie Davis, Mary Edwards, Nita Ferree, George Hack, Chris Haines, Rae Jesano, Margeaux Johnson, Sara
Kreinest, Meghan Latorre, Yang Li, Paula Markes, Hannah Norton, Narayan Raum, Alexander Rockwell, Sara Russell
Gonzalez, Nancy Schaefer, Dale Scheppler, Nicholas Skaggs, Matthew Tedder, Michele R. Tennant, Alicia Turner, Stephen
Williams. Indiana University: Katy Borner (IU PI), Kavitha Chandrasekar, Bin Chen, Shanshan Chen, Jeni Coffey, Suresh
Deivasigamani, Ying Ding, Russell Duhon, Jon Dunn, Poornima Gopinath, R>C), Hardesty, Brian Keese, Namrata Lele,
Micah Linnemeier, Nianli Ma, Robert H. McDonald, Asik Pradhan Gongaju, Mark Price, Yuyin Sun, Chintan Tank, Alan
Walsh, Brian Wheeler, Feng Wu, Angela Zoss. Ponce School of Medicine: Richard J. Noel, Jr. (Ponce PI), Ricardo
Espada Colon, Damaris Torres Cruz, Michael Vega Negrón. The Scripps Research Institute: Gerald Joyce (Scripps PI),
Catherine Dunn, Brant Kelley, Paula King, Angela Murrell, Barbara Noble, Cary Thomas, Michaeleen Trimarchi.
Washington University School of Medicine in St. Louis: Rakesh Nagarajan (WUSTL PI), Kristi L. Holmes, Caerie
Houchins, George Joseph, Sunita B. Koul, Leslie D. McIntosh. Weill Cornell Medical College: Curtis Cole (Weill PI), Paul
Albert, Victor Brodsky, Mark Bronnimann, Adam Cheriff, Oscar Cruz, Dan Dickinson, Richard Hu, Chris Huang, Itay Klaz,
Kenneth Lee, Peter Michelini, Grace Migliorisi, John Ruffing, Jason Specland, Tru Tran, Vinay Varughese, Virgil Wong.
This project is funded by the National Institutes of Health, U24 RR029822, "VIVO: Enabling National Networking of
Scientists".
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