NISO Virtual Conference: The Semantic Web Coming of Age

http://www.niso.org/news/events/2014/virtual/semantic/

NISO Virtual Conference: The Semantic
Web Coming of Age: Technologies and
Implementations
February 19, 2014
Speakers:
Ramanathan V. Guha, Ralph Swick,
Kevin Ford, Henry Story,
Pierre-Paul Lemyre, Bob DuCharme

NISO Virtual Conference:
The Semantic Web Coming of Age:
Technologies and Implementations
Agenda
11:00 a.m. – 11:10 a.m. – Introduction
Todd Carpenter, Executive Director, NISO

11:10 a.m. - 12:00 p.m. Keynote Address Ramanathan V. Guha, Google Fellow; Founder of Schema.org
12:00 p.m. - 12:45 p.m.: The W3C Semantic Web Initiative Ralph Swick, Domain Lead of the Information and Knowledge Domain, W3C
12:45 p.m. - 1:30 p.m. Lunch Break
1:30 p.m. - 2:15 p.m. Semantic Web Applications in Libraries: The Road to BIBFRAME Kevin Ford, Network Development & MARC Standards
Office, Library of Congress

2:15 p.m. - 3:00 p.m.: The Social Data Graph: The Friend of a Friend (FOAF) Project Henry Story, Chief Technical Officer & Co-founder at
Stample
3:00 p.m. - 3:15 p.m. Afternoon Break
3:15 p.m. - 3:45 p.m. Sharing Information on the Semantic Web: The Need for a Global License Repository Pierre-Paul Lemyre, Director of
Business Development, Lexum
3:45 p.m. - 4:15 p.m. Semantic Web Applications in Publishing  Bob Du Charme, Director of Digital Media Solutions, TopQuadrant
4:15 p.m. - 5:00 p.m. Conference Roundtable: Services that Build on Others Semantic Web Data: Semantic Search Beyond RDF Moderated
by: Todd Carpenter, Executive Director, NISO

Towards a web of Data
R.V.Guha
Google

Outline of talk
• How did we end up here … a personal perspective
– The tortuous path through standards and products
• Schema.org
– Why schema.org, principles, how does it work
– Status : schemas, adoption, partners, applications
• Reports from Google, Bing, Yahoo! & Yandex
• Looking forward: Schemas in the pipeline, Research problems

One day, 16 years ago, …
• Ralph Swick, Ora Lasilla, Tim Bray, Eric Miller and myself
• Trying to make sense of a flurry of activity
– XML, MCF, CDF, Sitemaps, …

• There were a number of problems
– PICS, Meta data, sitemaps, …
• But one unifying idea

Context: The Web for humans

Structured
Data
Web server

HTML

Goal: Web for Machines & Humans
Structured
Data
Web server

Apps

What does that mean?
Ryan, Oklahama

Actor

type
birthplace
Chuck Norris
birthdate
March 10th 1940

How do we get there?
• How does the author give us the graph
– Data Model
– Syntax
– Vocabulary
– Identifiers for objects

• Why should the author give us the graph?

Going depth first
• Many heated battles
– Lot of proposals, standards, companies, …

• Data model
– Trees vs DLGs vs Vertical specific vs who needs one?

• Syntax
– XML vs RDF vs json vs …

• Model theory anyone
– We need one vs who cares vs what’s that?

Timeline of ‘standards’
•
•
•
•
•
•

‘96: Meta Content Framework (MCF) (Apple)
’97: MCF using XML (Netscape)  RDF, CDF
’99 -- : RDF, RDFS
’01 -- : DAML, OWL, OWL EL, OWL QL, OWL RL
’03: Microformats
And many many many more … SPARQL, Turtle, N3, GRDDL,
R2RML, FOAF, SIOC, SKOS, …

• Lots of bells & whistles: model theory, inferencing, type
systems, …

But something was missing …
• Fewer than 10k sites were using these standards
• Something was clearly missing and it wasn’t more language
features

• We had forgotten the ‘Why’ part of the problem
• The RSS story

’07 - :Rise of the consumers
• Yahoo! Search Monkey, Google Rich Snippets, Facebook Open
Graph
• Offer webmasters a simple value proposition

• Search engines to webmasters:
– You give us data … we make your results nicer
• Usage begins to take off
– 1000x increase in markup’ed up pages in 3 years

Yahoo Search Monkey
• Give websites control over snippet presentation
• Moderate adoption
– Targeted at high end developers
– Too many choices

Yandex Enhanced Snippets & Services
• Snippets for Web Search
• Data for Vertical Search
Services
• Various syntax and
vocabularies

• Yandex specific vocabs
for unexamined
use cases

Google Rich Snippets: Recipe View

Google Rich Snippets
•
•
•
•
•
•

Multi-syntax
Adhoc vocabulary for each vertical
Very clear carrot
Lots of experimentation on UI
Moderately successful
Scaling issues with vocabulary

Situation in 2010
• Too many choices/decisions for webmasters
– Divergence in vocabularies
• Too much fragmentation
• N versions of person, address, …

• A lot of bad/wrong markup
– ~25% for microformats, ~40% with RDFA
– Some spam, mostly unintended mistakes
• Absolute adoption numbers still rather low
– Less than 100k sites

In the meantime …
• The Web has grown
– >25 million independent active sites
– Trillions of web pages
– ~5 billion pages change every day
– Spam, malware, …

• In other words: scaling problems along every
dimension

Schema.org
• Work started in August 2010
– Google, Yahoo!, Microsoft & then Yandex
• Goals:
– One vocabulary understood by all the search engines
– Make it very easy for the webmaster
• It is A vocabulary. Not The vocabulary.
– Webmasters can use it together other vocabs
– We might not understand the other vocabs. Others might

Schema.org: report card
• Over 15% of all sites/pages now have schema.org
• Over 5 million sites, over 25 billion entity references
• In other words
– Same order of magnitude as the web
– Not just ‘promising new stuff’ anymore
% urls

% urls

Schema.org: Major sites
•
•
•
•
•

•
•
•
•
•

News: Nytimes, guardian.com, bbc.co.uk,
Movies: imdb, rottentomatoes, movies.com
Jobs / careers: careerjet.com, monster.com, indeed.com
People: linkedin.com,
Products:
ebay.com, alibaba.com, sears.com, cafepress.com, sulit.com, f
otolia.com
Videos: youtube, dailymotion, frequency.com, vinebox.com
Medical: cvs.com, drugs.com
Local: yelp.com, allmenus.com, urbanspoon.com
Events: wherevent.com, meetup.com, zillow.com, eventful
Music: last.fm, myspace.com, soundcloud.com

Schema.org: categories
• Most used categories by occurrence
– Person, Offer, Product, PostalAddress, VideoObject, Image
Object, BlogPosting, WebPage, Article, AggregateRating, Lo
calBusiness, Place, Organization, MusicRecording, JobPosti
ng, Recipe, Book, Movie, Blog, Photograph, ImageGallery

• Most used categories by domains
– ImageObject, WebPage, PostalAddress, BlogPosting, Produ
ct, Person, Offer, Article, LocalBusiness, Organization, Blog,
AggregateRating, Review, VideoObject, Place, Event, Rating
, AudioObject, MusicRecording, Store

Schema.org: properties
• Top properties by occurrence
– name, url, image, description, offers, author, price, thumb
nailUrl, datePublished, addressLocality, address, itemOffer
ed, duration, streetAddress, isFamilyFriendly, priceCurrenc
y, playerType, paid, regionsAllowed, postalCode, hiringOrg
anization, jobLocation,
• Top properties by domain
– Name, description, url, image, contentURL, address, author
, telephone, price, postalCode, offers, ratingValue, priceCur
rency, datePublished, addressRegion, availability, email, be
stRating, creator, review, location, startDate

Schema.org principles: Simplicity
• Simple things should be simple
– For webmasters, not necessarily for consumers of markup
– Webmasters shouldn’t have to deal with N namespaces
• Complex things should be possible
– Advanced webmasters should be able to mix and match
vocabularies
• Syntax
– Microdata, usability studies
– RDFa, json-ld, …

• Can’t expect webmasters to understand Knowledge
Representation, Semantic Web Query Languages, etc.
• It has to fit in with existing workflows

• Avoid KR system driven artifacts
– domainIncludes/rangeIncludes
– No classes like ‘Agent’
– Categories and attributes should be concrete

• Copy and edit as the default mode for authors
– It is not a linear spec, but a tree of examples
• Vocabularies
– Authors only need to have local view
– But schema.org tries to have a single global coherent
vocabulary

Schema.org principles: Incremental
• Started simple
– ~ 100 categories at launch
• Applies to every area
– Add complexity after adoption
– now ~1200 vocab items
– Go back and fill in the blanks

• Move fast, accept mistakes, iterate fast

Schema.org Principles: URIs
• ~1000s of terms like Actor, birthdate
– ~10s for most sites
– Common across sites
• ~10ks of terms like USA
– External enumerations

Ryan, Oklahama

Actor
type

birthplace
Chuck Norris

• ~10m-100m terms like Chuck Norris and
Ryan, Oklahama
– Cannot expect agreement on these USA
– Consumers can reconcile entity
references

citizenOf

birthdate
March 10th 1940

Schema.org Principles: Collaborations
• Most discussions on public W3C lists
• Work closely with interest communities
• Work with others to incorporate their vocabularies
– We give them attribution on schema.org
– Webmasters should not have to worry about where each
piece of the vocabulary came from
– Webmasters can mix and match vocabs

Schema.org Principles: Collaborations
•
•
•
•
•
•
•
•
•
•

IPTC /NYTimes / Getty with rNews
Martin Hepp with Good Relations
US Veterans, Whitehouse, Indeed.com with Job Posting
Creative Commons with LRMI
NIH National Library of Medicine for Medical vocab.
Bibextend, Highwire Press for Bibliographic vocabulary
Benetech for Accessibility
BBC, European Broadcasting Union for TV & Radio schema
Stackexchange, SKOS group for message board
Lots and lots and lots of individuals

Schema.org Principles: Partners
• Partner with Authoring platforms
– Drupal, Wordpress, Blogger, YouTube

• Drupal 8
– Schema.org markup for many types
• News articles, comments, users, events, …

– More schema.org types can be created by site author
– Markup in HTML5 & RDFa Lite
– Coming out early 2014

Schema.org & W3C
• Web Schemas – vocabulary discussion
Part of W3C Semantic Web Interest Group


• W3C Community Group(s)
e.g. http://w3.org/community/schemabibex/


• Wiki, Mercurial, email lists, …
Informal collaboration rather than classic standardization
Ideas for extending schema.org welcomed
Also room this week Thu/Fri, see blog.schema.org






W3C Data Activity
• Subsumes & expands on Sem Web & eGov
• Success for Sem Web in several communities who
publish self-describing, re-usable structured data

• schema.org makes it easier for developers to use
common vocabulary
• W3C pleased with the success of schema.org and
continues to encourage data formats that support
mixing of vocabs

Schema.Org Usage @ MS
Today
• Used primarily to support Bing’s Rich Captions
• Also recommended for Windows 8 App Contracts
Tomorrow
• Extended usage in Rich Captions and Bing Search
• Development support via new Bing Platform Dev
Center
• Innovative experiences in other Microsoft
products

Bing Rich Captions

• Ensure your Schema.Org annotations represent
the primary content on the page -> ensures
correct caption data is shown
• Ensure your Schema.Org annotations are
appropriate and relevant to the content of the
page -> improves your chances of being shown
• Rich Captions do not support RDFa 1.1 or JSONLD -> stick with RDFa 1.0 or Microdata for now

Yahoo! Search
• Rich results
– Blanco et al. Enhanced Results for Web Search, SIGIR 2011
– Mika & Potter. Metadata Statistics for a Large Web Corpus, LDOW 2012

• Related entities
– Blanco et al.
Entity recommendations in Web Search
– Wed 15:15 Search track

Yahoo! Media
• Schema.org across the Yahoo! Network
– Article, Photo and Video markup
– Q&A and Sports under development

• Personalization
– Based on past reading behavior, Facebook profile, implicit feedback
– Concepts from a news taxonomy and entity-graph
– Nicolas Torzec. The Y! Knowledge Base: Making Knowledge Reusable at
Yahoo! SemTech 2013

Applications
• Applications drive adoption
• First generation of applications
– Rich presentation of search results

• Many new applications are coming up
– On search page and beyond

Newer Applications: Knowledge Graph

Non web search Applications
• Searching for Veteran friendly jobs

Non search applications: Google Now

Pinterest: Schema.org for Rich Pins

Non search Applications
• Open Table website  confirmation email 
Android Reminder

Future application
• Clinical trials
• 4000+ clinical trials at any time in the US alone
• Almost all the data ‘thrown away’
• All that gets published is a textual ‘abstract’
• Over half the trials are redundant
• Earlier trials have the data
• Assumptions, etc. cannot be re-examined
• Longitudinal studies extremely hard, but super

Vocabularies in the pipeline
•
•
•
•

Actions (Potential Actions), Events
Accessibility
Commerce: Orders, Reservations, …
Communication: Fleshing out
TV, Radio, Email, Q&A, …
• Media: Scholarly works, Comics, Serials
• Sports
• and many many more …

Big initiatives underway
• Representing time
– Lot of triples with associated time interval
– Hard to force into the triple model
– Looking at named graphs

• Tabular / CSV data
– Census data, Scientific data, etc.
– Need mechanisms for external specification of the
meaning of these tables

Research Problems
• Propositional Attitudes
– Fictional characters
– Possible actions

• Mapping Entities across sites
– Billions of entities
– But hundreds of billions of entity references
– Very large scale cross site entity mapping

Schema.org: concluding
• Provide webmasters
– A good reason for adding structured data markup
– Allow more advanced authors to add much richer markup
from many different vocabularies
– An easy way to do it
• Many different applications emerging
• Many interesting research problems left
• Light at the end of the tunnel

MCF
• Introduced the Directed Labeled Graph (DLG)
model
• Used to describe site structure
• Provided simple visualization plugin
• ~3m downloads, few thousand sites
(not too bad for ‘96)
• Documentation was about a page
• Lead to MCF using XML
(4 pages including diagrams)

97-99: RDF / RDFS
• MCF (4 pages) got renamed to RDF
• Data model becomes a religion
• Lot of new things …
reification, sequences, chains, model
theory, …
• Final version done only by 2002
• RDF Primer is 100 pages long!
• But … used by very few websites

‘99: RSS 0.91
• Netscape needs customizable home page ala
my.yahoo
• No $s to do deals
• Create format and open it up to everyone
– 2 year target was 5k feeds
– 14 years later  100m+ feeds

2002: OWL
• Tim BL, et. al. ‘Semantic Web’ paper
• Answer to lack of adoption: we need more
features
• Lots of Darpa and EU funding
• Very powerful solutions … looking for problems
• ~10 years later … < 100 sites use it

‘04-07: Microformats
• Reaction to the complexity of RDF & OWL
• Retrofitted vCard, etc. into HTML  hCard
• Adoption by Google & Yahoo! made it wildly
successful
• Evolutionary dead-end: no new vocabulary in
over 5 yrs.

Lessons
• The RSS story
• Make sure you have your carrot!
– Carrots work much better than sticks!

• Find the right initial level of generality
• Start simple and iterate fast
• Optimize for flexibility

Information on
the Semantic
Web:
The Need for a Global License
Repository

Distributed Production of
Information
• Originates in research (not
commercial venture)
• Access is its driving force (not
property)
• Has defined the Internet as we know
it
– Open standards & Open Source Software
(OSS)

– Web 2.0
– And now the semantic web

Distributed Production of
Information

Why does it work?

OSS
• Motives to produce OSS
– Ethical / policy reasons
– Non-monetary incentives
– Increase the speed of market adoption
– Co-create and appropriate value
• By building on previous works
• By integrating external contributions

OSS
• Each contributor is adding to the pool
of knowledge available to all
• This knowledge is more valuable than
what any contributor can achieve
individually

• Not new phenomenon
(science, music, education, ...)

OSS - Legal Framework
• Collaborative software development
existed before
– Under informal agreements
– Under bilateral contractual schemes

• OSS licenses created a favourable
legal environment
– By favouring reciprocity (BSD)
– By securing openness (GPL)

OSS
• What the success of OSS makes us
see clearly

– In a networked
world, centralized corporate
production can sometime be
surpassed by distributed
production
– Mass adoption requires a
proper legal framework

OSS - Legal Framework

Is this conclusion applicable only to
software?

Web 2.0
• Extensive use of Web services
facilitating mass collaboration
– Rich Internet applications
– Web forums
– Blogs

– Wikis
– Folksonomies (Social tagging)

Web 2.0
• Web-as-participation-platform
– Architecture of participation
– Users become producers
– Collective intelligence

Web 2.0 Legal Framework
• Adaptation of OSS licenses
– GNU Free Documentation License
– OpenContent License
– Creative Commons (CC)

• Extended the favourable environment
to all types of freely accessible
information
– By specifying the applicable reuse
conditions (no permission required)

– By clarifying the spectrum of potential
rights
– By standardizing the licensing process
and making automated retrieval possible
(CC)


The question of online right
management should be solved by
now?

Semantic web
• Collaborative initiatives developed
independently from each other
– Vertical information flow (Information
silos)

• Accessibility & reusability of
information call for reciprocity
between them and other sources of
information
– Horizontal information flow (seamless
interoperability)

Semantic Web
• Using technology for data sharing
and reuse
– Data modeling (XML, RDF)
– Syndication technologies (RSS)
– Ontologies (OWL)

– Heuristic (text-recognition)

Semantic Web
• Web understandable by computers
– Data get a meaning
– Dynamic discovery, composition and
execution
– Layers of services

Semantic Web
• Current state of implementation
– Information sources are pre-qualified
(limited dynamic discovery)
• Public domain sources
• Use of APIs under bilateral agreements

– Reproduction of information is often
limited (links are the norm, mashup still
the exception)
– Management of personal information is a
nightmare

Semantic web Legal Framework

Here again successful mass adoption is
not just about technology

The Legal Issue:
Fragmentation of Rights
• Rights are fragmented
– By reuse conditions
– By jurisdictions
– By formats / domains

• Scalable to the smallest element
(website > webpage > data)

The Legal Issue:
• Not a new problem
– Requests to limit the proliferation of OSS
licenses (FSF)
– Initiatives to standardize licensing (CC)

• Not fundamental as long as humans
are in charge of the reuse of
information

The Legal Issue:
• Seamless interoperability through
semantic technologies require
computers to automatically
– Retrieve applicable licenses
– Resolve their respective terms

– Select information with adequate
conditions for the anticipated reuse

The Legal Issue:
• Standardization under CC is helping
– Embedded license information ease their
retrieval
– Computer readable version ease their
resolution

– Standardized terminology and low
number ease the selection

The Legal Issue:
• But it is a partial solution
– Most content is not licensed under CC
(and often cannot)
– Copyright holders have the right to
attach alternative conditions to their
content
– CC is not generally accepting alternative
licenses

• Example of difficulties
– Website Terms of Use vs Google “Usage
rights” feature

The Legal Issue:

A higher level resolution mechanism is
required

The Solution:
A Global License Repository?
• A database of varying copyright
licenses and their conditions
• A standardized approach to licenses
resolution and selection (expand the
CC model?)

• A Web service that can be queried by
users and computers

The Solution:
• Issues that need to be addressed
– Unlimited number of reuse conditions
– Format and domain specific restrictions
– Internationalization
– Versioning of licenses
– Compatibility between licenses
(relicensing)

The Solution:
• Possible solutions
– Organizing conditions and restrictions
into groups or categories?
– Managing licenses at the lowest possible
level and associating related ones?

– Limiting the designation of compatibility
to the most common licenses?

The Solution:
• A successful implementation requires
– Promotion and large-scale adoption of a
standard tagging model for information
– Involvement of copyright holders in
feeding and updating the database

– Transparency and quality control
procedures generating trust in the
system

The Solution:
• A successful implementation requires
– Scalability insuring efficient interactions
at every level of development
– Provision of outputs under standardized
formats

– Provision of simple APIs facilitating
interactions with the repository

The Solution:
• Architecture
– Use of open standards and OSS to
display transparency
– Use of collaborative technologies to
distribute updates

– Use of aggregative technologies to
promote exploitation and reuse

Conclusion
• Facilitating the sharing of information
is the core function of the Internet
– Semantic web is a no-brainer in principle

• But mass adoption requires a legal
framework
– Providing a lawful mean to co-create
and appropriate value
– Securing the existing rights of copyright
holders

Contact

Let me know what you think!
Pierre-Paul Lemyre
<lemyrep@lexum.com>

Outline



http://snee.com/rdf/niso2014/



RDF



Taxonomies, semantics, and content



Metadata management



Content creation


101

RDF
●
●
●
●
●

Resource Description Format
W3C Standard
Syntaxes exist, but ultimately a data model
Very easy to aggregate
Tools exist to treat relational data and
spreadsheets as RDF


Slide 102

An RDF “statement”: the triple

● (Subject, predicate, object)
● “This resource, for this property, has this
value.”
● “John Smith has a hire date of 2012-10-11.”
● “Chair 523 located in in room 47.”
● “index.html has the title ‘My Home Page'.”


Slide 103

Using URIs
A triple?
Subject:

index.html

Predicate: title
Object:

“My Home Page”


Slide 104

Using URIs*
A triple?
Subject:

index.html

Predicate: title
Object:
“My Home Page”
A triple!
Subject: <http://www.myco.com/members/index.html>
Predicate: <http://purl.org/dc/elements/1.1/title>
Object: “My Home Page”
*Uniform Resource Identifier, not Uniform Resource Locator—an
identifier, not an address.

Slide 105

URIs as triple objects
<urn:isbn:9780062515872>
<http://purl.org/dc/elements/1.1/creator>
“Tim Berners-Lee” .
or…
<urn:isbn:9780062515872>
<http://topbraid.org/ids/TimBernersLee> .
or…
<urn:isbn:9780062515872>
<http://www.w3.org/People/Berners-Lee/card#i> .

Slide 106

<urn:isbn:9780062515872>
<http://www.w3.org/People/Berners-Lee/card#i>
<http://www.w3.org/2000/01/rdf-schema#label>

107

Slide 107

<urn:isbn:9780062515872>
<http://www.w3.org/People/Berners-Lee/card#i>
<http://www.w3.org/2000/01/rdf-schema#label>
< http://www.w3.org/People/Berners-Lee/card#i>
<http://xmlns.com/foaf/0.1/mbox>
“timbl@w3.org” .
< http://www.w3.org/People/Berners-Lee/card#i>
<http://dbpedia.org/property/almaMater>
<http://dbpedia.org/resource/The_Queen's_College,_Oxford> .

<http://dbpedia.org/resource/The_Queen's_College,_Oxford>
<http://dbpedia.org/property/established>
1341 .
108

Slide 108

A “graph”
“Tim Berners-Lee”

http://www.w3.org/2000/01/rdf-schema#label

urn:isbn:9780062515872
http://purl.org/dc/elements/1.1/creator
http://www.w3.org/People/BernersLee/card#i

http://dbpedia.org/property/almaMater

http://dbpedia.org/resource/The_Queen's_College,_Oxford

http://dbpedia.org/property/established

1341

http://xmlns.com/foaf/0.1/m
box
“timbl@w3.org”

Slide 109

SPARQL: look for patterns
within the graph
“Tim Berners-Lee”

http://www.w3.org/2000/01/rdf-schema#label

urn:isbn:9780062515872
http://purl.org/dc/elements/1.1/creator
http://www.w3.org/People/BernersLee/card#i

http://dbpedia.org/property/almaMater

http://dbpedia.org/resource/The_Queen's_College,_Oxford

http://dbpedia.org/property/established

1341

http://xmlns.com/foaf/0.1/m
box
“timbl@w3.org”

Slide 110

Defining structure
● Optional
●

Nice option in “schema vs. schemaless” debate

● RDFS (RDF Schema Language)
●

Enumerate classes, properties, and relationships between them
# Prefixes stand in for base URIs, like with XML
foaf:Person rdf:type owl:Class .
viaf:19831453 rdf:type foaf:Person .

● OWL (Web Ontology Language)
●

●

–

Further describe classes and properties, enable fancier inferencing
For example: Jack has a spouse value of Jill; "spouse" is an
owl:SymmetricProperty; software can then infer that Jill has a spouse
value of Jack. (Semantics!)

Both are W3C standards, both expressed with
triples (and therefore easy to aggregate)


Slide 111

Simple Knowledge Organization System

 Controlled vocabulary
 Taxonomy
 Thesaurus
 Ontology


112

Taxonomies

Mammal

Horse

Cat

Dog
metadata!

Bulldog

Collie

Above: subset-of relationship.
Alternatives: part-of, instance-of.


113

Thesaurus
Mammal

Horse

Cat

Dog

(use for: mutt, cur)
Related
term

Bulldog

Collie

Building

Residential

Doghouse


Commercial

House

114


 Taxonomy
 Thesaurus
 Ontology


115


 Taxonomy
 Thesaurus
 Ontology


SKOS: the W3C’s OWL
ontology for
creating
thesauri, taxonomies,
and controlled vocabularies.

116

SKOS: standardized properties
http://myCompany.com/animals/c43209101
preferred label (English): "dog"

preferred label (Spanish): "perro"
preferred label (French): "chien"
alternative label (English): "mutt"
alternative label (Spanish): "chucho"
history note: "Edited by Jack on 5/4/11"
related term: http://myCompany.com/shelters/c3048293


117

SKOS: custom properties
http://myCompany.com/animals/c43209101
preferred label (English): "dog"

preferred label (Spanish): "perro"
preferred label (French): "chien"
alternative label (English): "mutt"
alternative label (Spanish): "chucho"
history note: "Edited by Jack on 5/4/11"
related term: http://myCompany.com/shelters/c3048293
product: http://myCompany.com/vaccinations/c2197503
foo code: “5L-MN1-003”


118

Who is using SKOS?


AGROVOC



New York Times: People,
Organizations, Locations, Subject
Descriptors



Library of Congress subject headers



AGFA drug admin. forms



NASA: many categories


119

TopQuadrant’s TopBraid EVN


120

Metadata management
 Content metadata is more than just keywords
 3 Vs of Big Data: Volume, Velocity and Variety
 Gartner October 2013 poll on which is most

difficult:


Variety: 16%



Volume: 35%



Variety: 49%


121

Metadata in images from 3 suppliers
Supplier 1

Supplier 2

Supplier 3

file name

file name

file name

file size

file size

file size

last modified

last modified

last modified

shutter speed

shutter speed
GPS latitude

GPS latitude

GPS longitude

GPS longitude

creator name
X resolution

X resolution

Horz. resolution

Y resolution

Y resolution

Vertical resolution

copyright notice
keywords
aperture setting

re-use rights
subject
F stop

(etc.)

122

Accommodating overlapping metadata
 Store it all; each is just a triple
 Identify relationships for easier searching


“aperture setting” = “F stop”



“copyright notice” a subproperty of “re-use rights”



“Y resolution” a subproperty of “vertical
resolution”

 Image data a simple case. Consider audio,

video, management of digital rights…

123

Metadata and…

Prosopography: the study of careers,
especially of individuals linked by family,
economic, social, or political relationships.


Slide 124

Content creation
From an article on Massive Open Online Courses in the
February 8, 2014 Economist:
"...says Tylor Cowen, a cofounder of Marginal Revolution
University, it is possible that
textbook publishers are better
equipped than universities to
develop MOOCs profitably."


125

Available Data: The Linked Open Data Cloud


Slide 126

Wikipedia page on Andrew Johnson


Slide 127

DBpedia page for Andrew Johnson

(further down the page…)


Slide 128

Thank you!

bducharme@topquadrant.com
http://snee.com/rdf/niso2014/


129

NISO Virtual Conference
The Semantic Web Coming of Age: Technologies
and Implementations

Questions?
All questions will be posted with presenter answers on
the NISO website following the webinar:
http://www.niso.org/news/events/2014/virtual/semantic/

NISO Virtual Conference • February 19, 2014

THANK YOU
Thank you for joining us today.
Please take a moment to fill out the brief online survey.
We look forward to hearing from you!

NISO Virtual Conference: The Semantic Web Coming of Age

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